DESC:Technical field of the invention
[0003] The present invention relates to a system for implementation of targeted and ecological permanent transformations of the individuals. More specifically, the present invention relates to a system for enabling the users to undergo personalized, context-aware, and sustainable transformations across cognitive, emotional, behavioral, and skill domains by identifying capability gaps and delivering adaptive interventions based on multimodal inputs and expert knowledge. The present invention further discloses a method for implementation of targeted and ecological permanent transformations by acquiring multimodal user data, analyzing contextual and capability-related parameters, and delivering customized transformation strategies to enhance long-term personal outcomes of the users.
Background of the invention
[0004] In today’s fast-paced, hyper-connected, and increasingly demanding world, individuals are often forced into making difficult trade-offs between the most vital aspects of their lives such as career, family, health, and their desire to create meaningful impact. These compromises are often influenced by systemic constraints, limited awareness of alternatives, and scarcity of resources related to time, energy, or capability. The cumulative effect results in negative life outcomes, including chronic health issues, emotional exhaustion, weakened relationships, missed opportunities, and a lingering sense of unfulfillment.
[0005] Human beings universally aspire to move to the next level in their lives seeking progress in domains such as health, interpersonal relationships, career growth, skill development, and meaningful contribution. However, the transformation process remains lengthy, ambiguous, and marked by inefficiencies. Existing interventions, comprising coaching, therapy, self-help initiatives, and structured training programs remain narrowly scoped and unable to address the intertwined nature of real-world personal and professional challenges. These approaches are often time-intensive and fall short of delivering precision and individualized outcomes. No available mechanism facilitates a comprehensive journey through multiple aspects of life in a structured, intelligent, and scalable manner.
[0006] Further, personal transformation often depends on the insights and interventions of professionals who have developed domain-specific intuition through extensive exposure and practice. These professionals typically possess tens of thousands of hours of cumulative experience. Their expertise allows accurate recognition of systemic challenges and formulation of targeted capability development pathways. However, this dependence introduces a bottleneck. Access to such experts remains limited due to high costs, availability constraints, and geographic or logistical barriers. Additionally, the process of translating expert diagnosis into actionable developmental experiences often requires a second layer of facilitation by another group of skilled specialists, further limiting the scalability and affordability.
[0007] Within the diagnostic context, uncovering the underlying cause of personal challenges demands extensive probing, often requiring the review of vast data and selection from a repository exceeding hundreds of thousands of diagnostic questions. The biggest challenge in the medical industry is collection and analysis of data from participants and compare it with large data set to suggest one or more alternatives for each individual based on plurality of physiological parameters / markers / indicators) measured and analysed simultaneously.
[0008] Human experts rely on intuition to navigate these datasets; however, this approach is constrained by cognitive fatigue, memory limitations, and the bias inherent in selective questioning. After a certain number of questions, typically around 15, the quality of responses, the energy of the interaction begin to degrade and engagement reduces, resulting in diminished quality of outcomes.
[0009] While the human brain is highly capable, its real-time information processing bandwidth remains limited. Research indicates that conscious processing is restricted to approximately 10 bits of information per second, in stark contrast to the volume of sensory input continually received through vision, hearing, touch, taste, and smell. Even expert practitioners, though proficient in interpreting emotional and behavioral patterns across multiple modalities, remain limited in their capacity to sustain multi-channel analysis over time. Beginners or untrained users, focusing on a single channel at a time, often miss subtle indicators essential for deep insight or accurate understanding.
[0010] Further, the challenge of discerning precise capability gaps becomes magnified due to the intricate interdependence of life circumstances. A mechanism is therefore required that processes multiple forms of real-time sensory input, evaluates congruence and redundancy, and selects only the most pertinent subset of questions capable of eliciting transformative insights with reduced cognitive load.
[0011] Furthermore, in multi-topic engagements, overlapping signals are often generated across subdomains. Processing and correlating such data over time necessitates memory and pattern recognition capabilities developed through years of professional experience. Machines, however, possess the capacity to store and access vast multi-dimensional datasets without loss of fidelity. A machine-based framework is capable of simultaneously tracking over two hundred distinct responses and correlating them in real time, surpassing the limitations of short-term human memory, which is typically restricted to fewer than ten data points.
[0012] Developing professional expertise in fields requiring refined behavioral, emotional, or physiological interpretation generally demands prolonged mentorship and constant feedback. In the absence of such ongoing calibration, learners often fail to notice key information or formulate effective interventions. A mechanism capable of capturing missed data and providing real-time validation and expert-grade feedback is essential for sustaining learning progression and ensuring outcome consistency.
[0013] The transformation of skills is further constrained by the inability to identify and prioritize capabilities required for achieving superior life outcomes. Whether in domains such as leadership, emotional regulation, communication, or wellness, conventional tools lack the adaptability and real-time feedback necessary to deliver actionable interventions. Generic approaches lead to fragmented outcomes and protracted learning curves.
[0014] No available mechanism dynamically identifies specific capability gaps and delivers timely, personalized interventions aligned with the individual’s internal and contextual states. Existing approaches do not provide the precision or efficiency required for sustained progression and remain dependent on continuous human oversight.
[0015] In response to the challenges, several attempts have been made to address these challenges through various technological innovations. For instance, the patent application No. WO2018029533A2, titled "Life Performance Management System and Method Thereof", describes a system that receives input parameters related to user requirements and derives preferences based on behavioral analysis. It assigns weights, ranks results, and generates suggestions aligned with user-defined goals and preferences.
[0016] Patent application No. IN202541053216A, titled "Smart System and Method for Skill Gap Analysis and Targeted Training", introduces a framework that integrates biometric and behavioral data acquisition, ontology-driven skill inference, adaptive learning delivery, and federated learning to map physiological and cognitive responses to specific skill domains. The solution employs explainable artificial intelligence for diagnosis and natural language processing for micro-adaptive learning delivery.
[0017] Patent application No. IN202511055085A, titled "Multimodal AI Framework for Emotion Detection and Personalized Mental Wellness in IoT-Enabled Environments", discloses a system that utilizes facial expressions, vocal tone, and physiological data to detect emotions in real time. It includes mechanisms for predictive trend analysis, gamified engagement, context-sensitive mood adaptation, and chatbot-based interaction for mental wellness support.
[0018] Traditional approaches take a lot of time and most of the time are not customized and do not guarantee permanent results. And the approaches/methodologies that have the right tools and techniques to bring about those permanent results, require the involvement of a highly qualified human resource, which makes it impossible to scale up the impact. This brings in the need for an independent and scalable system that can bring about these targeted and permanent transformations or results.
[0019] Hence, despite the advancements in the field, there remains a need for a unified, targeted, and multimodal transformation system that leverages real-time inputs from individuals across cognitive, emotional, physiological, and behavioral dimensions to identify personal limitations and capability gaps, adaptively generate diagnostic insights, support personalized transformation across life domains without continuous expert facilitation, and provide context-aware progression paths aligned with the individual’s internal states, situational conditions, and long-term developmental trajectories.
Object of the invention
[0020] An aspect of the present invention relates to a system for intelligently and efficiently guiding individuals through personalized, evidence-based transformation involving capability enhancement, skill acquisition, and improvement across cognitive, emotional, physiological, and behavioral dimensions.
[0021] Another aspect of the present invention relates to a method for intelligently and efficiently guiding individuals through personalized, evidence-based transformation involving capability enhancement, skill acquisition, and improvement across cognitive, emotional, physiological, and behavioral dimensions.
Summary of the invention
[0022] The present invention addresses the limitations of the prior art by disclosing a comprehensive, intelligent, and modular system that facilitates targeted and ecological permanent transformations of the users. The system achieves the permanent transformations by identifying personal and skill-based capability gaps and enabling immediate, personalized development through a combination of multimodal data acquisition, intelligent inference, and adaptive intervention delivery.
[0023] It is disclosed herein a System that is capable of collecting high-quality micro inputs (details/data/information, thousands of data points) through omni channel. The inputs are generally visual, auditory, kinesthetic, gustatory, olfactory, behavioural, emotional, intellectual, and responsive/reactive.
[0024] When a participant interacts with the system, he is connected to multiple devices or sensors that continuously monitor and fetch high quality inputs to capture data which are humanly impossible to capture.
[0025] These instruments/apparatus/sensors/devices capture high quality inputs like micro muscle movements, micro skeletal movements, iris movement respiratory rate, change in skin colour, tone of muscles and other parameters in an individual. These devices are able to capture the micro level of data points which are humanly not possible to capture.
[0026] With the collected input/data, the system creates an anatomical, psychological, functional, pathological, emotional and physiological mapping of the current scenario by going through millions of data points. A set of recommendations from the list of installations may be selected based on the analysis of the data collected. The recommendation may be implemented on the participant to achieve the purpose. After the first implementation, the outcomes observed by the sensors are continuously sent for validation by the system. If validation is successful, the installation is complete. If the intended outcome is not achieved, the procedure is repeated for further iterations from the start till the validation is successful.
[0027] The system has inbuilt artificial intelligence machines, that continuously collects data and builds upon it based on the feedback received. The AI machine will run a data analysis to identify which combination out of a million permutations and combinations, where it stands. Based on the input gathered from the participant, the current scenario is compared with millions of data point, then permutations and combinations are generated which is compared with sets of collections of data and gives an output (derivation) comprising a list of procedures. The larger the data set, the more accurate is the output. The system executes the list on the participant to have an installation effect (which produces a long-lasting transformation). The installation process is also interactive.
[0028] Once the machine does the installation it checks and evaluates with comparisons of previous mapping and present mapping. If the correction is not done it will do iterative outputs for correction.
[0029] In an exemplary use, the machine, based on the biofunctional responses captured from a participant, can calibrate and predict that for this particular iris movement, this particular biometrical changes, this particular BMI, this particular vibration and thought process, an appropriate treatment modality can be given.
[0030] During this process, the machine captures micro split-second responses from the subconscious of the participant. This enables the decision-making capability of the machine to decide the precise set of installations.
[0031] The frequency of solar voice is used as a measurement to identify the frequency that has the maximum impact on the user. This reference is used to determine the frequency with which the installations should be done. This would resonate with their unconscious mind and make it much more customised for them. This becomes a rapport-building mechanism for the system.
[0032] In an embodiment, the system comprises a User Interface (UI) module that serves as the primary interaction layer between the user and the system, built on a Service-Oriented Architecture (SOA) or microservices framework, ensuring scalability, modularity, and interoperability across multiple platforms and devices. The UI module supports multimodal input and output including but not limited to text, voice, and gesture-based interactions. The UI module is adaptive, accessible, and responsive across multiple devices such as smartphones, tablets, and desktops.
[0033] In an embodiment, the User Interface (UI) module presents questions, insights, feedback, and transformation plans to the user and captures the user responses in real time through voice, gesture and haptic inputs, collectively called verbal responses. The UI module sends the user inputs to a multimodal signal processing module and receives outputs from an inference and recommendation engine and a transformation implementation module for delivering personalized insights, diagnostic questions, and targeted interventions to the user, thereby enabling real-time interaction, adaptive transformation guidance, and seamless user experience throughout the transformation journey for the user.
[0034] The UI module facilitates the multimodal interactions with the users using text, voice, gesture, and haptic inputs, and delivers the transformation content including personalized insights, questions, and interventions using different technologies such as Web Real-Time Communication (WebRTC), WebSockets, React/Flutter, voice-to-text Application Programming Interfaces (APIs), and accessibility APIs. In a summary, at least in an embodiment, UI module is configured to facilitate one or more multimodal interactions with one or more users through text, voice, gesture, and haptic inputs, and to deliver transformation content including personalized insights, questions, and interventions.
[0035] The system further comprises a user profile and context management module that maintains a dynamic, evolving profile for each user. The user profile and context management module stores and updates user-specific data such as demographic information, historical interaction records, transformation goals, emotional state indicators, and contextual metadata such as time of the day, geographical location, and device-related parameters associated with the user.
[0036] Further, from the ongoing user activity and the received feedback, the user profile and context management module is continuously updated to ensure the user profile reflects the user’s current state and the intentions. The user profile and context management module further supports session continuity, enables personalization, and manages privacy and consent parameters associated with each user. In an embodiment, the user profile and context management module feeds into the inference and recommendation engine, an intelligent questioning and diagnostic engine, and a skill transformation engine to generate personalized insights and deliver user-specific interventions. In a summary, at least in an embodiment, user profile and context management module is configured to maintain a dynamic, evolving profile of the user, including demographic data, transformation goals, emotional trends, contextual metadata, and/or historical interaction data.
[0037] The system further comprises a sensor integration and data acquisition module that interfaces with the external and embedded sensors to collect physiological data of the user, environmental data, and supports the integration with wearable devices such as heart rate monitors, electroencephalogram (EEG) headbands, image capturing devices such as cameras for facial expression analysis, audio capturing devices such as microphones for the tone and the pitch analysis of the user, and the environmental sensors such as light and noise sensors which collectively facilitate the acquisition of non-verbal response data. In a summary, at least in an embodiment, sensor integration and data acquisition module is configured to collect one or more real-time physiological, anatomical, bio-chemical, pathological, psychological, emotional state, behavioral, and/or environmental data from one or more sensors, including EEG, heart rate, skin conductance, facial expressions, voice tone, and posture.
[0038] In an embodiment, the sensor integration and data acquisition module collects different data such as heart rate variability, skin conductance, facial micro-expressions, voice tone and pitch of the user and sends the raw data to a multimodal signal processing module that processes and interprets multimodal inputs to extract meaningful features using machine learning models to analyze speech, facial expressions, gestures, and physiological signals to infer emotional states, stress levels, engagement, physiological, anatomical, bio-chemical, pathological, psychological features and cognitive load of the user and further generate a user state vector utilizing Natural Language Processing (NLP), computer vision, signal processing, emotion recognition models, Fuzzy Logic, and Tokenization. The structured outputs signals from the multimodal signal processing module are transmitted to the intelligent questioning and diagnostic engine and the inference and the recommendation engine for further processing. In a summary, at least in an embodiment, the multimodal signal processing module is operatively coupled to the sensor integration module, configured to analyze the one or more collected data using one or more artificial intelligence models to extract emotional, cognitive, physiological, anatomical, bio-chemical, pathological, psychological features and/or emotional state and generate a user state vector.
[0039] The system further comprises the intelligent questioning and diagnostic engine that dynamically selects the most relevant questions for the user from a large and expandable knowledge base comprising diagnostic and exploratory prompts. The intelligent questioning and diagnostic engine applies reinforcement learning and contextual inference techniques to adapt its questioning strategy in real time based on the user's current state. In an embodiment, the intelligent questioning and diagnostic engine dynamically generates a limited, highly relevant, and personalized set of diagnostic questions in real time, based on multimodal signals, user profile data, and knowledge base prompts. The intelligent questioning and diagnostic engine receives the user state from the multimodal signal processing module and the user profile and context management module, transmits the selected questions to the user interface module, and receives the corresponding responses from the user for further analysis. In a summary, at least in an embodiment, intelligent questioning and diagnostic engine is configured to dynamically generate a limited, highly relevant, and personalized set of diagnostic questions in real time, based on multimodal signals, user profile data, and knowledge base prompts.
[0040] Further, the system comprises the expert knowledge base and a capability map that store structured knowledge including transformation protocols, skill ontologies, domain-specific expertise, and validated capability models. The expert knowledge base and capability map support semantic search and inference to facilitate capability mapping and transformation planning. In an embodiment, the expert knowledge base and capability map comprise structured ontologies of life domains such as health, career, and relationships, skill taxonomies, transformation protocols, and capability gap models to support semantic reasoning and capability mapping and are queried by the intelligent questioning and diagnostic engine, an inference and recommendation engine, and a skill transformation engine for generating accurate and context-specific transformation recommendations. In a summary, at least in an embodiment, the expert knowledge base and a capability map comprises structured ontologies of life domains, skill taxonomies, transformation protocols, and/or capability gap models, and configured to support semantic reasoning and capability mapping.
[0041] The system further comprises the inference and recommendation engine that synthesizes the inputs from all other modules to generate personalized insights, identify root causes otherwise called as systemic influences or systemic constraints of the user challenges, and recommend transformation strategies for the users. The inference and recommendation engine uses probabilistic reasoning, pattern recognition, and contextual modeling to analyze the user conditions and determine optimal transformation pathways. The term "root cause" is used in a broad and inclusive manner, wherever the term appears, it may also refer to underlying systemic influences or systemic constraints, depending on the context in which it is used. This interpretation is intended to encompass both direct and indirect contributing factors that may affect the behavior, performance, or outcome of the subject matter described herein. In an embodiment, the inference and recommendation engine synthesizes multimodal signals, user profile data, and knowledge base content to identify root causes of the user challenges, detect capability gaps, simulate future trajectories, and generate personalized transformation recommendations; and further transmits the output to the user interface module and the skill transformation implementation module, and updates the user profile and context management module with new insights derived from the recommendation process. In a summary, at least in an embodiment, the inference and recommendation engine is configured to synthesize multimodal signals, user profile data, and knowledge base content to identify root causes or systemic influence or systemic constraints of user challenges, detect capability gaps, simulate future trajectories, and generate personalized transformation recommendations.
[0042] Further, the system comprises a transformation implementation module that delivers the personalized interventions prescribed by the inference and recommendation engine and simultaneously tracks the progress of the user. The transformation implementation module comprises micro-learning content, behavioral nudges, solar voice model, habit formation tools, metaphors, reorganizing unconscious priorities, Virtual Reality / Mixed Reality (VR/ MR) -based simulations, reconditioning, personalized mental activities and mental games based on each individual’s unique internal patterns, and real-time coaching simulations. The delivery of the content is dynamically adapted based on engagement parameters specific to the user, including multimodal feedback signals such as facial expressions, voice tone and pitch, posture, and gestures, as well as behavioural usage patterns such as interaction frequency, duration, and responsiveness. The Solar Voice Model comprises personalized auditory characteristics including frequency spectrum, tone, tempo, modulations, and content, which resonate with the unconscious cognitive patterns of the user, thereby enhancing personalization and establishing a rapport-building mechanism between the user and the system. The transformation implementation module receives the transformation plans from the inference and recommendation engine and transmits user engagement and progress data to the user profile and context management module and to a mentorship simulation and feedback module for iterative refinement and contextual adaptation. In a summary, at least in an embodiment, the transformation implementation module is configured to deliver one or more adaptive interventions using an emotional Voice, micro-learning content, behavioural nudges, habit formation tools, Metaphors, Reorganizing unconscious priorities, VR Based Simulations, Reconditioning, personalized mental activities, mental games, and/or real-time coaching simulations, and to monitor user engagement and internalization of interventions.
[0043] The system further comprises a parallel processing and memory management module that enables real-time tracking and correlation of multiple data streams received from various modules. The parallel processing and memory management module supports the monitoring of over two hundred concurrent physiological, behavioural, contextual, and interactional signals to facilitate deep temporal pattern recognition and ensure continuity of transformation across multiple sessions. The module is adapted to maintain session-specific and long-term memory by capturing historical data, behavioural trends, and engagement dynamics, thereby allowing the system to analyze recurring user behaviours, correlate current inputs with past states, and personalize transformation pathways over time. The parallel processing and memory management module supports all the functional modules of the system, particularly the inference and recommendation engine, the mentorship simulation and feedback module, and the intelligent questioning and diagnostic engine, by providing them with temporally aligned signal history, session continuity data, and correlated insights necessary for ensuring consistent transformation trajectories and seamless user experience. In a summary, at least in an embodiment, parallel processing and memory management module is configured to track more than one concurrent signals, maintain long-term memory across sessions, and support temporal pattern recognition and continuity of transformation.
[0044] Further, the system comprises a mentorship simulation and feedback module that emulates expert mentorship by validating insights generated by the inference and recommendation engine, detecting user blind spots, and providing expert-level feedback. The mentorship simulation and feedback module supports both end-users and professionals-in-training by offering real-time guidance and continuous reinforcement. In an embodiment, the mentorship simulation and feedback module optionally allows a Mentor User to operate and override the emulation logic/model to deliver supervised mentorship. The mentorship simulation and feedback module interacts with the skill transformation module, the expert knowledge base and capability map, and the inference and recommendation engine to contextualize feedback, reinforce learning, and align user progression with the overall transformation objectives. In a summary, at least in an embodiment, the mentorship simulation and feedback module is configured to emulate expert-level feedback, validate system-generated insights, detect blind spots, and/or provide learning reinforcement for both end-users and/or professionals-in-training.
[0045] The system further comprises the skill transformation engine that identifies the specific skills required to be developed by the user and generates a personalized capability development roadmap aligned with the user potential to enhance the superior life outcomes for the user. The skill transformation engine tracks the user progress across defined milestones and dynamically updates the roadmap based on the system feedback and user engagement. In an embodiment, the skill transformation engine applies Accelerated Time Compression (ATC) models to reduce the overall time required for transformation and milestone achievement in comparison with the conventional methods. The skill transformation engine operates in conjunction with the inference and recommendation engine, the transformation implementation module, and the mentorship simulation and feedback module to perform the skill gap analysis, generate personalized learning paths, and deliver content adaptive to the user. In a summary, at least in an embodiment, skill transformation engine is configured to generate a personalized capability development roadmap, track progress towards enhancing the milestones, and apply Accelerated Time Compression (ATC) models to reduce transformation and milestone achievement time compared to conventional methods.
[0046] Further, the system comprises a security, privacy, and compliance module responsible for governing data handling operations in accordance with one or more global data protection frameworks, including but not limited to the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA). The security, privacy, and compliance module manages end-to-end data encryption, access control based on the user roles, consent tracking, and data anonymization. In an embodiment, the security, privacy, and compliance module enforces data protection, encryption, consent management, and compliance with applicable privacy standards by monitoring and securing the inter-module communications of the system and maintaining the detailed audit logs. In a summary, at least in an embodiment, the security, privacy, and compliance module is configured to enforce data protection, encryption, consent management, and/or compliance with global data privacy regulations.
[0047] The system further comprises a cloud infrastructure and deployment module that serves as the backbone for the system scalability, availability, and cross-platform access. The cloud infrastructure and deployment module supports containerized deployment, load balancing, and edge computing for real-time responsiveness using technologies such as Kubernetes and Docker, and ensures seamless operation across public, private, edge, and hybrid environments. The cloud infrastructure and deployment module manages inter-service communication and real-time responsiveness through RESTful application programming interfaces (APIs), and integration with cloud service platforms such as Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure. facilitates efficient deployment and orchestration of system components across geographically distributed nodes. In a summary, at least in an embodiment, the cloud infrastructure and deployment module is configured to support scalable, containerized deployment of the system across cloud, edge, and /or hybrid environments.
[0048] In an embodiment, the system is adapted to understand and simulate the connection between capability sets and life consequences over time, enabling predictive modelling of future outcomes and strategic intervention planning.
[0049] Further, in another embodiment, the system is configured to generates output structured reasoning and transformation logic to support specialist intuition development, including annotated case sheets and transformation prescriptions for professional learning and calibration.
[0050] Furthermore, in an embodiment, the system is adapted to perform ecological prediction and personalized trajectory simulation to ensure that all recommended interventions are safe, sustainable, and/or contextually aligned with the user’s physiological, anatomical, bio-chemical, pathological, psychological and emotional state, and/or environmental conditions.
[0051] In an embodiment, the system is further adapted to enable polycontextual capability development and contextual optimization by identifying optimal contexts for skill acquisition and generalizing developed capabilities across multiple life domains.
[0052] Further, in an embodiment the system is adapted to, facilitate capability generalization across domains via connecting capabilities, allowing transfer of well-formed skills from one domain to another through system stimulated development of capabilities for bridging.
[0053] Furthermore, in an embodiment the system is adapted to perform scope enhancement by expanding the user’s perception of what is possible and desirable, through multimodal signal-driven discovery of latent choices, aspirations, potential and transformation opportunities.
[0054] Further, in an embodiment the system is adapted to perform dynamic personalized questioning by selecting and adapting questions in real time based on multimodal signal congruence, redundancy, scope, and/or consequence analysis.
[0055] Further, in an embodiment the system is configured to generate evolution mapping outputs that track identity-level changes, emotional maturity, and transformation milestones across time and life domains.
[0056] In an embodiment, the system is configured to generate a Personalized Evolution Chart, comprising a dynamically computed simulation of the user’s transformation journey including capability set derivation, ecological validation, and/or ATC prescription.
[0057] In an embodiment, the system is configured to apply Accelerated Time Compression (ATC) models to simulate superior life outcomes in significantly reduced time frame based on capability mapping, system stimulated capability development ecological validation.
[0058] Furthermore, in an embodiment, the system is adapted to deliver emotionally resonant interventions using Solar Voice Model, wherein the tone, pitch, and frequency of the voice are dynamically adapted to the user’s subconscious preferences to enhance internalization and rapport.
[0059] In another embodiment, the system is adapted to generate an Impact Chart with Contrast, configured to compare the user’s transformation outcomes against global statistical baselines across adjustments or transformations across one of skill development, capability development, changes in unconscious patterning, shifts in thinking, consequence, and/or evolution levels, thereby quantifying the improbability, drastically reduced time frames, significance, and systemic value of achieved changes.
[0060] In yet another embodiment, the system is adapted to continuously update its knowledge base for capability–life outcome mapping using validated historical data, real-time user interactions, and evolving societal trends.
[0061] The present invention further discloses a method for implementation of targeted and ecological permanent transformations of the users. The method comprises multiple sequential steps each configured to progressively guide the user through a comprehensive journey of self-discovery, capability enhancement, and behavioral evolution. The method is initiates by preparing the user through a phase of capability priming and state calibration. The preparation involves aligning the user’s internal cognitive, emotional, and physiological conditions with the relevant external factors to establish a foundational readiness for transformation. In an embodiment, the preparation step comprises acquiring multimodal input data from the user, wherein the input data comprises verbal responses and non-verbal physiological, anatomical, biochemical, pathological, psychological, emotional, and/or behavioral indicators. In a summary, in an embodiment, this step includes, acquiring multimodal input data from the individual through a user interface module and sensor integration and data acquisition module, wherein the input data comprises verbal responses, one or more non-verbal physiological, anatomical, bio-chemical, pathological, psychological and/or emotional state signals.
[0062] Further, the evaluation of the users is performed through multimodal diagnostics and signal-driven assessments, wherein the challenges, capability gaps, and priority transformation areas are identified. In an embodiment, the multimodal input data is adapted to extract emotional, cognitive, and behavioral features from the verbal and non-verbal responses including physiological signals such as EEG, heart rate, and micro-expressions of the user, and based on the extracted features, a user state vector is generated. In another embodiment, a personalized set of diagnostic questions is dynamically generated based on the real-time signal evaluation, contextual relevance, and historical user profile data. The generated questions are classified according to a four-layer framework comprising redundancy, congruence, scope, and consequences, thereby enabling precise identification and prioritization of the core limitations and transformation opportunities of the user. In a summary, in an embodiment, this step includes, processing the multimodal input data using a multimodal signal processing module configured to extract plurality of emotional, cognitive, and behavioral features from the verbal and non-verbal responses, and generating a user state vector. In an embodiment, the step further comprises the sub-step of dynamically generating a personalized set of diagnostic questions using an Intelligent Questioning and Diagnostic Engine, wherein the questions are selected based on real-time signal evaluation, contextual relevance, and historical user profile data, and categorized using a four-layer framework comprising redundancy, congruence, scope, and/or consequences.
[0063] Following the diagnostic assessment, personalized transformation pathways are recommended based on the diagnostic outcomes, contextual parameters, and domain-specific expert knowledge. The individual’s responses and extracted signal features are synthesized to identify capability gaps, transformation opportunities, and the root causes for cognitive, behavioral, or functional limitations. Based on this synthesis, a personalized transformation plan is generated by mapping the current capabilities of the individual to potential future outcomes, using a framework in which consequences accumulate over time through specific behavioral or contextual adjustments. In an embodiment, the recommendations are derived using multiple models such as Adjustment over Time = Consequences (AxT = C) and Capability × Experience = Evolution. Further, the simulated trajectory outputs are produced to generate structured transformation recommendations, including personalized evolution mapping. In an embodiment, an Accelerated Time Compression (ATC) prescription is generated, comprising a prioritized set of capabilities and corresponding transformation actions that reduce the time required for skill development, capability enhancement, and achievement of superior life outcomes. In another embodiment, an Impact Chart with Contrast is computed to compare the individual’s projected transformation outcomes against global statistical benchmarks, thereby quantifying the improbability, significance, reduced timeframes, and systemic value of the transformation process. In a summary, in an embodiment, this step includes, synthesizing the individual’s responses and signal features using an inference and recommendation engine to identify capability gaps, transformation opportunities, and root causes or systemic influences or systemic constraints of user challenges, and generating a personalized transformation plan using a capability gap model, a transformation framework comprising consequences compounding over time following certain set of adjustments, and trajectory simulation models including Evolution Mapping and Personalized Evolution Chart. In an embodiment, further it comprises the sub-step of generating an Accelerated Time Compression (ATC) prescription, comprising a prioritized set of capabilities and transformation set designed to reduce the time required for skill development, capability acceleration, enhanced life outcomes and computing an impact chart with contrast to compare the user’s projected outcomes against global benchmarks and to store in a skill transformation engine.
[0064] Further, the tailored interventions are delivered to the individual to facilitate skill acquisition and behavioral evolution aligned with the identified transformation needs. These interventions are implemented through multimodal delivery pathways that include Solar Voice Model, trance induction, age regression, peak performance simulations, Metaphors, Reorganizing unconscious priorities, VR Based simulations, reconditioning, personalized mental activities and mental games based on each individuals unique internal patterns. The delivery of these interventions is continuously adapted in real time based on physiological, anatomical, biochemical, pathological, psychological, and emotional signals of the user detected during the process, thereby ensuring internalization of each transformation action by the individual aligned with their current state. In a summary, in an embodiment, this step includes, implementing the transformation plan using a transformation implementation module configured to deliver one or more interventions through multimodal interfaces, including emotional voice, micro-learning, behavioral nudges, and coaching simulations, and to adapt delivery based on real-time user engagement and physiological, anatomical, bio-chemical, pathological, psychological and/or emotional state feedback.
[0065] In an embodiment, the delivery of the adaptive interventions is modulated according to the user engagement metrics and personalized responsiveness, ensuring contextual relevance. Further the validation of the intended transformation outcomes is performed by comparing the post-intervention data against the baseline data and the predefined transformation goals. In an embodiment, the post-intervention signals are compared against baseline data and transformation goals using congruence vector analysis and evolution mapping. In another embodiment, the transformation plan is iteratively refined based on the outcome validation, user feedback, and updated signal data, thereby ensuring ecological alignment, cross-domain capability generalization, and sustained personal evolution. In a summary, in an embodiment, this step includes, validating transformation outcomes using a mentorship simulation and feedback module, wherein one or more post-intervention signals are compared against baseline data and transformation goals using congruence vector analysis and evolution mapping. In an embodiment, further it comprises the sub-step of iteratively refining the transformation plan based on outcome validation, user feedback, and updated signal data to ensure ecological alignment, cross-domain capability generalization, and sustained personal evolution.
[0066] The method functions as a closed-loop and feedback-responsive transformation framework that continuously aligns with the evolving context, emotional conditions, and defined transformation objectives of the users. Furthermore, the modular architecture of the method enables scalable, personalized, and precise transformation delivery, making it suitable for diverse applications across personal development, professional growth, and behavioral change.
Brief description of the drawings
[0067] The foregoing and other features of embodiments will become more apparent from the following detailed description of embodiments when read in conjunction with the accompanying drawings. In the drawings, like reference numerals refer to like elements.
[0068] Figure 1 illustrates a block diagram of a system for implementation of targeted and ecological permanent transformations, in accordance with an embodiment of the invention.
[0069] Figure 2 illustrates a flowchart of a method for implementation of targeted and ecological permanent transformations, in accordance with an embodiment of the invention.
Detailed description of the invention
[0070] In order to more clearly and concisely describe and point out the subject matter of the claimed invention, the following definitions are provided for specific terms, which are used in the following written description.
[0071] The term “Ecological Permanent Transformation” refers to a long-lasting change in the user’s cognitive, emotional, behavioural, or skill state that is sustainable.
[0072] The term “Permanent Transformation” refers to a long-lasting change in the user’s cognitive, emotional, behavioural, or skill state that is sustainable.
[0073] The term “Capability Gap” refers to the difference between a user’s current ability and the level required to achieve a desired outcome.
[0074] The term “Solar Voice Model” refers to a voice output technique where tone, pitch, and modulation are adapted to the user’s subconscious preferences.
[0075] The term “User State Vector” refers to a combined mathematical representation of the user’s current emotional, cognitive, and physiological condition.
[0076] The term “Multimodal Input” refers to the user data, collected through various channels such as voice, text, gesture, physiological signals, and environment.
[0077] The term “Accelerated Time Compression (ATC)” refers to a framework that equips the user to achieve transformations and/or milestones in the shortest timeframe from their current state.
[0078] The present invention relates to a system for implementation of targeted and ecological permanent transformations of the users, comprising a plurality of interrelated modules to acquire and interpret verbal and non-verbal signals from the users, identify their capability gaps, generate transformation strategies, deliver personalized interventions, and track the evolution across diverse life domains, ensuring a personalized, scalable, and sustainable transformation for the users.
[0079] The present invention further relates to a method for implementation of targeted and ecological permanent transformations of users, comprising sequential steps comprising capability priming, multimodal assessment, transformation planning, intervention delivery, and outcome validation. The method is adapted to operate in a feedback-driven loop, utilizing personalized input data to iteratively refine the transformation journey in alignment with the user’s evolving internal states, contextual conditions, and transformation goals.
[0080] Figure 1 illustrates a block diagram of a system for implementation of targeted and ecological permanent transformations, in accordance with an embodiment of the invention. The system (100) comprises a User Interface (UI) module (101) that receives multimodal user inputs, such as spoken or typed responses, facial expressions, gestures, and other non-verbal cues.
[0081] The User Interface (UI) module (101) serves as the primary interaction gateway between the user and the system (100), facilitating seamless, intuitive, and intelligent communication. The User Interface (UI) module (101) supports a wide range of multimodal input and output mechanisms including traditional text-based interactions, voice commands, gesture recognition, and haptic feedback, thereby enabling the users to engage with the system (100) in a natural and accessible manner.
[0082] The User Interface (UI) module (101) operates using an adaptive and context-aware mechanism that dynamically adjusts the UI layout, interaction style, and content delivery based on the user’s device, such as a smartphone, tablet, desktop, or an wearable; the user’s preferences; accessibility needs; and real-time emotional or cognitive state of the user as inferred by the system (100). For example, in an embodiment, upon detecting cognitive fatigue or emotional distress in the user, the system (100) simplifies the interface, reduces cognitive load, or switches to a more empathetic tone of communication.
[0083] The User Interface (UI) module (101) further ensures responsive and cross-platform compatibility, providing a consistent performance and user experience across various operating systems and screen sizes. The User Interface (UI) module (101) supports real-time rendering of questions, insights, feedback, and transformation plans generated by an inference and recommendation engine (107), which are presented in a visually engaging and cognitively optimized format using visual cues, animations, and voice synthesis such as the Solar Voice Model, to enhance comprehension and retention by the user.
[0084] In an embodiment, the User Interface (UI) module (101) captures verbal responses as a voice input through a microphone, processed using a speech-to-text and Natural Language Understanding (NLU) model. In another embodiment, gesture inputs are captured through a camera-based motion tracking or wearable sensors, enabling the users to navigate or respond using hand movements or facial expressions. In yet another embodiment, the haptic inputs are captured through touchscreens or wearable devices, enabling the user to interact with the system (100) through tactile gestures such as tapping, swiping, or applying pressure. The multimodal inputs are pre-processed and forwarded to a multimodal signal processing module (104), where the inputs are analyzed for emotional tone, engagement level, and contextual relevance. The User Interface (UI) module (101) receives structured outputs from the inference and recommendation Engine (107) and a transformation implementation module (108), and renders them back to the user in a personalized and engaging manner.
[0085] The User Interface (UI) module (101) incorporates accessibility Application Programming Interfaces (APIs) that support screen readers, voice navigation, high-contrast modes, and other assistive technologies. The User Interface (UI) module (101) further enables real-time feedback loops, allowing users to rate their experience, provide clarifications, or request alternative formats for content delivery.
[0086] In an embodiment, the system (100) comprises a backend server responsible for managing the data flow, handling user requests, and coordinating communications between various modules of the system (100) with the User Interface (UI) module (101). Further, to support real-time audio and video communication between the user and the system (100), Web Real-Time Communication (WebRTC) protocol is employed; for enabling low-latency, bidirectional data exchange between the server and the user interface, WebSockets are utilized. Further, the user interface is developed using cross-platform frameworks such as React or Flutter, allowing for a responsive and consistent experience of the user across different devices. Furthermore, Voice-to-Text Application Programming Interfaces (APIs) are used to convert spoken input into structured text, and accessibility Application Programming Interfaces (APIs) are integrated in the system (100) to ensure compliance with the recognized accessibility standards such as the Web Content Accessibility Guidelines (WCAG) and the Americans with Disabilities Act (ADA). The captured multimodal inputs comprising voice, gesture, and the haptic signals are transmitted to the multimodal signal processing module (104) for interpretation, and obtains personalized insights, questions, and transformation plans from the inference and recommendation engine (107) and the transformation implementation module (108). The outputs are rendered back to the user through the User Interface (UI) module (101) in an adaptive, accessible, and engaging format.
[0087] The system (100) further comprises a user profile and context management module (102) that maintains a comprehensive, evolving, and context-aware digital representation of each user, and continuously learns from the user interactions, adapting to behavioral and environmental changes, and transmitting the relevant context to other functional modules of the system (100) to enable personalized and context-sensitive experiences by the user.
[0088] The user profile and context management module (102) stores and manages detailed user-specific data, including but not limited to the demographic information such as age, gender, language preferences, education level, and occupation; and further preserves historical interaction records such as previous sessions, answered questions, completed interventions, submitted feedback, and behavioral trends of each user over time. The user profile and context management module (102) further captures system-validated transformation goals and objectives defined by the user across various life domains, including health, career, relationships, and personal growth to ensure coherence with the transformation logic of the system (100).
[0089] The user profile and context management module (102) stores and manages emotional and cognitive states of the user, as inferred from multimodal signal processing, including mood trends, stress levels, engagement scores, and cognitive load. Further, the calibrated states corresponding to various triggers, recurring emotional patterns, uniquely expressed internal states and their meanings, past behavioral trends, and emotional responses to different stimuli are stored. Over time, by analyzing the behavioral patterns and emotional reactions to various stimuli, the user profile and context management module (102) develops and refines validated hypotheses about unspoken aspects of the user's life, inferred through sustained interaction with the system (100).
[0090] Furthermore, the user profile and context management module (102) incorporates a capability mapping mechanism that enables assessment of the current capability state of the user, that involves a comprehensive assessment of strengths, weaknesses, blind spots, regrets, and areas of guilt, along with recurring emotional patterns calibrated to the specific sensory modalities of the user. Sensory acuity is further evaluated by examining the individual's ability to distinguish and recall sensory inputs across different visual, auditory, kinesthetic, olfactory, gustatory, and vestibular channels wherein the unique expressions and internal states are calibrated and interpreted to uncover their personal significance. Further, the present trajectory (T0) of the individual is determined, along with the experiential and contextual factors contributing to its formation. This includes mapping historical behavioral patterns, attentional dynamics, and emotional responses to various environmental and internal triggers. Furthermore, the validated hypotheses are generated regarding unspoken life situations of the user, inferred through the interactive engagement of the user with the system (100).
[0091] The user profile and context management module (102) further stores contextual metadata, such as time of day, day of the week, GPS-derived location, device type, network status, and environmental factors such as the ambient light and noise levels. The contextual information is used in real-time to enhance decision-making processes across various modules of the system (100) and environmental sensitivity is maintained in the delivery of transformation content.
[0092] In an embodiment, the user profile and context management module (102) dynamically updates in real time based on the changes in the user behavior, feedback from the system (100), and inputs from the sensors. For example, upon detecting consistently positive responses of a user to visual content for a specific triggers or a context, the system (100) adapts its delivery strategy accordingly. Similarly, upon detecting an elevated stress level of the users during certain times or during certain probes or a states or on a topic, the system (100) adjusts the tone, pacing, or complexity of its interactions with the user.
[0093] Further, the user profile and context management module (102) enables session continuity, allowing the user to resume their transformation journey from any authorized device or platform. This is achieved through persistent session tracking, state management, and synchronization with the cloud infrastructure, ensuring seamless transitions and uninterrupted progress across multiple touchpoints.
[0094] The user profile and context management module (102) serves as the foundation for the personalization within the system (100). The stored user profile in the user profile and context management module (102), enables other modules to adapt their functioning based on the user context such as selection of diagnostic or exploratory questions, the recommendation of appropriate interventions, modulation of conversational tone in coaching dialogues, identification of probable or superior trajectory shifts, adjustment of transformation timeframes, selection of challenge levels for goal attainment, and ecological evaluation of proposed outcomes.
[0095] In an embodiment, the user profile and context management module (102) further incorporates privacy and consent management features; tracks user permissions, manages data access rights, and ensures compliance with global data protection regulations such as General Data Protection Regulation (GDPR), the Health Insurance Portability and Accountability Act (HIPAA), and the California Consumer Privacy Act (CCPA). Users retain full control over their personal data, and the system (100) supports data anonymization, minimization, and secure storage to maintain privacy and legal compliance.
[0096] The user profile and context management module (102) preserves the session continuity across multiple sessions and devices to ensure a seamless user experience; enables real-time personalization of every system interaction, and upholds privacy standards through embedded regulatory compliance features.
[0097] Further, the user profile and context management module (102) receives continuous input directly or indirectly from the user interface module (101), the sensor integration and data acquisition module (103), a multimodal signal processing module (104), the inference and recommendation engine (107), and the transformation implementation module (108). Processed data from the user profile and context management module (102) is transmitted to the inference and recommendation engine (107) for contextual decision-making; to the intelligent questioning and diagnostic engine (105) for adaptive questioning, and to the skill transformation engine (111) for the development of personalized capability.
[0098] The system (100) further comprises a sensor integration and data acquisition module (103) that acquires physiological signals of the user including, but not limited to, heart rate, skin conductance, electroencephalography (EEG) signals, and micro-expressions through integrated devices such as cameras, microphones, and wearable sensors. The sensor integration and data acquisition module (103) is responsible for capturing a wide spectrum of non-verbal responses from the user during interactions. These responses are essential for understanding the user's emotional state, cognitive load, physiological condition, and behavioral cues that provide context and depth to the verbal responses captured through the UI module (101). In an embodiment, the sensor integration and data acquisition module (103) interfaces with a variety of external and embedded sensors, including wearables, environmental sensors, biometric devices, and computer vision systems. These sensors work in real time to collect physiological, behavioral, and environmental data, which are transmitted to the Multimodal Signal Processing Module (104) for interpretation. During the user interaction with the system (100), specifically with the UI module (101), these sensors continuously monitor subtle physiological and behavioral signals and transmit them to the multimodal signal processing module (104) for further analysis. As an example, a sudden increase in heart rate or skin conductance indicates stress or anxiety; clenching of the jaw or tightening of facial muscles suggests discomfort or deception; EEG wave patterns reveals cognitive engagement, emotional arousal, or truthfulness/congruence; and voice pitch and tone reflects confidence, hesitation, or the emotional shifts of the user.
[0099] Further, the incongruence set, refers to the observable mismatches between the verbal and non-verbal cues, for example, a person sounds excited in their tone, yet display clenched muscles, indicating possible conflict. Similarly, saying 'yes' while nodding 'no' suggests a contradiction between the spoken intent and physical response. In another instance, a person dismisses a topic as irrelevant, yet their sensory data shows heightened physiological activity, pointing to a potential unconscious objection. Further, during an asymmetrical facial expression or sensor data reveals one side of the face or the body exhibits relaxed muscles while the other side shows tension. This imbalance indicates an internal conflict or emotional incongruence related to the topic or the probe being discussed. In an embodiment, these signals are captured as multi-layered data streams that run in parallel with verbal responses, enabling the system (100) to derive deep, contextual insights about the user’s internal state.
[00100] The sensor integration and data acquisition module (103) captures multiple data including but not limited to, Heart Rate Variability (HRV), Skin conductance (electrodermal activity), facial micro-expressions, voice tone, pitch, and modulation; muscle tension and movement; eye movement and blink rate; brainwave activity (EEG); respiratory rate and rhythm; posture and body orientation; environmental context such as light, noise, temperature, and their combinations for generating a comprehensive, multimodal input stream that reflects the user’s physiological, emotional, cognitive, and contextual state in real time to support downstream diagnostic and inferential processes within the system (100).
[00101] In an embodiment, the Heart Rate Monitor uses photoplethysmography (PPG) to measure the volume of blood flow, providing real-time heart rate data per minute to detect stress, excitement, or calmness; the EEG Headband utilizes electrodes placed on the scalp of the user to detect electrical activity in the brain, and captures brainwave activity such as presence of alpha, beta, theta, delta, gamma waves to assess attention, relaxation, cognitive load, and emotional arousal; the Skin Conductance Sensor (GSR) measures the sweat gland activity to detect emotional arousal or stress; the facial recognition camera analyzes facial expressions and micro-expressions to infer emotions such as happiness, anger, or anxiety, including micro muscle movements and calibrated responses; the microphone captures the voice tone, pitch, speed, and pauses to assess confidence, hesitation, or emotional state; the accelerometer detects the body movement, restlessness, or fidgeting, indicating nervousness or discomfort; the gyroscope measures the orientation and balance, useful for detecting posture shifts or subtle body language; the temperature sensor monitors skin or ambient temperature, which changes with the stress or emotional arousal; light sensor detects the ambient lighting conditions to adjust the UI module (101) and interpret user comfort; the noise sensor measures the background noise to assess environmental distractions or stressors; the blood pressure monitor tracks the systolic and diastolic pressure of the user, that rises with anxiety or excitement; the pulse oximeter measures the blood oxygen saturation and pulse rate, useful for detecting physiological stress; the respiration rate sensor monitors the breathing patterns, that change with the relaxation or tension; the galvanic skin response sensor detects the changes in skin resistance, linked to the emotional arousal; the Eye-Tracking sensor tracks the gaze direction, blink rate, and pupil dilation to assess focus, interest, or deception; the EMG Sensor (Electromyography) measures the muscle activity, especially in the face or jaw, to detect tension or clenching; the infrared sensor detects the heat signatures and facial blood flow indicating the emotional changes; the Ultra Violet (UV) sensor monitors the UV exposure, useful in the outdoor contexts for environmental awareness; the CO2 sensor measures the carbon dioxide levels in the environment to assess the air quality and potential cognitive fatigue;
[00102] Further the Global Positioning system (GPS) sensor tracks the location of the user to provide contextual awareness such as presence of user at home, work, or at a public space; the barometer measures the atmospheric pressure that affects the mood and physical comfort; and the proximity sensor detects the user distance from the device, useful for engagement tracking.
[00103] In an embodiment, the EEG Headbands connected to the user capture the brainwave activity across different frequency bands, each associated with specific cognitive and emotional states. For example, the presence of Alpha Waves (8-12 Hz) indicate relaxation and calmness, often observed during meditation or restful states; Beta Waves (13-30 Hz) are associated with the active thinking, focus, and problem-solving. In contrast, the high beta activity indicates stress or anxiety; Theta Waves (4-7 Hz) are linked to creativity, intuition, and daydreaming which is common during the light sleep or deep relaxation; Delta Waves (0.5-3 Hz) are present during the deep sleep and restorative processes. High delta activity during wakefulness indicates brain injury or dysfunction; and Gamma Waves (30-50 Hz) are associated with the high-level cognitive processing, learning, and information integration.
[00104] In an embodiment, the brain wave sensor captures more than sixteen raw signals to arrive at the patterns of sixteen waves that correlate to specific states of high performance and low performance of the user. By analyzing the brainwave patterns, the system (100) infers the combination of changes of these waves (alpha, beta, delta, theta, gamma) across different regions of the brain, the user’s cognitive load, emotional arousal, and even truthfulness/congruence. For example, increased beta activity combined with an elevated heart rate and skin conductance suggest stress or anxiety, while high alpha activity with relaxed facial expressions indicates calmness and receptivity.
[00105] In an embodiment, the sensors operate in synchronized parallel streams, capturing continuous data while the user interacts with the system (100). In an exemplary scenario, while answering a question, the EEG headband of a user detect increased beta wave activity indicating cognitive effort; the GSR sensor shows elevated skin conductance suggesting stress; the microphone picks up a slight tremor in the voice, the facial recognition camera detects a micro-expression of discomfort; the eye-tracking sensor reveals gaze aversion, and the EMG sensor detect jaw clenching. Together, these signals form a multi-dimensional profile of the user’s internal state, which is interpreted by the system (100) to assess their emotional and cognitive engagement; identify stress, anxiety, or confidence; and further adapt the questioning strategy or intervention delivery in real time.
[00106] The integration of these sensors allows the system (100) to create a comprehensive, real-time profile of the user’s physiological and emotional state, wherein the physiological set include the anatomical, bio-chemical, pathological, psychological and emotional state with their dynamic responses.
[00107] In an embodiment, while the user is engaged in a task, the EEG headband detects increased beta wave activity, indicating cognitive effort. Simultaneously, the heart rate monitor shows an elevated heart rate, suggesting stress. The system (100) correlates these signals to determine the cognitive load and stress experienced by the user, prompting the user interface module (101) to adjust the interaction to reduce pressure.
[00108] In another embodiment, during a challenging question, the GSR sensor detects increased skin conductance, indicating emotional arousal. The facial recognition camera captures a micro-expression of surprise, incongruence or confusion. The system (100) interprets these signals to indicate the user struggle with the question and the need of additional support or clarification.
[00109] In another embodiment, during a verbal response of the user, the microphone picks up a tremor in the voice, suggesting hesitation or uncertainty. The eye-tracking sensor reveals the user’s avoidance of eye contact, a potential sign of discomfort or deception. The system (100) uses these insights to adapt its questioning strategy, by offering reassurance or rephrasing the question. Moreover, the eye-tracking sensor captures unique patterns in eye movement and gaze behavior in response to specific contexts or topics being explored. This data is used to selectively navigate toward critical components that the individual is avoiding or that require heightened focus, enabling a deeper and more targeted engagement.
[00110] In yet another embodiment, the EMG sensor detects muscle tension in the user’s jaw, indicating stress or anxiety. The infrared sensor shows increased facial blood flow, further confirming emotional arousal. The system (100) uses this data to adjust its tone and pacing, aiming to calm the user and create a more supportive interaction environment.
[00111] In another embodiment, the respiration rate sensor monitors the user’s breathing patterns, detecting shallow or rapid breaths that indicate stress. The noise sensor picks up background noise, suggesting a distracting environment. The system (100) recommend a quieter space or provide calming prompts to help the user focus if needed.
[00112] Additionally, in another embodiment, the sensors are calibrated to detect states of comfort and excitement. Based on these readings, the system (100) dynamically generates follow-up questions to deepen and enhance the elicited positive states, thereby facilitating richer and higher-quality engagement. By continuously monitoring and integrating data from the sensors, the system (100) dynamically adapt its interactions to the user’s current state. The multi-layered approach ensures a responsive and empathetic system (100), capable of traversing through multiple and multidimension personalised information and arriving at the required personalized understanding in the shortest possible way, providing a personalized and supportive experience to the user that enhances the effectiveness of the transformation process.
[00113] The sensor data is collected in real time and pre-processed locally or at the edge. The raw or the semi-processed data is sent to the multimodal signal processing module (104) wherein the structured signals are generated for use by the Inference and recommendation Engine (107), Intelligent questioning and diagnostic Engine (105), and the User Profile Module and context management module (102).
[00114] The transmitted data received by the multimodal signal processing module (104), interprets the user’s input across multiple dimensions. In an embodiment, the multimodal signal processing module (104) uses Natural Language Processing (NLP), computer vision, and biometric signal analysis to decode the user’s emotional state, cognitive load, and behavioral patterns. In an embodiment, the multimodal signal processing module (104) detect stress from the voice pitch, engagement from facial expressions, or emotional tone from the word choice. These interpretations are essential for understanding the user’s current state and tailoring the system’s responses accordingly.
[00115] The multimodal signal processing module (104) is responsible for interpreting and transforming raw, heterogeneous data streams captured from various sensors and user interfaces into a structured, meaningful signals. In an embodiment, these signals reflect the user’s emotional state, cognitive load, behavioral patterns, incongruence, and physiological responses. The multimodal signal processing module (104) acts as the intelligent bridge between raw sensor data and high-level inference, enabling the system (100) to understand the user beyond their verbal responses.
[00116] The multimodal signal processing module (104) processes inputs from multiple modalities simultaneously, including but not limited to speech and voice such as tone, pitch; facial expressions and micro-expressions; gestures and body posture; physiological signals such as heart rate, EEG, skin conductance; and environmental context such as noise, light, and further extracts high-dimensional features from these inputs and convert them into structured representations to be used by downstream modules such as the Inference and Recommendation Engine (107), Intelligent Questioning and Diagnostic Engine (105), and the user profile and context management module (102).
[00117] The multimodal signal processing module (104) further leverages a suite of Artificial Intelligence (AI) and Machine Learning (ML) models, wherein each model is specialized for processing a specific input modality and trained on large, diverse datasets for high-fidelity, real-time inference. In an embodiment, Natural Language Processing (NLP) models are employed to analyze spoken or written language in order to detect sentiment, user intent, and emotional tone utilizing the techniques of sentiment analysis to classify textual data into positive, negative, or neutral categories; emotion classification for identifying emotional states such as joy, anger, fear, or sadness; and named entity recognition coupled with the contextual information extraction. Further, representative models applied for this purpose include Bidirectional Encoder Representations from Transformers (BERT), Robustly Optimized BERT Pretraining Approach (RoBERTa), Distilled BERT (DistilBERT), and Whisper for speech-to-text processing.
[00118] In another embodiment, Computer Vision models are utilized to analyze facial expressions, eye movement, and gestures to extract non-verbal cues of the user. The techniques implemented comprises the Facial Action Coding System (FACS) for detecting micro-expressions, pose estimation techniques for recognizing physical gestures, and eye-tracking to assess attentional focus and user engagement, utilizing the models such as OpenFace, MediaPipe, OpenPose, YOLO version 8 (YOLOv8), DeepFace, and optionally, fuzzy logic–based models.
[00119] In an embodiment, Speech Emotion Recognition (SER) models are utilized to analyze vocal features such as pitch, jitter, shimmer, and Mel-Frequency Cepstral Coefficients (MFCCs) that reflect the emotional states and stress levels of the users, utilizing the models such as Convolutional Neural Network–Recurrent Neural Network hybrids (CNN-RNN), wav2vec 2.0, and DeepSpeech integrated with emotion classification layers.
[00120] In another embodiment, Physiological Signal Processing models are used to interpret biosignals such as heart rate, electroencephalogram (EEG), and skin conductance wherein the signal analysis methods include time-series analysis, frequency domain analysis such as Heart Rate Variability (HRV), EEG band power computation, and anomaly detection techniques. The models utilized include Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), and Transformer-based models optimized for the time-series data.
[00121] Furthermore, in another embodiment, Multimodal Fusion models are applied to combine the insights derived from individual modalities to construct a unified and contextually enriched representation of the user’s state. The fusion strategies encompass early fusion at the feature level, late fusion at the decision level, and attention-based fusion for context-aware weighting of multimodal signals. The models employed for fusion include Multimodal Transformers, Tensor Fusion Networks (TFN), and the Multimodal Transformer (MMT) architecture.
[00122] The invention envisaged various embodiment, to support real-time processing of multimodal data and execution of AI models, the system (100) utilizes a robust hardware stack, in different combinations, including but not limited to Edge AI Devices such as NVIDIA Jetson Xavier, Google Coral, Intel Movidius to Perform on-device inference for latency-sensitive tasks such as facial expression recognition, voice analysis; High-Performance Central Processing Units (CPUs) and Graphical Processing Units (GPUs) such as Intel i9, AMD Ryzen 9, NVIDIA RTX 4090, A100 to Run deep learning models for Natural Language Processing (NLP), computer vision, and time-series analysis; Tensor Processing Units (TPUs) to accelerate training and inference of large-scale transformer models; Dedicated Digital Signal Processors (DSPs) to Efficiently process audio and biosignal data (e.g., ECG, EEG); Sensor Hubs and Microcontrollers such as ARM Cortex-M series, STM32 to Interface with sensors, perform initial signal conditioning and filtering; Memory and Storage such as RAM of minimum 32GB for real-time processing; and SSD storage devices with high IOPS for fast data access and logging
[00123] In an embodiment, the system (100) operates through multiple stages beginning with data ingestion, wherein raw data is streamed from the sensor integration and data acquisition module (103). This data includes audio signals from the microphones, video frames from cameras, time-series data from biosensors such as electroencephalogram (EEG) and Galvanic Skin Response (GSR), and environmental data such as ambient light and noise levels. During the pre-processing stage, each data stream undergoes signal-specific transformations. Audio data is subjected to noise reduction and voice activity detection, video data is processed through frame normalization and face detection, EEG data is filtered using artifact removal and bandpass techniques, and GSR or heart rate variability (HRV) data is smoothed and analyzed using peak detection. In the feature extraction stage, machine learning models extract modality-specific features, such as Mel-Frequency Cepstral Coefficients (MFCCs) from audio, facial landmarks and action units from video, alpha, beta, and theta wave power from EEG, and HRV metrics from heart rate data. These features are used during the inference stage, wherein each modality is analyzed to infer emotional state of the user such as calm, anxious, or excited, to find cognitive load such as focused or distracted, to understand engagement level such as attentive or disengaged, truthfulness/congruence indicators such as hesitation or stress markers, and incongruence such as a user saying “Yes” while nodding “No”. The outputs from all the modalities are fused using attention-based models to generate a composite user state vector representing the user’s current emotional, cognitive, and physiological state. Finally, the structured output is passed to the Intelligent Questioning and Diagnostic Engine (105) to adapt the next question based on the user state, to the Inference and Recommendation Engine (107) for correlating verbal and non-verbal signals to derive deeper insights, and to the User Profile and Context Management Module (102) to update the emotional and behavioral trends of the user.
[00124] The multimodal signal processing module (104) enables a wide range of use cases by interpreting and synthesizing multimodal input data from the users. These use cases include detecting stress or anxiety during sensitive questions, identifying user disengagement or cognitive overload, adjusting the tone and pacing of interventions in real time, validating verbal responses using physiological cues, personalizing content delivery based on the user’s emotional readiness, and detecting incongruence between verbal and non-verbal behavior.
[00125] In an embodiment, the multimodal signal processing module (104) transmits the structured signals to the intelligent questioning and diagnostic engine (105) and the inference and recommendation engine (107) wherein the input sources for the multimodal signal processing module (104) comprise raw data received from the sensor integration and data acquisition module (103) that provides physiological signals such as electroencephalography (EEG), heart rate, Galvanic Skin Response (GSR), and respiration data; environmental signals including ambient noise, light, and temperature; and behavioral signals such as posture, gestures, and facial expressions. The user interface (UI) module (101) further supplies verbal inputs in the form of speech and text, as well as gestural and haptic inputs.
[00126] Further, a pre-processing layer within the multimodal signal processing module (104) processes each data stream to clean and normalize the input. In an embodiment, the audio signals undergo noise reduction, silence trimming, and speech segmentation; video frames are processed through face detection, frame normalization, and landmark extraction; EEG and bio-signals are subject to artifact removal, filtering, and signal smoothing; and text data is processed using tokenization, lemmatization, and sentiment tagging.
[00127] Further, a feature extraction layer within the multimodal signal processing module (104) employs specialized artificial intelligence models to extract high-dimensional features from each modality. In an embodiment, the voice inputs undergo features extraction such as pitch, tone, Mel-Frequency Cepstral Coefficients (MFCCs), jitter, and shimmer; facial signals yield action units, micro-expressions, and gaze direction; EEG signals provide power distributions in alpha, beta, theta, and gamma frequency bands; GSR and Heart Rate Variability (HRV) signals yield stress markers and arousal levels; and textual inputs are analyzed for sentiment, intent, emotion, and topic relevance.
[00128] Further, each modality-specific feature set is passed through an inference layer within the multimodal signal processing module (104) wherein Natural Language Processing (NLP) models such as BERT and RoBERTa are used for emotion and intent detection; computer vision models such as OpenFace and MediaPipe are utilized for facial analysis; speech emotion recognition is performed using models such as wav2vec 2.0 and DeepSpeech; and time-series models including Long Short-Term Memory (LSTM) networks and Transformer-based architectures are used for interpreting EEG and HRV signals.
[00129] Further, the modality-specific inferences are fused into a unified user state vector within a multimodal fusion layer. The fusion process involves attention-based fusion, contextual weighting, and temporal alignment and the resulting user state vector represents the emotional state such as calm, anxious; cognitive load such as focused, overloaded; engagement level; truthfulness/congruence indicators; and incongruence patterns.
[00130] Further, the output within the multimodal signal processing module (104) generates structured outputs that include the user state vector as a composite representation of the user’s current state, signal confidence scores indicating the reliability of each modality’s inference, temporal tags for synchronization, and a trigger response vector that represents a composite set of internal and external inputs that elicit a particular user state.
[00131] Furthermore, the structured signals are routed the intelligent questioning and diagnostic engine (105) that utilizes the signals to adapt subsequent questions based on the user’s emotional, cognitive, and physiological state to uncover capabilities required for achieving desired life outcomes. The intelligent questioning and diagnostic engine (105) is further capable of skipping, rephrasing, or deepening questions based on the user engagement. The intelligent questioning and diagnostic engine (105) employs contextual inference and adaptive logic to determine which questions will yield the most insight into the user's challenges. By narrowing down from potentially millions of questions to a concise, context-aware subset, the intelligent questioning and diagnostic engine (105) minimizes cognitive overload and ensures that the diagnostic process is efficient, targeted, and personalized based on the user’s current state and historical profile. Further, the structured signals from the multimodal signal processing module (104) are routed to the inference and recommendation engine (107) that correlates the multimodal data to the historical patterns to detect root causes and capability gaps; and to personalize transformation strategies of the user.
[00132] In an embodiment, the intelligent questioning and diagnostic engine (105) dynamically selects and sequences the most relevant questions from a vast and expandable knowledge base comprising millions of diagnostic and exploratory prompts with an objective to uncover the root causes of the user's challenges and extract meaningful data in the shortest possible timeframe, while maintaining minimal cognitive load through adaptive, personalized, and context-aware questioning.
[00133] In an embodiment, the adaptive questioning is achieved by continuously adjusting the questioning strategy in real time based on the user’s emotional and cognitive state received from the multimodal signal processing module (104), historical responses and behavioral patterns from the user profile and context management module (102), engagement level and stress indicators, heightened emotional or physiological signal intensity, and incongruence between the verbal and non-verbal cues. Each question is further scored for relevance based on contextual fit with the ongoing session, its probability of yielding high-value insights, its alignment with the transformation goals, and the user’s emotional readiness and receptivity. Further, a contextual branching is facilitated through decision trees and probabilistic models that guide the interaction flow by branching into deeper or adjacent topics depending on the user responses to avoid redundancy, maintains conversational coherence, and ensures emotional safety. In an embodiment, the exploratory depth of questioning is dynamically controlled to suit the user’s cognitive condition, ranging from surface-level probing during initial exploration to the deep-dive inquiries for root cause analysis; and is further adjusted or simplified based on the real-time cognitive load assessments of the user.
[00134] The intelligent questioning and diagnostic engine (105) integrates multiple key technologies such as Reinforcement Learning (RL) is used to iteratively optimize the questioning policies based on long-term feedback; Natural Language Understanding (NLU) is applied to interpret user responses; Knowledge graphs are employed to map the interrelationships between the life domains, symptoms, and root causes; semantic search mechanisms are utilized to retrieve the questions based on meaning rather than mere keyword matches; and, contextual bandit algorithms are used to balance the exploration and exploitation in the selection of questions during the user interaction.
[00135] The data flow within the intelligent questioning and diagnostic engine (105) involves multiple stages. The input received from the multimodal signal processing module (104) comprises emotional state, cognitive load, engagement level, truthfulness/congruence indicators, incongruence sets, and psychological patterns, along with additional data from the user profile and context management module (102), such as demographics, personal goals, historical data, preferred communication style, and previously recorded question-response patterns.
[00136] Further, the user state and context are fused into a unified session context vector. This vector is used to query the expert knowledge base and capability map (106) using semantic and contextual filters as well as psychological sets. Candidate questions are further scored based on the relevance to the session context, emotional and cognitive readiness, and historical effectiveness derived from reinforcement learning feedback mechanisms, from the output of the multimodal signal processing module (104). Based on the scoring, the top-N questions (typically 1 to 3) are selected for delivery, accompanied by metadata such as intended tone and delivery mode.
[00137] The output from the intelligent questioning and diagnostic engine (105) is routed to the user interface module (101) that provides selected questions ready for text, voice, or gesture-based delivery, along with metadata such as tone, pacing, and parameters for Solar Voice Model-based rendering. The output from the intelligent questioning and diagnostic engine (105) is further routed to the inference and recommendation engine (107) transmitting the user’s verbal and non-verbal responses, metadata related to the questions such as intent, domain, and difficulty level, and confidence scores along with the branching logic for further analysis.
[00138] The intelligent questioning and diagnostic engine (105) significantly reduces the time to find the insight of the user by prioritizing high-impact questions early in the interaction; minimizes user fatigue by adapting to the user’s emotional state and using emotionally intelligent questioning; and enables expert-level diagnostic interactions without requiring direct human intervention. In addition, intelligent questioning and diagnostic engine (105) facilitates the uncovering of systemic connections and influences.
[00139] The system (100) further comprises the expert knowledge base and capability map (106) that serves as the central repository of structured expert knowledge that powers the system's (100) ability to diagnose user challenges, map the capability gaps, and recommend personalized transformation strategies. The expert knowledge base and capability map (106) acts as the intellectual backbone of the system (100), enabling intelligent inference, semantic reasoning, and skill development planning.
[00140] In an embodiment, the internal structure of the expert knowledge base and capability map (106) comprises ontologies of life domains, skill taxonomies, capability gap models, and transformation protocols. The ontologies of life domains are structured representations of key areas of human life, such as sleep, nutrition, stress related to health; leadership, productivity, decision-making related to career; communication, empathy, conflict resolution related to relationships; and self-awareness, resilience, purpose related to the personal growth. Each domain is further divided into subdomains, concepts and micro components, and interdependencies.
[00141] The internal structure of the expert knowledge base and capability map (106) comprises hierarchical skill taxonomies that include cognitive skills such as critical thinking, memory, focus; emotional skills such as regulation, empathy, confidence; behavioral skills such as habits, routines, adaptability, and social skills such as listening, persuasion, collaboration. Each skill is further defined with clear definitions, proficiency levels, observable behaviors, and assessment criteria.
[00142] In an embodiment, the internal structure of the expert knowledge base and capability map (106) incorporates capability gap models, which are frameworks that define ideal capability profiles for specific goals or roles, common deficiencies and their root causes, and progression paths from current to desired capability states for the users. The capability gap models are further utilized to compare user profiles against established benchmarks, identify missing or underdeveloped capabilities, recommend targeted interventions, and run simulations of life outcomes.
[00143] In an embodiment, the transformation protocols are embedded within the expert knowledge base and capability map (106) and represent expert-designed sequences of interventions aimed at skill development, emotional healing, behavioral change, and capability development. The transformation protocols include step-by-step guidance, prerequisites and dependencies, expected outcomes and timelines, and test conditions to validate completion of the implementation.
[00144] In an embodiment, the expert knowledge base and capability map (106) utilizes key technologies such as semantic graphs to represent relationships between concepts, skills, and outcomes; ontology management to enable modular updates and version control; inference engines to support logical reasoning and capability mapping; search indexes for fast retrieval using semantic and contextual queries; and trajectory simulations for modelling the future outcomes.
[00145] The expert knowledge base and capability map (106) receives inputs from the intelligent questioning and diagnostic engine (105) in the form of contextual queries, branching logic, and user responses. Additional inputs are received from the inference and recommendation engine (107) for root cause analysis, capability gap detection, and transformation strategy generation. The skill transformation engine (111) further communicates with the expert knowledge base and capability map (106) to request skill mappings, generate learning paths, and update progress of the users.
[00146] In an embodiment, the knowledge base and capability map (106) performs semantic search and reasoning to match the user context with relevant knowledge nodes, traverse ontologies to find related skills or causes, and score; and rank the results based on the relevance and impact. Further, capability mapping is performed by comparing the user profile with ideal capability models and identifying the gaps wherein protocol retrieval functions identify and select one or more transformation protocols that are aligned with the user’s goals, emotional readiness, and contextual parameters.
[00147] The knowledge base and capability map (106) generates and transmits structured outputs to the intelligent questioning and diagnostic engine (105) to provide contextually relevant diagnostic questions and follow-up prompts based on inferred gaps; to the inference and recommendation engine (107) to supply root cause hypotheses, capability gap insights, and recommended transformation strategies; and to the skill transformation engine (111) to transmit personalized skill development roadmaps, learning content and sequencing, and progress benchmarks.
[00148] The expert knowledge base and capability map (106) enables expert-level reasoning without the need for human experts, and ensures consistency, accuracy, and personalization in diagnostics; supports scalable and modular knowledge integration; and bridges the gap between the user data and the actionable transformation strategies.
[00149] The system (100) further comprises the inference and recommendation engine (107) , responsible for synthesizing a large and expandable knowledge base of over millions multimodal data inputs, interpreting user states, identifying root causes of user challenges, detecting capability gaps, and generating personalized transformation strategies. The inference and recommendation engine (107) acts as the brain of the system (100), integrating insights from all other modules to produce actionable, context-aware, and adaptive recommendations that guide the user’s transformation journey.
[00150] The core objectives of the inference and recommendation engine (107) include performing root cause analysis by correlating verbal and non-verbal signals, incorporating world data, applying specialized knowledge, and drawing upon historical patterns. The inference and recommendation engine (107) further detects capability gaps across emotional, cognitive, behavioral, and skill domains, and generates recommendations that are timely, relevant, and aligned with the user’s goals and present state. In an embodiment, the inference and recommendation engine (107) moves beyond surface-level inputs by uncovering deeper, often hidden drivers of user behavior, systemic influences, and transformation requirements, thereby increasing the likelihood of achieving optimal life outcomes for the user in their current context.
[00151] In an embodiment, the inference and recommendation engine (107) comprises several functional layers. A data aggregation layer collects structured outputs from the multimodal signal processing module (104), the user profile and context management module (102), the intelligent questioning and diagnostic engine (105), and the skill transformation engine (111). These data streams representing emotional state, history, preferences, goals, user responses, capability maps, and learning paths are synchronized and normalized to ensure cohesive downstream processing.
[00152] Following aggregation, a feature engineering and representation layer converts the raw and structured data into high-dimensional feature vectors. These vectors encode emotional and physiological states of the user such as anxiety or focus; behavioral patterns such as hesitation or engagement; cognitive indicators such as overload or clarity; historical performance trends such as recurring difficulties and progress velocity; and comparative achievement mappings that factor in family setup, available opportunities, demographic conditions, and relative benchmarking against global standards. These features collectively build a composite user state model reflecting both the current and evolving conditions for the users.
[00153] The inference and recommendation engine (107) leverages a suite of artificial intelligence and machine learning models, encompassing supervised, unsupervised, and reinforcement learning techniques. Probabilistic reasoning models such as Bayesian networks are used to model dependencies between user states and potential outcomes, while Hidden Markov Models (HMMs) are employed to track transitions in emotional or cognitive states over time. Further, pattern recognition capabilities are enabled through clustering algorithms such as K-Means and DBSCAN, that identify the behavioral clusters, while the anomaly detection models highlight deviations indicative of distress or transformative breakthroughs. Further, contextual modeling techniques such as contextual bandits and graph neural networks support the real-time intervention optimization and model relationships among the user goals, emotional states, and skill development needs. Further, recommendation systems based on the collaborative filtering, content-based filtering, and hybrid approaches help to suggest appropriate interventions, and reinforcement learning models such as policy gradient methods and Q-learning adaptively refine the recommendation strategies based on the user feedback and observed outcomes.
[00154] Root cause analysis is a critical sub-function within the inference and recommendation engine (107). In an embodiment, the root cause analysis sub-module identifies underlying challenges by mapping multimodal user signals and responses to psychological and behavioral frameworks; causal inference techniques are used to differentiate correlation from causation; and knowledge graphs help trace dependencies between the symptoms and deeper systemic issues. The inference and recommendation engine (107) further constructs dynamic models showcasing the creation of cascading effects across the user’s life though a small changes in thoughts, emotions, behaviors, or external conditions. The inference and recommendation engine (107) further identifies redundant influences and compensatory subsystems that stabilize negative environmental pressures or constraints. For example, on exhibiting repeated signs of stress by a user while discussing career goals, the inference and recommendation engine (107) infers a latent issue related to self-efficacy or fear of failure. Additionally, the inference and recommendation engine (107) detects the presence of a strong support system that buffers stress, or recognize that a subtle shift in self-perception could initiate broader transformation across multiple life domains.
[00155] A capability gap detection component of the inference and recommendation engine (107) evaluates the user's current state against ideal capability profiles retrieved from the expert knowledge base and capability map (106). Based on the observed behavior and responses, the inference and recommendation engine (107) identifies missing or underdeveloped skills, emotional or cognitive barriers, and behavioral patterns impeding progress. In an embodiment, the inference and recommendation engine (107) further anticipates future growth trajectories and proactively recommends capabilities that helps the user to manage the enhanced challenges accompanying personal development. Further, the gaps are prioritized based on their urgency, impact, and the user’s readiness for change.
[00156] Using the insights derived from the root cause analysis and the capability gap detection, the recommendation generator within the inference and recommendation engine (107) determines the most suitable interventions for the user. In an embodiment, the generator selects interventions based on ease of implementation, ecological alignment, and potential for capability generalization across multiple domains. The interventions types include micro-learning, coaching, metaphor-based reframing, reorganization of unconscious priorities, VR simulations, cognitive reconditioning, reflective prompts, and personalized mental exercises or games customized to the user's unique internal landscape. The generator further chooses the optimal delivery mode such as visual, auditory, or interactive, and schedules the timing and frequency of interventions to maximize effectiveness. All the recommendations generated by the inference and recommendation engine (107) are contextual, adapting to the user’s emotional state, environment, and cognitive bandwidth at any given moment.
[00157] In an embodiment, the inference and recommendation engine (107) is supported by a robust hardware infrastructure to enable real-time inference and recommendation delivery. The infrastructure comprises high-performance compute units such as GPUs such as NVIDIA A100 or RTX 4090; TPUs for transformer-based workloads, and CPUs such as Intel Xeon or AMD EPYC for orchestration. Memory requirements further include a minimum of 64 GB RAM to support concurrent model execution and NVMe SSDs for fast data access, and Edge AI devices such as NVIDIA Jetson or Google Coral are used for latency-sensitive processing. Further, cloud infrastructure incorporates Kubernetes for container orchestration, TensorFlow Serving or ONNX Runtime for model deployment, and real-time data streaming with Redis or Kafka.
[00158] The inference and recommendation engine (107) receives structured signals from the multimodal signal processing module (104), user history and context from the user profile and context management module (102), capability maps from the skill transformation engine (111), and user response data from the intelligent questioning and diagnostic engine (105), and performs real-time inference using AI models, synthesizes a unified user state, identifies root causes and capability gaps, and generates personalized recommendations. Output from the inference and recommendation engine (107) is routed to the user interface module (101) for delivery of recommendations and insights, to the transformation implementation module (108) for executing intervention plans, and back to the user profile and context management module (102) for updating insights and tracking state changes.
[00159] The inference and recommendation engine (107) enables deep personalization by combining real-time input with long-term user trends and profile data; exhibits contextual intelligence by adapting the outputs to the user’s cognitive and emotional readiness, current environment, and state of awareness; offers scalable expertise, replicating expert-level diagnostic and recommendation capabilities without human intervention; continuously improves over time through reinforcement learning and user feedback loops, and achieves a holistic understanding of the user by integrating diverse data types into a coherent transformation strategy.
[00160] The system (100) further comprises the transformation implementation module (108) responsible for executing the personalized interventions generated by the inference and recommendation engine (107) and ensuring the interventions are effectively internalized by the user. The transformation implementation module (108) delivers the prescribed content and monitors user engagement, dynamically adapts its delivery strategy in real time, and tracks progress towards transformation goals to ensure measurable and lasting outcomes.
[00161] In an embodiment, the transformation implementation module (108) marks the transition point within the system (100) from analysis and planning to actual execution and behavioral reinforcement. The transformation implementation module (108) comprises a wide array of tools and techniques including, but not limited to, micro-learning content, behavioral nudges, habit formation mechanisms, real-time coaching simulations, a novel Solar Voice Model, and a suite of interactive and cognitive tools for personalized mental engagement. In an embodiment, the micro-learning content comprises short, focused lessons or insights structured for rapid consumption and optimal retention; the behavioral nudges are structures as subtle, non-coercive prompts that gently steer the user toward the desired behavioral outcomes; the habit formation tools rely on the repetition-based mechanisms to assist users in building and sustaining new behavioral patterns; the real-time coaching simulations offer the interactive scenarios wherein the users safely rehearse and refine their skills in dynamic, feedback-rich environments; and the Solar Voice Model provides an AI-driven voice delivery that leverages vocal modulation to improve emotional impact and subconscious receptivity, incorporates installations, metaphors, and other suggestive elements to foster long-term or even permanent behavioral shifts. In multiple embodiments, additional tools such as metaphor-driven storytelling, reorganization of unconscious priorities, immersive virtual reality (VR) simulations, reconditioning protocols, and personalized mental activities or games derived from the user’s unique internal patterns are employed either individually or in various combinations.
[00162] In an embodiment, the Solar Voice Model acts as a voice-based delivery mechanism leveraging AI-generated speech to deliver affirmations, interventions, or coaching narratives in a manner that resonates with the user's subconscious mind. The Solar Voice Model possesses the unique ability to shift attention fluidly between conscious and subconscious domains. The Solar Voice Model is architected to build rapport, deepen emotional resonance, and enhance the effectiveness of behavioral installations and further improves subconscious priority alignment, stimulates Autonomous Sensory Meridian Response (ASMR), enhances cross-contextual mapping, and enables generalized capability installation across multiple real-life scenarios. Additionally, the Solar Voice Model enriches the user's educational and knowledge frameworks, calibrated using Content Framing parameters, including tone, pitch, and vocal dynamics based on the real-time user responses.
[00163] In an embodiment, the key parameters of the Solar Voice Model include tone, pitch, speed, gender, vocal identity, and a frequency resonance mapping mechanism wherein the tone refers to the emotional quality of the AI voice, such as warm, authoritative, or soothing, each eliciting distinct psychological responses. For example, a calm, nurturing tone reduces anxiety and enhance openness, while a confident, assertive tone elevates motivation and focus. In an embodiment, pitch denotes the frequency range of the voice wherein the higher pitches convey energy or urgency, and the lower pitches suggest calmness and authority. The system (100) adjusts the pitch dynamically based on the user’s emotional state, lowering pitch during stress, and the intended effect of the intervention, raising pitch for energizing segments. In an embodiment, speed or tempo defines the speech delivery rate. Slower speech is employed to enhance comprehension and induce relaxation, while faster speech is used to stimulate engagement. The rate of speech delivery is modulated to align with the user’s cognitive load, the complexity of content, and the emotional tone of the interaction. In an embodiment, gender and vocal identity are customized based on user preference or experimental A/B testing to determine the vocal archetypes, such as mentor, friend, or coach that yield optimal engagement and retention. In another embodiment, frequency resonance mapping, also referred to as Frequency Spectrum Resonance Mapping, enables the system (100) to determine vocal frequencies over time that produce the most beneficial impact on the user. This determination is based on the physiological responses such as heart rate and EEG patterns; behavioral metrics such as engagement and completion rates; emotional markers including facial expression and vocal tone; and additional mappings such as Relativity Time Perception Mapping and Selective Attention Mapping that identifies the parts of the content require conscious attention, subconscious processing, or both. The identified optimal frequencies are subsequently used as reference tones for future interventions, thereby creating neurologically and emotionally synchronized experiences that enhance trust, receptivity, and the likelihood of transformation. The Solar Voice Model further incorporates musical and vocal rhythms, tonal expressiveness, and melodic cues that act as stimuli for neural plasticity and potential brain regeneration.
[00164] In an embodiment, adaptive content delivery, also referred to as target response-based adaptive content and stimulus delivery is enabled through continuous monitoring of user engagement using sensor feedback such as eye-tracking data, facial expression recognition, and physiological metrics like heart rate; interaction patterns such as pauses, replays, skips; and verbal or non-verbal cues such as vocal tone and posture. In an embodiment, based on the multimodal feedback, the system (100) dynamically adjusts several variables, the type of content such as switching from visual to auditory delivery; the delivery format such as Solar Voice Model versus textual content; the timing and frequency of interventions; and the nature of stimulus mechanisms. In an embodiment, on detection of cognitive fatigue, the system (100) transitions to a slower-paced Solar Voice Model delivery with simplified content. Conversely, on demonstration of high engagement, the system (100) increase the content complexity or introduce interactive elements. Furthermore, for transformation of a specific useful emotional state or cognitive intensity, the system (100) provides the required content and stimuli to elevate and sustain that state for effective leverage.
[00165] In an embodiment, the transformation implementation module (108) tracks the user progress along multiple transformation dimensions. These dimensions include behavioral metrics such as task completion rates, habit consistency, and engagement regularity; emotional parameters such as mood shifts, reductions in stress levels, and emotional stability; cognitive metrics such as enhanced focus, improved comprehension, and decision-making quality; skill-based performance such as mastery of targeted capabilities; and cross-mapping ability, which is the ability to take a well-formed arc and apply it across various areas of life. This data is continuously looped back into the user profile and context management module (102) for ongoing personalization, into the mentorship simulation and feedback module (110) for generating expert-level feedback, and into the inference and recommendation engine (107) for refining subsequent recommendations. Users receive real-time feedback through a range of interfaces including visual dashboards, verbal affirmations, and gamified progress indicators.
[00166] In an exemplary embodiment, to support the real-time voice synthesis and adaptive delivery, the transformation implementation module (108) utilizes a combination of hardware such as Edge AI processors such as NVIDIA Jetson or Google Coral for localized voice synthesis, high-performance GPUs such as NVIDIA RTX 4090 for deep learning inference, high-dynamic-range microphones and speakers for precise voice capture and output, and wearable physiological monitoring devices for heart rate or EEG data acquisition. On the software side, the transformation implementation module (108) incorporates text-to-speech (TTS) engines such as Tacotron 2, WaveNet, or FastSpeech; voice cloning models like SV2TTS, Vall-E, or Bark; emotion-aware TTS models trained to modulate voice parameters based on emotional context; and real-time feedback platforms such as TensorFlow Lite or ONNX Runtime suitable for edge-based deployment.
[00167] The transformation implementation module (108) receives transformation plans and intervention content from the inference and recommendation engine (107) and retrieves the user state data and preferences from the user profile and context management module (102). Further, the transformation implementation module (108) selects the appropriate intervention type and delivery format, synthesizes voice content using the Solar Voice Model, adjusts tone, pitch, speed, and frequency based on the user’s real-time state, and applies all relevant stimulus mapping techniques, and further delivers the synthesized content through user interfaces in audio, visual, or interactive formats, captures user responses and physiological feedback, and routes progress data back to the user profile and context management module (102) and the mentorship simulation and feedback module (110).
[00168] The transformation implementation module (108) enables deep personalization by customizing delivery mechanisms to align with the subconscious patterning and user-specific preferences wherein the emotional resonance is heightened through precise modulation of tone and pitch. Further, repetitive delivery using optimal frequency supports robust behavioral reinforcement and habit formation, and consistent and emotionally attuned voice delivery builds trust and rapport between the system (100) and the user. Finally, the system (100) enables scalable coaching, mentoring, training, and counseling by emulating human-like interventions without requiring continuous human supervision or intervention.
[00169] The system (100) further comprises a parallel processing and memory management module (109) that enables real-time, high-fidelity tracking and correlation of a vast number of verbal and non-verbal data streams during user interactions. The parallel processing and memory management module (109) overcomes the inherent cognitive and memory limitations of the human experts by leveraging advanced machine capabilities to monitor, store, and analyze more than 200 concurrent signals simultaneously, and ensures continuity of the transformation, supports contextual awareness across user sessions, and facilitates deep pattern recognition, thereby enabling the system (100) to deliver expert-level insights while maintaining a coherent and individualized transformation journey for each user.
[00170] In an embodiment, the parallel processing and memory management module (109) offers session memory management by maintaining a persistent memory for each user interactions across different sessions; stores historical data including but not limited to emotional states, physiological responses, question-and-answer sequences and outcomes of prior interventions; enables the system (100) to retain and utilize user-specific patterns, behavioral preferences, and developmental progress over extended timeframes.
[00171] The parallel processing and memory management module (109) further manages temporal pattern analysis by analyzing the evolution of user responses and multimodal signals over time and incongruence sets; detects trends, repetitive behaviors, and emotional cycles; and supports predictive modeling by identifying early indicators of stress, disengagement, or imminent breakthroughs. Further, a signal correlation engine correlates multiple simultaneous data streams such as electroencephalogram (EEG) signals, vocal tone, facial expressions, and heart rate data in real time; identifies co-occurring patterns across these modalities to infer deeper psychological or behavioral states of the user; and enhances diagnostic accuracy by validating a user’s verbal responses through physiological and behavioral cues. Further, a contextual continuity functionality ensures the recording of full contextual awareness of the user’s environment, such as location, time of day, and device usage, throughout the interaction lifecycle thereby enabling seamless transitions between different devices or sessions without any degradation in personalization or contextual understanding. Further, a memory optimization and prioritization function utilizes intelligent caching mechanisms and data prioritization strategies to retain the most relevant information and employs decay functions to phase out outdated or less useful data while preserving critical long-term behavioral and emotional patterns.
[00172] In an embodiment, the parallel processing and memory management module (109) supports real-time signal tracking of over 200 concurrent data streams, encompassing biometric, behavioral, and environmental data sources; enables high-frequency sampling, such as EEG signals at 256 Hz and heart rate data at 1 Hz, without any performance degradation; constructs and maintains long-term memory graphs linking the user’s emotional and cognitive states, contextual events, and transformation outcomes over time. Furthermore, the parallel processing and memory management module (109) supports multimodal synchronization, aligning data from various modalities such as audio, video, and sensor-based streams using timestamp-based synchronization protocols to ensure temporal coherence.
[00173] In an embodiment, the parallel processing and memory management module (109) receives inputs from the sensor integration and data acquisition module (103), the multimodal signal processing module (104), and the user profile and context management module (102), and performs real-time signal correlation, memory updates, and pattern recognition operations. The parallel processing and memory management module (109) further applies temporal analytics to detect underlying trends, anomalies, and transformation-relevant signals wherein the enriched, time-aware, and temporally indexed output data is supplied to the inference and recommendation engine (107), the mentorship simulation and feedback module (110), the intelligent questioning and diagnostic engine (105), and the skill transformation engine (111) for enhanced processing and decision-making.
[00174] The parallel processing and memory management module (109) enables expert-level continuity, simulating the memory depth and pattern recognition capabilities of a seasoned human mentor; delivers scalable intelligence, allowing the system (100) to manage multiple users simultaneously without compromising on the depth of personalization; enhances diagnostic accuracy by validating inferences across multimodal data streams and long-term timelines; and supports adaptive learning, continuously refining and evolving user models based on both historical and real-time input.
[00175] The system (100) further comprises a mentorship simulation and feedback module (110) that replicates the nuanced guidance, intuitive understanding, and corrective feedback typically offered by highly experienced human mentors. The mentorship simulation and feedback module (110) serves the end-users undergoing personal or skill-based transformation; and optionally, professionals-in-training, also referred to as mentor user, who develops diagnostic, coaching, or facilitation capabilities to operates the system (100) or override the emulation model The mentorship simulation and feedback module (110) ensures the users expert-level validation; detection of blind spots; and reinforcement of continuous learning without requiring real-time human intervention.
[00176] In an embodiment, the mentorship simulation and feedback module (110) emulate the decision-making patterns of seasoned mentors using artificial intelligence models that are trained on expert behavior datasets, feedback loop structures, and codified transformation protocols; utilizes historical data, domain-specific heuristics, and situational context cues to simulate interpretation of a user’s situation by an expert mentor or respond to their cognitive and emotional state.
[00177] In an embodiment, an insight validation engine cross-verifies the recommendations and diagnostic outputs generated by the inference and recommendation engine (107), flags inconsistencies, overgeneralizations, or overlooked signals to ensure the outputs align with best practices and expert reasoning patterns. Further, a blind spot detection function identifies the areas potentially neglected either by the system (100) or the user; employs anomaly detection algorithms and pattern deviation analysis to surface subtle yet critical insights; and assists users and trainees in recognizing unconscious biases, emotional blockages, or unarticulated cognitive needs. Further, a feedback generation function provides real-time, context-sensitive responses to both users and the professionals. In an embodiment, the feedback comprises clarifications or rephrasings of the diagnostic or assessment questions, suggestions for deeper introspective exploration, and affirmations or corrective responses based on the user's multimodal signals and inputs. The feedback is delivered through text, visual cues, or the Solar Voice Model for enhanced emotional resonance. Further, a learning reinforcement functionality for profesionals tracks the performance of professionals-in-training; provides expert-level commentary on their diagnostic accuracy, questioning methodologies, and overall effectiveness in delivering interventions; recommends targeted learning modules or simulated exercises to close the skill gaps; and allows the mentor user to override automated functionality on requirement of manual configuration or expert input. Further, a calibration and confidence scoring function assigns confidence levels to the system-generated insights and recommendations; highlights the areas where human review or supplemental data input is warranted; and supports transparency and trust in the automated mentorship, including scalable coaching, mentoring, training, counselling, and capability development.
[00178] The mentorship simulation and feedback module (110) integrates multiple AI and computational capabilities. In an embodiment, an expert behavior modeling is achieved through training on annotated datasets comprising expert decisions, detailed feedback trajectories, and transformation journey; a causal inference models, also referred to as interconnected causal inference models, understand the underlying systemic influences or root causes of user challenges and corresponding mentor interventions; anomaly detection algorithms identify deviations from the normative behavioral or emotional patterns to highlight potential blind spots; and reinforcement learning algorithms continuously optimize the quality of the system's (100) feedback by iteratively learning from user engagement and outcome data.
[00179] In an embodiment, the mentorship simulation and feedback module (110) receives inferences and recommendations from the inference and recommendation engine (107), skill development plans from the skill transformation engine (111), domain knowledge and transformation protocols from the expert knowledge base and capability map (106), real-time user data from the user profile and context management module (102) and the multimodal signal processing module (104); validates the insights, detects blind spots, generates expert-level feedback, updates training profiles of professional users, checks ecological alignment, and calculates viability of the transformation path; and transmits the feedback and actionable guidance to the user interface module (101), forwards corrections or enhancements to the inference and recommendation engine (107), and transmits the learning recommendations to the skill transformation engine (111).
[00180] In an embodiment, the mentorship simulation and feedback module (110) enables expertise delivery at scale, allowing the system (100) to provide mentorship-level support to numerous users simultaneously; fosters continuous improvement for both end-users and professional trainees by offering structured and intelligent feedback; functions as a layer of quality assurance by validating outputs and ensuring reliability of the system (100); and contributes to the blind spot elimination, also referred to as blind spot mitigation, by surfacing hidden insights that may otherwise remain unnoticed.
[00181] The system (100) further comprises a skill transformation engine (111) responsible for identifying, planning, and accelerating the development of specific skills required by an individual to achieve their personal or professional transformation goals. In an embodiment, the skill transformation engine (111) is further responsible for addressing personal and professional goals and for maximizing the potential capabilities of the individual in a significantly reduced timeframe, potentially achieving up to a 99 percent reduction, ensuring targeted, measurable, and time-efficient transformation. In an embodiment, the skill transformation engine (111) performs skill gap analysis by comparing the user’s current capabilities, as inferred from behavioral data, responses, and physiological signals, against a target capability model, and uses Artificial Intelligence to detect missing or underdeveloped skills across various domains, including emotional intelligence, strategic thinking, communication, leadership, physical health, and cognitive agility.
[00182] The skill transformation engine (111) further constructs a personalized learning path for each user by outlining the skills to be developed, the sequence of learning, the estimated time to mastery, and the associated milestones and checkpoints, based on the user’s learning style, emotional readiness, cognitive load, and environmental context. Furthermore, in an embodiment, the skill transformation engine (111) incorporates an adaptive content delivery mechanism, referred to as Target Response Based Adaptive Content and Stimulus Delivery, that dynamically adjusts the format, pacing, and complexity of interventions based on real-time engagement, emotional state, and progress velocity. The adaptive delivery utilizes various formats such as micro-learning modules, interactive simulations, Solar Voice Model-guided coaching, and habit-building exercises. Additionally, other formats are employed including metaphors, reorganizing unconscious priorities, virtual reality-based simulations, reconditioning, and personalized mental activities and mental games according to each individual's unique internal patterns.
[00183] In an embodiment, milestone tracking and time optimization are critical functionalities of the skill transformation engine (111) wherein each skill development journey is segmented into micro-milestones that represent progressive stages of mastery. These milestones include activities such as understanding a concept, applying it within a simulated environment, demonstrating the skill in real-world scenarios, and sustaining it over time. Each milestone is defined by a specific skill objective, an expected time frame for achievement, performance indicators to measure success, feedback loops for iterative refinement, and unconscious competency mapping to assess the deep-rooted learning.
[00184] In an embodiment, the system (100) reduces the time required to achieve each milestone through multiple techniques. These include real-time feedback from multimodal sensors such as EEG, facial expressions, and voice tone; adaptive questioning mechanisms that rapidly surface blockers and limitations; utilization of the Solar Voice Model to enhance subconscious learning and retention; and mentorship simulation to provide expert-level nudges and support without requiring human intervention.
[00185] In an embodiment, the tracking mechanism involves a variety of progress and time metrics. Progress is evaluated through the completion rate of modules, accuracy demonstrated in simulation tasks, emotional stability maintained during skill-oriented activities, and physiological indicators of confidence, such as reduced stress signals. Further, time metrics are captured, including the actual versus expected time to complete each milestone, generation of an Accelerated Time Compression (ATC) Report, analysis through Impact Charts with contrast, Evolution Mapping, and development of a Personalized Evolution Chart. Additional time-based data points include time spent in each learning mode such as audio, visual, or interactive along with time to first success and time to sustained mastery, as well as the overall time taken to achieve each of the recommended transformations. All these data streams are visualized in a dynamic dashboard accessible to both the user and the system (100) to monitor and reflect on progress.
[00186] In an embodiment, a continuous feedback loop enables dynamic adaptation of the learning pathway. The skill transformation engine (111) receives updated insights from the inference and recommendation engine (107), engagement tracking data from the transformation implementation module (108), and expert-level validation from the mentorship simulation and feedback module (110). Further, based on the inputs, the system (100) refines the learning path, adjusts the difficulty levels of milestones, recalculates estimated completion times, and recommends strategies for reinforcement or acceleration.
[00187] In an embodiment, the skill transformation engine (111) receives multiple input streams, including transformation goals, user preferences, and historical data from the user profile; real-time signals reflecting the emotional, cognitive, and physiological states of the user; and outputs from the inference and recommendation engine (107), such as identified root causes and readiness levels. Based on this integrated input, the skill transformation engine (111) detects skill gaps, generates individualized transformation roadmaps, plans adaptive content delivery, and simulates optimal learning trajectories. This enables the generation of a personalized skill development roadmap with milestone-specific timelines, real-time progress tracking, and feedback loops that remain in continuous synchronization with other system modules to maintain overall systemic coherence.
[00188] In an embodiment, the impact of these capabilities is substantial. The system (100) enables accelerated learning, allowing users to achieve their transformation goals in a fraction of the traditional timeframe; precision development is supported by targeting only the required capabilities, thereby eliminating time spent on generic training; and scalable mentorship is achieved through the simulation of expert-level guidance, making high-quality transformation support available to every user at any time and in any location.
[00189] In an embodiment, an advanced capability of the skill transformation engine (111) integrates with the Accelerated Time Compression (ATC) models, which are specialized frameworks to fast-track personal evolution and transformation across multiple domains of life. These ATC models enable the system (100) to reduce the time traditionally required for meaningful and measurable transformation potentially by up to 99% when compared with the conventional experiential learning and development cycles.
[00190] In an embodiment, the system (100) supports multiple ATC models, each of which is tailored to produce specific user outcomes aligned with targeted transformation goals. These models are curated and maintained by a set of specialist modules, which include general specialists for broad-spectrum transformation efforts and domain specialists who focus on specific areas such as physical health, emotional intelligence, relationships, or professional growth including career development. Each ATC model further comprises a set of personalized capabilities that are identified for the user based on diagnostic insights and mapped directly to the structured implementation procedures. The formulation of these capabilities emerges from an initial diagnostic process, which is subsequently transitioned into the implementation phase. The structure of each ATC model is composed of specific core elements. These include the beliefs to be recalibrated, emotional response capabilities in specific contexts, unconscious priority shifts, resolution of internal conflicts, and transformation sequences and timeframes. The elements are grouped into evolutionary cycles, within each cycle, the system (100) facilitates capability development and emotional maturity that would traditionally take years to achieve or in other words conventionally would require several years to emerge or might, in some cases, never manifest at all.
[00191] In an embodiment, diagnostics acts as a foundational and prerequisite step that precedes the creation and application of ATC. The diagnostic involves opening up a comprehensive cognitive and emotional map of the user, encompassing an understanding of the individual's backstory, current worldview, and aspirations for the future. Through the map, the system (100) identifies both resourceful traits and unresourceful patterns, as well as existing capabilities. Furthermore, the user’s surrounding ecosystem is analyzed to assess the readiness and receptivity for undergoing transformation. The system (100) employs trajectory simulation techniques to make predictive assessments about what is realistically possible for the individual, based on the grounded evaluation of the user’s current capabilities, emotional readiness, and the contextual factors influencing their developmental path.
[00192] In an embodiment, the ATC process is structured as a strategic cycle of evolution, consisting of three core steps. Step A is the Diagnostic Phase, during which the ATC list is generated by aggregating multimodal data inputs and conducting a thorough contextual analysis. Step B involves the Implementation Phase, where the system (100) delivers carefully aligned interventions in accordance with the ATC model’s sequencing. Step C is the Review Phase, which serves not merely as a final validation stage but as an active recalibration mechanism. This review helps the user arrive at conclusions and decisions that are far more refined and strategically sound than those they would have reached in isolation, enabled by the system’s expert-level inference and feedback processes.
[00193] In an embodiment, a distinction is made between personal transformation and personal evolution. Personal transformation refers to a targeted adjustment within a specific behavioral or emotional domain such as overcoming persistent anger, resolving deep-seated sadness, or eliminating resistance to learning which allows the individual to unlock new capabilities and overcome localized obstacles. In contrast, personal evolution is a generative and holistic process that affects all aspects of life simultaneously, including one’s professional work, interpersonal relationships, learning trajectory, and emotional maturity. Whereas such comprehensive evolution typically occurs for most individuals once in a decade or longer, the ATC approach is designed to compress this extended timeline into just a few months, enabling accelerated life progression without compromising on depth or sustainability.
[00194] In an embodiment, the review mechanism within the ATC cycle is elevated to the level of a strategic intervention. It not only validates the success of previously delivered interventions but also recalibrates the individual’s entire developmental trajectory. The review phase allows for continuous evolution by helping the user gain insights and directions that are aligned with their highest potential, again supported by the system’s (100) robust feedback loops and intelligent inference engines. The design of the ATC process, therefore, is not just about expediting transformation, but also about ensuring that such transformation is deeply ecological, meaningfully integrated, and strategically aligned with the user’s real potential and life context.
[00195] In an embodiment, the ATC system (100) is built to enable real-time transformation that is in complete harmony with the user’s evolving potential. It supports multi-domain impact by extending influence across a user's personal and professional spheres, including family life, career progression, business growth, and health optimization. The system (100) facilitates the creation of ecological outcomes that are both sustainable and deeply integrated with the individual’s internal and external reality. Each strategic cycle of evolution encapsulates multiple nested layers of capability development that not only address current gaps but also unlock new realms of functioning. In this manner, the embedded ATC models and reports within the skill transformation engine (111) allow the overall system (100) to achieve transformation processes that are not only fast and highly personalized but also meaningful, generative, and capable of unleashing untapped human potential.
[00196] In an embodiment, the Impact Chart with Contrast is a critical analytical and decision-support mechanism integrated within the skill transformation engine (111). This component enables the system (100) to quantify and visually represent the value of transformation achieved by the user through system-guided interventions, as compared to conventional developmental timelines and outcome probabilities based on global population data. The chart is specifically designed to contrast accelerated transformation with normative baselines, allowing both the system (100) and the user to measure tangible progress and intervention impact.
[00197] In an embodiment, the Impact Chart with Contrast is structured across three distinct levels of transformation. The first level is the Adjustment Level, which corresponds to immediate state-level changes and the development of specific capabilities, such as emotional regulation. The second level is the Consequence Level, which reflects life experience outcomes, including instances like repairing broken relationships. The third level is the Evolution Level, which captures deep-seated identity and personality shifts, such as transitioning from a withdrawn persona to becoming a joyful, expressive, and socially engaged individual.
[00198] In an embodiment, the primary purpose of the Impact Chart with Contrast is twofold. From the system’s (100) perspective, it enables the identification of which Target Transformation Trajectory (T2) and associated capability sets are delivering the highest Accelerated Time Compression (ATC) value. It empowers the system (100) to maximize opportunities that are immediate and well-formed, resulting in quick, impactful wins that are also sustainable in the long term. The chart further supports prioritization of interventions that successfully convert highly improbable outcomes into probable ones. By continuously analyzing real-time and historical data, the system (100) selects the most optimal T2 path for the user and refines its overall transformation strategy using robust, evidence-based feedback mechanisms.
[00199] In an embodiment, from the user’s perspective, the Impact Chart with Contrast provides a clear, visual, and comparative understanding of their transformation journey, and illustrates the outcomes that have been achieved; the typical duration such outcomes require within the general population; and the specific capabilities and system-driven interventions that enabled those results. This chart communicates that the achieved transformation is not speculative or aspirational but is the product of precise, measurable, and replicable processes. Furthermore, it fosters systemic intelligence in the user by helping them accurately attribute life outcomes to underlying capabilities. This, in turn, supports more informed long-term decision-making and promotes systemic thinking across various dimensions of life.
[00200] In an embodiment, the Adjustment-Level Impact Chart with Contrast provides detailed insights into micro-level behavioral shifts. For example, a user who previously exhibited anger in response to specific triggers now demonstrates calmness in identical scenarios. The chart compares how long it typically takes individuals to overcome anger without guided intervention, the statistical probability of success in such situations across the population, and the actual time taken for the user to achieve this behavioral change with the assistance of the system (100). Additional metrics are presented alongside, including improvements in health, productivity, and emotional stability, thereby quantifying the secondary benefits of the adjustment.
[00201] In an embodiment, the Consequence-Level Impact Chart with Contrast documents the transformation of tangible life experiences. For instance, the system (100) may facilitate the reconciliation of a strained father-daughter relationship. The chart illustrates the average timeframe required for similar reconciliations in the general population, the likelihood of achieving such outcomes without structured support, and the emotional and psychological shifts recorded during the system-mediated resolution process. Supplementary metrics such as emotional well-being, family harmony, and overall social functioning are also captured, providing a holistic view of the intervention’s impact.
[00202] In an embodiment, the Evolution-Level Impact Chart with Contrast addresses deeper, identity-level transformations. For example, a user who was formerly withdrawn and stern becomes socially expressive, joyful, and engaged with the world around them. The chart compares the normative timeframe for such fundamental shifts in personality, often ranging from five to ten years or, in some cases, never occurring at all with the timeline achieved through the system’s (100) structured interventions. It also outlines the capability stack responsible for enabling this transformation. Additional evaluative dimensions include enhancements in self-image, energy levels, life satisfaction, and ripple effects across various domains of the user’s life, all of which collectively validate the depth and generative power of the transformation.
[00203] In an embodiment, the system (100) leverages a comprehensive combination of global datasets and real-time user signals to generate, evaluate, and present the Impact Chart with Contrast. The data computation is structured around multiple sources and analytic models that together enable the system (100) to assess the comparative effectiveness and depth of user transformations. The first category of data includes world data and statistical models. In an embodiment, the system (100) references global psychological, behavioral, and sociological datasets to establish baseline expectations for transformation timelines and the statistical probability of success across various life domains. These benchmarks are used to contextualize the user's progress within a global reference framework.
[00204] In an embodiment, the system (100) further integrates system-generated data, which comprises real-time user signals, multimodal sensor outputs, and recorded transformation milestones. These inputs are dynamically collected and used to compute the user’s actual outcomes. To derive the differential impact, the system (100) performs contrast computation. In an embodiment, this involves calculating the delta or measurable difference between conventional transformation outcomes and those achieved through system-guided processes. The delta is computed across four critical dimensions: the timeframe required to achieve a specific transformation, the probability of success based on global benchmarks, the quality of the outcome achieved, and the ripple effects of the transformation across multiple life domains.
[00205] In an embodiment, to ensure the authenticity and depth of transformation, the system (100) further employs a Congruence Vector Analysis. This analysis fuses multimodal signals, including emotional, cognitive, and physiological indicators, into a unified vector representation. The congruence vector quantifies the alignment between these modalities, serving as a validation metric for the genuineness and internal coherence of the user’s transformational progress.
[00206] In an embodiment, the system (100) provides multiple benefits to the user by integrating the data sources and computational processes. First, the system (100) offers clarity and motivation by allowing users to gain a clear understanding of what they have achieved, how rare or statistically difficult these outcomes are within the general population, and what specific system (100) features or capabilities enabled the progress. This enhances user motivation and deepens their commitment to the transformation process. Second, it supports the avoidance of wishful thinking by preventing users from falsely assuming that high-quality outcomes are easily replicable without deliberate effort or structured support. The chart anchors success in precise system (100) capabilities and interventions. Third, the system (100) promotes capability awareness. Users are encouraged to value the specific capabilities they have developed, even if those were acquired in a short duration. This prevents underestimation of critical skills and fosters continued personal growth. Fourth, it enhances decision-making intelligence. By establishing a transparent link between life situations, transformation outcomes, and the underlying capabilities that produced them, the system (100) empowers users to make more informed and higher-quality decisions in future contexts.
[00207] In an embodiment, several system (100) modules collaboratively support the generation and operational integration of the Impact Chart with Contrast. The skill transformation engine (111) is responsible for generating the chart and embedding it within the user’s personalized transformation roadmap. The inference and recommendation engine (107) performs the computational contrast analysis using techniques such as trajectory simulation, causal inference, and pattern recognition. The expert knowledge base and capability map (106) contributes to the process by supplying statistical baselines, validated transformation protocols, and capability benchmarks. The user profile and context management module (102) manages and updates transformation histories, emotional state trends, and relevant contextual metadata for each user. Finally, the parallel processing and memory management module (109) supports the overall process by tracking long-term patterns in user signals and validating the authenticity and consistency of the transformation milestones recorded over time.
[00208] In an embodiment, the Evolution Mapping capability within the skill transformation engine (111) captures, analyze, and present the trajectory of a user’s transformation over time. This capability enables the system (100) to monitor the acquisition of specific skills and profound, identity-level changes across emotional, physiological, behavioral, and cognitive dimensions. Such mapping is crucial for validating the authenticity of transformation, deepening the user's self-awareness, and enabling accurate bio-feedback and feedforward control mechanisms.
[00209] In an embodiment, the functional operation of Evolution Mapping involves real-time logging and comparative analysis of a wide spectrum of user parameters. These parameters include sensory-specific inputs such as visual, auditory, and kinesthetic cues; physiological and biochemical signals such as heart rate, electroencephalogram (EEG), skin conductance, facial blood flow, and muscle tension; cognitive and emotional states such as thinking patterns, beliefs, desires, dreams, top challenges, and transformation goals; and behavioral and contextual data encompassing life situations, social interactions, and environmental context. In an embodiment, the collection of such data is facilitated through guided probes and questionnaires that target visual, auditory, and kinesthetic modalities, as well as through video interviews and multimodal interactions. Sensor-based monitoring is employed during and after intervention sessions to provide an uninterrupted stream of transformation-relevant data.
[00210] In an embodiment, the system (100) performs before-and-after comparisons and monitors progressive changes across both single and multiple user sessions to detect and validate transformation milestones. The system (100) analyzes three tiers of change. Adjustment-level changes refer to immediate shifts in emotional responses, such as transitioning from anger to calmness. Consequence-level changes reflect tangible life outcomes, such as improvements in relationships or restoration of communication. Evolution-level changes involve deeper transitions in personality, identity, and worldview, for example, evolving from a state of sadness to joy or from social withdrawal to active engagement.
[00211] In an embodiment, the system (100) further analyzes changes in the user's physical appearance and facial expressions, physiological and biochemical markers, emotional responses to identified triggers, and the emergence of new desires, capabilities, and life goals. These parameters are processed and synthesized to construct a longitudinal view of personal evolution.
[00212] In an embodiment, Evolution Mapping incorporates a dual presentation and feedback mechanism. The first mode, referred to as Raw Data Presentation, displays unfiltered, real-time evidence of the user’s transformation. This includes before-and-after snapshots of thinking patterns, emotional states, and life circumstances. The second mode, referred to as the Impact Chart with Contrast, is presented subsequently and provides a comparative analysis of the user’s transformation against statistical baselines derived from global datasets. This dual approach enhances the user’s intuition regarding their future developmental trajectory, improves the accuracy of self-feedback by anchoring perception in real-time evidence, and prevents users from misattributing success to randomness or irrelevant factors.
[00213] The evolution mapping addresses a fundamental human limitation: the inability to accurately recognize and appreciate rapid, effortless personal transformation. Traditional self-awareness mechanisms depend on gradual changes and subjective memory, which are often incomplete or distorted. The Evolution Mapping function mitigates this limitation by capturing transformation events as they occur and embedding timestamped visual and physiological contrasts that bypass cognitive biases. It trains the unconscious mind to focus on effective strategies by reinforcing behavior patterns and decisions that demonstrably work. For instance, a user who believes they have not undergone any meaningful change may experience a powerful realization when shown comparative videos highlighting shifts in their demeanor, voice tone, and emotional state over a span of weeks, thereby reinforcing their belief in continued transformation.
[00214] In an embodiment, the implementation of Evolution Mapping involves multiple system (100) modules. The sensor integration and data acquisition module (103) captures continuous multimodal signals across multiple sessions. The multimodal signal processing module (104) interprets these signals into structured emotional, cognitive, and physiological vectors. The user profile and context management module (102) stores both historical and real-time data, enabling robust longitudinal comparisons. The skill transformation engine (111) generates evolution maps and integrates them into the user’s broader transformation roadmap. The inference and recommendation engine (107) synthesizes this data to identify significant milestones and confirm the occurrence of genuine evolution. The parallel processing and memory management module (109) maintains continuity by tracking evolving signal patterns across extended periods. The mentorship simulation and feedback module (110) provides expert-level validation and reflective guidance based on the mapped evolution.
[00215] In an embodiment, this comprehensive integration enhances self-awareness by providing concrete evidence of transformation, improves decision-making by illustrating the correlation between capabilities and life outcomes, and enables both conscious and unconscious learning through accurate feedback loops. Furthermore, by allowing users to visually and objectively track their progress, it significantly boosts motivation and confidence, reinforcing their belief in the plausibility and sustainability of future success.
[00216] In an embodiment, the skill transformation engine (111) incorporates a sophisticated reverse engineering framework for constructing a Personalized Evolution Chart specific to each user. Unlike a static representation, the Personalized Evolution Chart is a dynamically generated, evidence-based simulation that models the user’s transformation journey. This simulation begins from the user’s current state, defined as T0, projects their natural trajectory labelled T1, and outlines one or more optimized future states denoted as T2 and beyond. In an embodiment, the system (100) utilizes this framework to identify the precise capability set required to transition the user from their current trajectory to an improved and ecologically sustainable future pathway.
[00217] In an embodiment, the functional operation of the reverse engineering framework involves working backward from a desired future state to determine multiple diagnostic and strategic parameters. The system (100) assesses what must be true for the user to be situated in their current state (T0), projects where they are likely to arrive if they continue on their current trajectory (T1), determines the additional capabilities, mindset adaptations, and contextual shifts required to achieve a better future outcome (T2), and evaluates the systemic consequences and ecological impacts that would result from such transformation. This process is inherently iterative, multidimensional, and personalized. It synthesizes multimodal data streams, historical transformation patterns, context-driven leverage points, and statistical modeling to simulate and validate various transformation pathways.
[00218] In an embodiment, the system (100) models temporal states and simulation logic in the following structure. The T0 phase involves a comprehensive analysis of the user’s current reality, derived from multimodal inputs and contextual metadata. For instance, the system (100) may analyze a 45-year-old individual earning ?35 lakhs, who is divorced and exhibits particular emotional and behavioral tendencies. The system (100) computes what internal configurations such as capability deficits, belief structures, emotional responses, and environmental conditions must exist for the person to be at this juncture, and projects the natural evolution of this configuration, should no meaningful intervention occur.
[00219] In the T1 phase, the system (100) simulates the projected trajectory of the user over a period of two to three years based on current capabilities, emotional responses, behavioral patterns, and the surrounding ecosystem, including factors like family dynamics, career trajectory, and health conditions; and predicts where the user is likely to land, who they will become, and how their relationships, career, and health might evolve. This predictive simulation provides the necessary contrast to highlight the gap between current circumstances and the desired future state.
[00220] The T2 phase involves formulating hypotheses of desirable future states, each representing an improved life configuration. These T2 hypotheses are validated through two methodological approaches. The first approach, referred to as capability-based simulation, involves reverse engineering the required capability set to achieve each T2 state, simulating the impact of acquiring such capabilities, and validating the ecological viability of each outcome to ensure no harm or imbalance. The second approach involves statistical sampling from the expert knowledge base and capability map (106), wherein the system (100) utilizes global transformation data and expert-validated models to identify feasible capability sets, simulate their projected impact, and determine the most effective and ecologically balanced T2 option.
[00221] Upon selection of the optimal T2, the system (100) constructs a Personalized Evolution Chart mapping the user’s progression from T0 to T1 against the optimized T0 to T2 path, which is quicker and more beneficial. The system (100) further derives an ATC (Accelerated Time Compression) prescription, a prioritized list of capabilities to be developed in a significantly shortened timeframe. Furthermore, the system (100) identifies leverage contexts where these capabilities can be more naturally developed. For example, if the user lacks creativity in business problem-solving, the system (100) may recommend developing creative skills in a personal context such as playing with children and subsequently transferring this competency into professional scenarios. This enables the system (100) to plant the seeds for extended transformation, supporting future trajectories such as T2 to T3, T3 to T4, and eventually to a generalized state Tx. In an embodiment, the system (100) embeds capability sets that address the immediate shift from T0 to T2 and lay the foundational layers for long-term growth across multiple life domains. Many of the identified capabilities are multi-layered and designed to support trajectory accelerations that may not occur within a single lifetime.
[00222] In an embodiment, the system (100) performs an integrated life outcome and ecology check for each capability in the prescribed set. This involves assessing the benefits of developing the capability, evaluating potential harms or unintended consequences, analyzing cross-domain effects on relationships, career, and health, and ensuring that the transformation remains ecologically viable and sustainable, further ensuring the transformation roadmap is technically effective, holistic, balanced, and conducive to long-term integration into the user's life ecosystem.
[00223] In an embodiment, several key innovations are embedded within this framework. The system (100) leverages predictive modeling of life trajectories to forecast where a user may be within two to three years under current conditions, and where they could be with the aid of optimal interventions. The system (100) further performs reverse engineering based on pre-validated transformation outcomes by using historical data to derive successful capability sets for application to new users. Furthermore, the system (100) supports poly-contextual capability development by recognizing that certain competencies can be developed in one life domain and successfully transferred to another, thereby improving efficiency and personalization. Ecological validation and systemic impact analysis ensure that interventions do not result in downstream harm or imbalance. Lastly, the Personalized Evolution Chart and ATC Prescription provide the user with a clear, structured visual roadmap, detailing transformation milestones, timelines, and required capabilities.
[00224] In an embodiment, multiple system (100) modules are involved in implementing this framework. The mentorship simulation and feedback module (110) and the skill transformation engine (111) serve as the core modules responsible for capability mapping, ATC prescription, and evolution chart generation. The inference and recommendation engine (107) conducts trajectory simulation, hypothesis validation, and capability set derivation. The expert knowledge base and capability map (106) supplies statistical models, validated transformation protocols, and ecological impact assessments. The user profile and context management module (102) provides access to historical transformation data, contextual metadata, and user-specific transformation goals. The parallel processing and memory management module (109) maintains continuity by tracking evolving signal patterns and transformation history, enabling accurate reverse engineering of user trajectories.
[00225] The system (100) comprises a security, privacy and compliance module (112) that ensures all user data, particularly sensitive and Personally Identifiable Information (PII), is handled with the highest standards of security, privacy, and regulatory compliance. The security, privacy and compliance module (112) safeguards the system (100) against unauthorized access, data breaches, and potential misuse, while simultaneously empowering users with full control over their personal data. The invention envisages various embodiments involving different combinations of the following technologies and methodologies, without limitation.
[00226] In an embodiment, the security, privacy and compliance module (112) performs a set of core functions. The first function is end-to-end encryption, that comprises multiple subcomponents. All data transmitted between client devices, sensors, and the cloud infrastructure is encrypted in transit using Transport Layer Security (TLS) 1.3 or higher. In parallel, data at rest, stored within databases or file systems, is protected using Advanced Encryption Standard (AES)-256 encryption protocols. Furthermore, key management is performed through secure Hardware Security Modules (HSMs), with encryption keys being rotated at periodic intervals to ensure cryptographic resilience. In the second function, Role-Based Access Control (RBAC), accesses to data and system functionalities is strictly governed based on predefined user roles, such as end-user, system administrator, developer, or auditor. The least privilege principle is enforced ensuring that both users and services have access only to what is strictly necessary. Moreover, access policies are dynamically updated based on contextual parameters, such as user location, device identity, and time of access.
[00227] In an embodiment, the security, privacy and compliance module (112) supports consent management mechanisms. Users are prompted for explicit, informed consent prior to the initiation of any data collection procedures. Such consent is granular in nature, allowing users to selectively approve the collection of different types of data including physiological, location, and voice data. All consent logs are immutable and securely stored, ensuring auditability and accountability.
[00228] In an embodiment, the anonymization and pseudonymization functions are employed to protect PII before the data is used for analytics, training, or benchmarking. These techniques include tokenization of identifiers, differential privacy for generating aggregated insights, and data masking applied to sensitive data fields.
[00229] In an embodiment, the audit logging function ensures that every instance of access, modification, or transmission of user data is recorded. Each log entry contains a timestamp, a pseudonymized user identifier, the specific action performed, and the module accessed. These logs are stored in tamper-proof repositories and continuously monitored for anomalies.
[00230] In an embodiment, the security, privacy and compliance module (112) enforces data minimization principles. Only the minimum required data is collected and retained. Automated data retention policies ensure that obsolete or unnecessary data is deleted or archived after a predefined time period.
[00231] In an embodiment, the user rights management function grants users direct control over their data through a secure User Data Portal. Users can view all data collected about them, request an export of their data in machine-readable format, revoke previously granted consent at any time, and request permanent deletion of their data under the Right to be Forgotten.
[00232] The system (100) complies with one or more of the regulatory frameworks, either individually or in combination thereof such as the General Data Protection Regulation (GDPR – EU), the Health Insurance Portability and Accountability Act (HIPAA – US), the California Consumer Privacy Act (CCPA), ISO/IEC 27001 (Information Security Management), and SOC 2 Type II (Service Organization Control).
[00233] In an embodiment, for protecting PII and other user data such as name, email, and phone number is encrypted both at rest and in transit and is pseudonymized during internal processing; biometric data, such as EEG and Heart Rate Variability (HRV) is stored in isolated, encrypted containers that are governed by strict access control policies; location data is collected only with explicit user consent and is anonymized before being used for analytics; voice and video data are processed locally whenever possible and stored in encrypted form with time-limited retention; emotional and cognitive data, derived through system operations, is tagged as sensitive and processed exclusively within secure, sandboxed environments.
[00234] In an embodiment, the infrastructure and tools employed by the security, privacy and compliance module (112) include enterprise-grade cloud platforms such as Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure with Virtual Private Cloud (VPC) isolation, Identity and Access Management (IAM) policies, and tiered security groups. Encryption libraries such as OpenSSL, AWS Key Management Service (KMS), and Google Tink are employed. Monitoring and alerting is handled using Security Information and Event Management (SIEM) tools such as Splunk and Datadog, along with dedicated anomaly detection systems. Regular third-party audits and red team penetration testing exercises are conducted to identify vulnerabilities. Furthermore, a zero trust architecture is implemented that mandates continuous verification of identity, device integrity, and contextual parameters for every access request.
[00235] In an embodiment, the data flow within the module involves multiple stages. During data collection, all user inputs and sensor-acquired data are immediately encrypted. During data processing, only pseudonymized data is used by the inference and transformation subsystems. For data storage, encrypted databases with full access control and audit logging are employed. For data sharing, only anonymized data is released for research or benchmarking, contingent on user consent. During data deletion, users may trigger secure deletion workflows through the portal, with all actions logged and verified for integrity.
[00236] The security, privacy and compliance module (112) enhance trust and transparency, whereby users remain fully informed and empowered to control their data; ensures full compliance with global data protection standards and provides resilience against data breaches, unauthorized use, and insider threats. Finally, the security architecture is inherently scalable, supporting millions of users simultaneously without compromising on privacy or operational integrity.
[00237] The system (100) further comprises the cloud infrastructure and deployment module (113) that forms the foundational layer enabling the system (100) to operate at scale, across platforms, and with high availability. The cloud infrastructure and deployment module (113) is responsible for orchestrating the deployment of all other system (100) modules, managing inter-module communication, ensuring fault tolerance, and enabling real-time responsiveness through edge computing environments. The invention envisages several embodiments employing different combinations of the technologies and methodologies, without limitation.
[00238] In an embodiment, the architectural design of the system (100) is based on a distributed microservices architecture. Each module ranging from the user interface to the inference and recommendation engine (107) is containerized and deployed independently. This configuration facilitates modular updates, horizontal scaling, and fault isolation. The architecture supports both cloud-native and hybrid deployment models, thereby offering flexibility for diverse use cases such as enterprise deployments, healthcare-specific implementations, or deployments in remote and bandwidth-constrained environments.
[00239] In an embodiment, the hardware requirements comprise high-performance compute nodes powered by 64-bit processors such as Intel Xeon or AMD EPYC, with a baseline of 64 GB RAM, and 128 GB or more for modules performing artificial intelligence inference; and storage infrastructure is provisioned using NVMe SSDs to enable high-speed read/write operations for real-time data processing. For artificial intelligence workloads involving multimodal signal processing and inference, the system (100) utilizes graphical processing units (GPUs) such as NVIDIA A100, RTX 4090, or T4 to accelerate deep learning inference and ensure low-latency performance. Edge devices including, but not limited to, NVIDIA Jetson Xavier NX, Google Coral, and Raspberry Pi 5 equipped with Coral USB accelerators are deployed for localized computation. These edge devices enable the system to operate under constraints of limited connectivity or in contexts where data privacy mandates on-device inference.
[00240] In an embodiment, the software stack of the deployment environment is orchestrated using Kubernetes, which manages container lifecycle operations including scheduling, scaling, and health monitoring. Docker is used to containerize each system module, ensuring consistency across development, quality assurance, and production stages. Kubernetes applications are deployed using Helm, while Kustomize facilitates environment-specific configuration management. Infrastructure provisioning is achieved using infrastructure-as-code tools such as Terraform or Pulumi. The system (100) supports deployment on multiple major cloud platforms including Amazon Web Services (AWS) using Elastic Kubernetes Service (EKS), EC2, and S3; Google Cloud Platform (GCP) using Google Kubernetes Engine (GKE) and Compute Engine; and Microsoft Azure using Azure Kubernetes Service (AKS) and VM Scale Sets, thereby ensuring cloud-provider agnosticism and operational resilience.
[00241] In an embodiment, inter-service communication among the system modules is facilitated through RESTful Application Programming Interfaces (APIs) and gRPC for high-performance internal communication; WebSocket protocols are employed for real-time updates, particularly between the user interface module and backend services; a service mesh such as Istio or Linkerd is integrated to ensure secure, observable, and reliable inter-module communication ; message brokers such as Apache Kafka or RabbitMQ are deployed for asynchronous communication and event-driven workflows; Redis is employed for session management and caching to enhance system responsiveness and reduce computational latency.
[00242] In an embodiment, the security and compliance framework of the deployment infrastructure comprises end-to-end encryption using TLS 1.3 for data in transit and AES-256 encryption for data at rest; role-based access control (RBAC) is implemented using the cloud-native identity and access management (IAM) features of the respective cloud service provider; secrecy management and secure handling of credentials are enforced using tools such as HashiCorp Vault or AWS Secrets Manager. The infrastructure complies with global data protection standards, including but not limited to the General Data Protection Regulation (GDPR), the Health Insurance Portability and Accountability Act (HIPAA), and Service Organization Control 2 (SOC 2). Further, features such as audit logging, data anonymization, and consent management are natively integrated into the deployment framework to ensure ethical and lawful data governance.
[00243] In an embodiment, artificial intelligence and machine learning model hosting is achieved using technologies such as TensorFlow Serving, ONNX Runtime, or NVIDIA Triton Inference Server, deployed within Kubernetes environments. These inference servers are scaled using Kubernetes Event-Driven Autoscaling (KEDA) based on real-time workload demands. Model versioning and deployment lifecycle management are handled through model registries such as MLflow or Amazon SageMaker Model Registry.
[00244] In an embodiment, cross-platform access is enabled across web, mobile, desktop, and edge computing platforms. Web and mobile interfaces are implemented using frontend technologies such as React and Flutter, ensuring accessibility and responsive design. Desktop clients are deployed using Electron or Flutter Desktop. Lightweight user interfaces are deployed on edge devices and synchronized with the cloud infrastructure using low-latency protocols such as MQTT or WebRTC.
[00245] In an embodiment, firmware requirements for edge deployments are addressed by supporting platform-specific software environments. For example, NVIDIA Jetson devices operate on the JetPack Software Development Kit (SDK) based on Ubuntu, Coral devices use Mendel Linux with integrated Edge TPU runtime, and Raspberry Pi devices run on Raspberry Pi OS with Docker support. These environments are optimized to support secure, low-latency artificial intelligence inference at the edge.
[00246] In an embodiment, the deployment modes of the system (100) include multiple configurations such as cloud-native, hybrid, on-premise, and offline-first deployments. In a cloud-native mode, all the system modules are deployed in cloud environments such as AWS, GCP, or Azure. In a hybrid mode, core modules reside in the cloud while latency-sensitive modules such as signal processing and user interface are deployed at the edge. In an on-premise mode, the entire system (100) is hosted locally within regulated industries or data-sovereign environments. In an offline-first mode, the system (100) operates entirely on edge devices with periodic synchronization to the cloud, enabling functionality in remote or bandwidth-constrained settings.
[00247] The system (100) is implemented using a computing device such as a personal computer, laptop, tablet, smartphone, wearable device, or any other electronic device with embedded user interfaces and computational capabilities. The computing device may comprise a microprocessor, graphics processing unit (GPU), memory unit, power source, and various user interface components such as buttons, knobs, audio and gesture interfaces, and touch-based elements. The communication between modules is facilitated through wired or wireless networks, using short-range or long-range protocols, and employing interfaces such as serial, parallel, or hybrid configurations.
[00248] In an embodiment, the data flow within the system (100) begins with the user’s interaction, which is captured through the User Interface (UI) Module (101) and various sensors. This interaction may include spoken or typed responses, facial expressions, gestures, and physiological signals such as heart rate or skin conductance. These inputs are collected in real time through integrated devices like microphones, cameras, and wearable sensors. The UI module (101) ensures a seamless and intuitive experience for the user, while the Sensor Integration and Data Acquisition Module (103) captures the raw, multimodal data necessary for deeper analysis. Further, Once the data is collected, it is passed to the Multimodal Signal Processing Module (104), which interprets the user’s input across multiple dimensions. This module uses natural language processing (NLP), computer vision, and biometric signal analysis to decode the user’s emotional state, cognitive load, and behavioral patterns. For example, it can detect stress from voice pitch, engagement from facial expressions, or emotional tone from word choice. These interpretations are essential for understanding the user’s current state and tailoring the system’s responses accordingly. Further, the processed signals are forwarded to the Intelligent Questioning & Diagnostic Engine (105), which dynamically selects the most relevant questions from a vast knowledge base. This engine uses contextual inference and adaptive logic to determine which questions will yield the most insight into the user’s challenges. It avoids overwhelming the user by narrowing down from potentially millions of questions to a small, highly relevant subset. This ensures that the diagnostic process is both efficient and personalized, based on the user’s current state and historical profile. Furthermore, the responses to these questions, along with the interpreted signals and contextual data, are synthesized by the Inference and Recommendation Engine (107). This core module performs deep analysis to identify root causes of the user’s challenges, detect patterns, and generate actionable insights. It integrates data from multiple sources—user profile, signal processing, questioning engine, and knowledge base—to produce a comprehensive understanding of the user’s needs. The output is a set of personalized recommendations and transformation strategies tailored to the individual’s goals and context. Further, these insights are passed to the Skill Transformation Engine (111), which maps the identified gaps to specific capabilities and skills. This module constructs a personalized development roadmap, outlining the exact skills the user needs to build or enhance. It aligns these skills with the user’s life domains—such as health, career, or relationships—and ensures that the transformation plan is both targeted and holistic. The roadmap is adaptive, updating as the user progresses or as new data becomes available. Further, the Transformation Implementation Module (108) takes this roadmap and delivers the corresponding interventions. These may include micro-learning sessions, behavioral nudges, habit-building exercises, or real-time coaching simulations. The module also tracks the user’s engagement and progress, feeding this data back into the system to refine future interventions. This ensures that the transformation process is not only personalized but also responsive to the user’s evolving needs. In an embodiment, to ensure the quality and accuracy of the transformation process, the Mentorship Simulation and Feedback Module (110) acts as a virtual expert. It validates the recommendations made by the inference engine, identifies any blind spots, and provides expert-level feedback. This module simulates the role of a human mentor, offering guidance and refinement without requiring continuous human involvement. It also supports professional users in training by offering real-time feedback and learning reinforcement. Further, throughout this process, the Parallel Processing and Memory Management Module (109) maintains continuity and historical awareness. It tracks hundreds of parallel signals and maintains long-term memory across sessions, enabling the system (100) to recognize patterns over time and across contexts. This module ensures that the system (100) can build a deep, evolving understanding of the user, which is critical for delivering consistent and meaningful transformation.
[00249] In an embodiment, all modules continuously update the User Profile & Context Management Module (102), which serves as the central repository of user data. The user profile includes demographic information, behavioral history, emotional patterns, skill levels, and transformation goals. It is dynamically updated with every interaction, ensuring that the system’s (100) understanding of the user remains current and comprehensive.
[00250] In an embodiment, the system (100) operates on a robust Cloud Infrastructure and Deployment Module (113), which ensures seamless, secure, and scalable operation. This module manages inter-module communication, data synchronization, and real-time responsiveness across devices. It also integrates with the Security, Privacy and Compliance Module (112) to enforce data protection, access control, and regulatory compliance. Together, these infrastructure components ensure that the system is reliable, accessible, and ethically sound.
[00251] The term “module” or “engine” as used herein refers to hardware- or software-based logic, implemented using any suitable programming language including Java, C, or assembly. Modules or Engine may be embedded in firmware, realized as programmable gate arrays, or stored in any form of computer-readable medium. One or more software instructions in the modules or engine may be embedded in firmware, such as an EPROM. It will be appreciated that modules or engine may comprised connected logic units, such as gates and flip-flops, and may comprise programmable units, such as programmable gate arrays or processors. The modules described herein may be implemented as either software and/or hardware modules or engines and may be stored in any type of computer-readable medium or other computer storage device. The described modules, including the user interface module (101), user profile and context management module (102), sensor integration and data acquisition module (103), multimodal signal processing module (104), intelligent questioning and diagnostic engine (105), expert knowledge base and capability map (106), inference and recommendation engine (107), and parallel processing and memory management module (109), interact with one another directly or through the cloud infrastructure and deployment module (113).
[00252] Exemplary relevant content presented to the user may include audio, graphics, video, animation, interactivity features, surveys, polls, URLs, or embedded media players to render diagnostic and transformation-oriented media. While specific operations have been attributed to particular modules, the invention anticipates that such operations may be executed by other modules, devices, or distributed systems in alternate embodiments.
[00253] Various modifications to the disclosed embodiments are apparent to those skilled in the art. The principles disclosed herein may be applied to alternate configurations without departing from the scope of the invention. Accordingly, the invention encompasses all alternatives, modifications, and variations consistent with the novel and inventive features disclosed in the present specification.
[00254] Figure 2 illustrates a flowchart of a method for implementation of targeted and ecological permanent transformations, in accordance with an embodiment of the invention.
[00255] In an embodiment, the method (200) is initiated by preparing one or more users through capability priming and state calibration by aligning one or more internal and external factors for initiating a transformation process in step (201).
[00256] This step (201) involves a multi-layered process that includes education, state elicitation, physiological calibration, and readiness assessment. The method guides the user through a series of interactive experiences designed to educate and orient the user about the transformation process; prime the user’s mental and emotional state for optimal engagement; elicit and record baseline physiological and behavioral signals; interrupt suboptimal states and induce optimal ones; and construct a dynamic capability map based on the user’s current life context and performance benchmarks.
[00257] The step (201) involves multiple system modules such as the User Interface (UI) Module to deliver interactive onboarding, educational content, and guided reflections; present targeted questions and exercises for state elicitation; and to capture verbal, gestural, and haptic responses. Further, the sensor integration and data acquisition module collects real-time physiological data such as Heart rate variability, EEG brainwave activity, Skin conductance, Facial micro-expressions, Voice tone and pitch, Breathing patterns and Interfaces with wearables and environmental sensors. Furthermore, the multimodal signal processing module analyzes multimodal inputs to infer the emotional tone, cognitive load, stress levels, Engagement and readiness, and uses AI models for emotion recognition, Natural Language Processing (NLP), and signal fusion. Moreover, the user profile and context management module stores and updates the demographic data, historical interactions, emotional and physiological trends, transformation goals of the user and maintains session continuity and personalization context. Further, the skill transformation engine constructs a preliminary capability map, benchmarks the user’s current state against age- and role-specific standards, and identifies potential areas for growth and transformation. Furthermore, the inference and recommendation engine synthesizes all the inputs to assess readiness, determines whether the user is primed for the next step, and flags any cognitive or emotional blocks that may hinder the progress of the user.
[00258] The detailed process flow involves multiple stages. In the Priming and Orientation stage, the UI Module initiates a structured onboarding sequence, and the user is guided through educational content explaining the transformation journey. Interactive exercises are used to elicit initial responses and reflections. During the State Elicitation and Calibration stage, the system presents a small, highly targeted set of questions, while the Sensor Module captures real-time physiological and behavioral responses. The Signal Processing Module interprets these signals to detect suboptimal states (e.g., stress, confusion) and optimal states (e.g., clarity, calmness). The system may use techniques such as guided breathing or visualization to shift the user into an optimal state.
[00259] In the Memory and Congruence Scanning stage, the user is prompted to recall specific memories or experiences, which are used to elicit congruent emotional responses and identify areas of internal alignment or conflict. The micro-responses are tracked to detect congruence or incongruence between verbal and non-verbal signals. In the Capability Mapping stage, the Skill Transformation Engine evaluates the user’s current life context. For example, a 23-year-old CTO in an MNC may be mapped as a high performer in the career context based on world statistics, whereas a 40-year-old team lead with proper education, proper support, and with all favorable life circumstances may be mapped as a below-average performer in the career context according to world statistics. The method uses this data to build a personalized capability map. Finally, in the Readiness Assessment stage, the inference and recommendation engine integrates all data streams and determines whether the user is cognitively and emotionally ready to proceed. If readiness is confirmed, the method transitions to the next step. If not, additional priming or recalibration may be initiated.
[00260] In an embodiment, in the data exchange and interoperation process, the UI module captures the user inputs and responses and transmits them to the sensor integration and data acquisition module that sends the raw physiological and behavioral data to the multimodal signal processing module. Further, the multimodal signal processing module processes this data and sends the structured emotional and cognitive state vectors to the inference and recommendation engine which in turn updates the user readiness and capability indicators to the user profile and context management module. Further, the skill transformation engine stores the capability map and transformation potential in the user profile and context management module. The user profile and context management module in turn, provides contextual information to all the modules to ensure personalization and continuity.
[00261] The outcome of Step (201) is a fully calibrated user profile comprising emotional and physiological baselines, with identified suboptimal and optimal states, and a personalized capability map. In a summary, in an embodiment, the step (201) includes, acquiring multimodal input data from the individual through a user interface module and sensor integration and data acquisition module, wherein the input data comprises one or more verbal responses, non-verbal responses, physiological, anatomical, bio-chemical, pathological, psychological and/or emotional state signals.
[00262] In an embodiment, in step (202), the users are evaluated through a multimodal diagnostic and signal driven assessment to identify one or more challenges, capability gaps, and priority areas.
[00263] In an embodiment, a real-time diagnostic process is carried out by eliciting and analyzing both verbal and non-verbal responses of the user through a dynamic and adaptive questioning mechanism. The step aims to uncover underlying constraints, hidden limitations, unresolved challenges, and potential areas for transformation through a multimodal signal-based evaluation framework.
[00264] The step (202) is designed to uncover the root causes of the user’s limitations and potential by engaging them in a highly personalized, context-aware dialogue. A limited but precise set of questions is dynamically selected from a vast knowledge base (over 1 million and continuously expanding), while simultaneously capturing and analyzing a wide range of physiological, behavioral, and cognitive signals from the user. The approach does not rely on generalized statistical models of user types. Instead, it utilizes real-time calibration and contextual signal interpretation to tailor the diagnostic process uniquely to each individual.
[00265] In an embodiment, the intelligent questioning and diagnostic engine selects and sequences questions based on user state and contextual relevance, and uses reinforcement learning and taxonomy-based tagging to adapt questioning paths. The sensor integration and data acquisition module captures multimodal signals including micro-muscular movements (conscious and ideomotor), heart rate, blood pressure, respiratory rate, pupil dilation, eye movement, postural sway, facial blood circulation, muscle tension, perspiration, and EEG brainwave activity. The multimodal signal processing module analyzes verbal and non-verbal responses using AI models and applies a four-layer signal evaluation framework comprising redundancy (repetition and consistency of signals), congruence (alignment between verbal and non-verbal cues), scope (contextual relevance of the response), and consequences (potential impact if the issue is unaddressed). The user profile and context management module provides historical and contextual data to personalize the diagnostic process and stores updated insights and signal patterns. Further, the inference and recommendation engine synthesizes multimodal data to identify root causes and transformation levers and maps the signals to capability gaps and transformation opportunities. Furthermore, the expert knowledge base and capability map supplies structured taxonomies and transformation protocols and supports semantic search and contextual branching. Moreover, the parallel processing and memory management module tracks and correlates over two hundred signals in real time and maintains long-term memory of the user across sessions for pattern recognition.
[00266] In an embodiment, initially, a set of high-impact and contextually relevant questions is selected and presented to the user based on the current state and life context of the user. These questions are drawn from a large, taxonomy-tagged repository that is continuously updated. The questioning flow is dynamically adapted in response to the user's immediate feedback and signal patterns. Further, during the response phase, a wide range of multimodal signals is captured. These include physiological responses such as heart rate variability, respiration rate, muscular micro-movements, facial temperature fluctuations, pupil dilation, and other involuntary indicators, alongside conscious behavioral responses such as tone, pitch, posture, and word choice. The captured signals are processed using a multi-layer evaluation framework. First, signal redundancy is examined to detect repetitions across modalities. Second, congruence between verbal and non-verbal cues is assessed to reveal any underlying emotional dissonance or internal conflict. Third, the contextual relevance of the response is evaluated to determine the semantic scope. Fourth, the potential consequences of the issue, if left unaddressed, are inferred based on signal intensity and topic sensitivity. Further, the diagnostic process proceeds in a calibrated, non-generalized manner wherein individual signal responses are interpreted in real time to determine topic sensitivity, physiological reactivity, and potential incongruence. Based on the interpretations, deeper or tangential diagnostic paths are triggered to ensure precision and emotional safety. In an embodiment, memory recall prompts are occasionally presented to elicit emotionally significant responses. The responses that correlate with known taxonomy nodes or transformation templates trigger the retrieval of related questions and resolution paths to refine the diagnostic trajectory. Further, historical transformation records, including compressed temporal sequences, are used to support interpretation and question sequencing. Challenges and constraints identified during this step are classified into four distinct categories. The first category includes known but unresolved constraints. The second category comprises constraints known to the user but not recognized as problematic or actionable. The third category pertains to known challenges for which attempted resolutions have failed, and the fourth category consists of unknown constraints that are identified only through strong physiological responses or known consequences. Further, Branching into deeper or adjacent topics is dynamically guided by the strength and type of signals. This approach ensures that the diagnostic process remains efficient, emotionally safe, and highly personalized.
[00267] In an embodiment, data exchange and interoperation occur across various functional modules. The intelligent questioning and diagnostic engine delivers selected questions to the UI module, which in turn captures user responses and relays them to the sensor integration and data acquisition module. The sensor integration and data acquisition module transmits raw multimodal data to the multimodal signal processing module, where it is analyzed and structured. These structured signal interpretations are then sent to the inference and recommendation engine, which retrieves relevant transformation models and taxonomies from the expert knowledge base and capability map, and updates the user state and diagnostic insights in the user profile and context management module. The parallel processing and memory management module maintains session continuity and tracks the evolution of the signals over time.
[00268] The outcome of Step (202) includes a detailed diagnostic map outlining the user’s challenges, opportunities, and transformation levers; categorization of issues based on the user’s levels of awareness and consequences; identification of high-impact areas for intervention; and an enriched user profile incorporating multimodal signal data and contextual insights.
[00269] In a summary, in an embodiment, the step (202) includes, processing the multimodal input data using a multimodal signal processing module configured to extract emotional, cognitive, and behavioral features from the verbal and non-verbal responses, and generating a user state vector. In an embodiment, it further comprises the sub-step of dynamically generating a personalized set of diagnostic questions using an intelligent questioning and diagnostic engine, wherein the questions are selected based on real-time signal evaluation, contextual relevance, and historical user profile data, and categorized using a four-layer framework comprising redundancy, congruence, scope, and/or consequences.
[00270] In an embodiment, in step (203), one or more personalized transformation pathways are recommended for the users based on one or more diagnostic outcomes, contextual parameters, and expert knowledge.
[00271] In an embodiment, the step (203) is designed to generate a personalized, evidence-based set of transformation procedures for the user, grounded in the multimodal diagnostic data collected in Step (202). The objective is to identify the most impactful interventions capable of shifting the user’s trajectory by addressing capability gaps, unlocking new opportunities, and initiating long-term evolution.
[00272] The step (202) functions by synthesizing the user’s verbal and non-verbal responses, physiological signals, and contextual data to formulate a tailored set of transformation recommendations. These recommendations are not generic but are informed by a comprehensive understanding of the user’s current capabilities, the potential consequences of inaction, and the specific opportunities that can be unlocked through targeted developmental pathways.
[00273] Using the insights, transformation procedures are selected and structured according to calibrated frameworks. In an embodiment, one such framework involves a model expressed as AxT = C, where adjustments over time (AxT) are used to simulate the consequences (C) of either inaction or targeted intervention. In certain cases, this framework is referred to as "Impact Over Time" and/ or "Consequences Over Time" and a transformation framework that maps New Capabilities × Life Experience = Evolution. In an embodiment, this model is informed by historical data, including Accelerated Time Compression (ATC) reports and user evolution trajectories. Further in different embodiments more models and charts such as Impact Chart with Contrast, Evolution Mapping and/or Personalized Evolution Chart are generated for specific users.
[00274] The Inference and Recommendation Engine serves as the core decision-making module that synthesizes all diagnostic inputs. It applies probabilistic reasoning and pattern recognition to generate recommendations. The Expert Knowledge Base and Capability Map provides structured transformation protocols, skill ontologies, and capability gap models. It supports semantic reasoning and contextual mapping. The Skill Transformation Engine maps identified gaps to specific skills and capabilities. It generates a personalized learning and development roadmap. The User Profile and Context Management Module supplies historical, contextual, and emotional data. It stores the generated transformation plan and readiness indicators. The Parallel Processing and Memory Management Module correlates current findings with historical user data and system-wide patterns. It ensures continuity and consistency in recommendation logic.
[00275] The Inference Engine receives structured outputs from Step 202, including emotional and physiological states, verbal and non-verbal response patterns, and identified challenges and opportunities. It integrates this data with the user’s historical profile and contextual metadata. The Skill Transformation Engine compares the user’s current capabilities with ideal benchmarks for their goals and context. Gaps are identified across emotional, cognitive, behavioral, and skill domains. The Knowledge Base provides transformation protocols aligned with the identified gaps. The system uses the AxT = C model to simulate potential consequences of inaction and benefits of intervention. It also applies the Impact Chart with Contrast model: Adjustment ? Consequences ? Evolution.
[00276] Multiple future scenarios are simulated to evaluate the outcomes that may arise from the user's existing capabilities, the new opportunities that could be unlocked through the recommended interventions, and the broader impact of these opportunities on life consequences and long-term personal evolution. Further, a tailored set of transformation procedures is generated, comprising emotional recalibration techniques, cognitive restructuring exercises, behavioral nudges and habit formation strategies, skill-building modules, and others such as metaphors, reorganizing unconscious priorities, VR-based relations, reconditioning, personalized mental activities and mental games based on each individual's unique internal patterns. Each recommendation is tagged with test conditions: expected impact, required effort, time to effect, and dependencies and prerequisites.
[00277] Recommendations are prioritized based on user readiness, emotional receptivity, cognitive load, and environmental context ensuring the fact that the plan is achievable, sustainable, and aligned with the user’s transformation goals. The final transformation plan is stored in the user profile and context management module, and is made available to the transformation implementation module for execution in Step (204). Further, Feedback is monitored and the plan is dynamically adjusted in response to the user's interactions and progress.
[00278] In an embodiment, in the process of data exchange and interoperation, the inference and recommendation engine communicates with the expert knowledge base and capability map to retrieve transformation protocols and capability models. It sends the identified gaps and transformation goals to the skill transformation engine. Further, the skill transformation engine stores the personalized roadmap and progress indicators in the user profile and context management module.
[00279] Furthermore, the inference and recommendation engine prepares the recommendations for user review and confirmation through the UI module. Simultaneously, the parallel processing and memory management module tracks the historical patterns and validates the recommendation logic.
[00280] The outcome of Step (203) is a personalized, prioritized, and context-aware transformation plan. This includes a clear mapping of current capabilities to future opportunities and outcomes, along with a structured roadmap for implementation that outlines expected milestones and feedback checkpoints.
[00281] In a summary, in an embodiment, the step (203) includes, synthesizing the individual’s responses and signal features using an inference and recommendation engine to identify capability gaps, transformation opportunities, and root causes, and generating a personalized transformation plan using a capability gap model, a transformation framework comprising Consequences compounding over time following certain set of adjustments, and trajectory simulation models including Evolution Mapping and Personalized Evolution Chart. In an embodiment, step (203) further comprises the sub-step of generating an Accelerated Time Compression (ATC) prescription, comprising a prioritized set of capabilities and transformation set designed to reduce the time required for Skill development, Capability acceleration, enhanced life outcomes and computing an Impact Chart with Contrast to compare the user’s projected outcomes against global benchmarks and stored in Skill Transformation Engine.
[00282] In an embodiment, in step (204), one or more adaptive interventions are implemented by delivering one or more skill modules and practices suited to the user’s transformation needs. This step involves transitioning from planning and diagnostic stages to actual transformation by administering curated procedures aligned with the personal goals of the users.
[00283] In an embodiment, the personalized transformation plan generated in Step 203 is implemented by delivering targeted interventions that are dynamically adapted to the user’s real-time physiological, cognitive, and emotional states. This step ensures that the recommended procedures are not only delivered but also internalized effectively, resulting in measurable shifts in capability, behavior, and performance.
[00284] The step (204) transitions from diagnosis and planning to action and transformation. A curated set of interventions ranging from cognitive and emotional recalibration to behavioral and skill-based exercises is delivered through a multimodal, interactive interface. These interventions are monitored and adjusted in real time based on the user’s responses, ensuring high precision and personalization. The interventions are designed to engage both conscious and unconscious processes, using techniques such as personalized solar voice including spectrum, frequency spectrum, tone, content, modulations and tempo, trance induction, age regression, and/or peak performance simulations. In an embodiment, involuntary physiological sets including anatomical, bio-chemical, pathological, psychological, and emotional states with their dynamic responses are tracked to validate the effectiveness of each intervention and calibrate future actions.
[00285] In an embodiment, the transformation implementation module is responsible for delivering the prescribed interventions along with Adaptive Transformation Components (ATC) using various adjustments and formats, such as audio, visual, and interactive modes. The transformation implementation module further manages the pacing, sequencing, and modality of the delivery. Further, the sensor integration and data acquisition module continuously monitors the physiological set, including anatomical, biochemical, pathological, psychological, and emotional states, along with their dynamic and behavioral responses during interventions and captures data such as heart rate, electroencephalography (EEG), facial expressions, and muscle tension. Moreover, the multimodal signal processing module analyzes real-time signals to assess parameters such as engagement, stress, receptivity, and cognitive load, and detects involuntary responses that may indicate internalization or resistance. Further, the User Interface (UI) Module presents the interventions in an accessible, multimodal format and supports interaction through voice, gesture, and haptic inputs, and the skill transformation engine tracks the progress of the user against the predefined capability development milestones and updates the learning path based on the real-time feedback. Additionally, the mentorship simulation and feedback module emulates expert feedback mechanisms to validate the quality of intervention delivery and provides corrective guidance or reinforcement as needed.
[00286] In an embodiment, intervention selection and delivery are handled by the Transformation Implementation Module, which selects the appropriate intervention from the recommended set. In an embodiment, interventions may include the Solar Voice Model; trance induction and guided visualization; age regression techniques; peak performance games and simulations; micro-learning modules and behavioral nudges; and / or other interventions such as metaphors, reorganizing unconscious priorities, virtual reality (VR)-based relations, reconditioning, personalized mental activities, and/or mental games based on each individual's unique internal patterns.
[00287] Further, in an embodiment, during real-time monitoring and calibration, the sensor integration and data acquisition module captures physiological signals such as EEG brainwave activity, heart rate variability, skin conductance, facial micro-expressions, and voice tone and pitch, wherein the multimodal signal processing module interprets these signals to assess emotional resonance, cognitive engagement, stress or resistance, and internalization of the intervention.
[00288] Further, adaptive adjustment, also referred to as Target Response Based Adaptive Adjustment and Stimulus Delivery, is carried out based on real-time feedback, where the tone, pitch, and pacing of the Solar Voice Model; the complexity and duration of the intervention; and the sequence of procedures, such as switching from cognitive to emotional focus are dynamically adjusted.
[00289] In an embodiment, multi-procedure application is used for complex challenges, where multiple interventions may be applied in sequence or in parallel. Further, based on user response and transformation goals, optimal combination and order are determined.
[00290] In an embodiment, the progress tracking and feedback loop are implemented through the skill transformation engine, that monitors progress toward defined milestones. The mentorship simulation and feedback module further validates the effectiveness of each intervention and provides expert-level feedback, and if necessary, the process loops back to refine or repeat the specific interventions.
[00291] In an embodiment, in the process of data exchange and interoperation the transformation implementation module delivers intervention content to the UI module that captures user interaction and physiological response, which is transmitted to the sensor integration and data acquisition module. Further, the sensor integration and data acquisition module forwards the real-time data to the multimodal signal processing module, which processes and provides interpreted signals to the mentorship simulation and feedback module for validation. The mentorship simulation and feedback module, in turn, updates the progress and flags issues to the skill transformation engine. Finally, the skill transformation engine logs the outcomes and adjusts the learning path within the user profile and context management module.
[00292] The outcome of Step (204) is the targeted interventions that are delivered and internalized by the user wherein the real-time physiological and behavioral feedback confirms the effectiveness of each procedure, and capability development milestones are either achieved or adjusted accordingly.
[00293] In a summary, in an embodiment, this step (204) includes, implementing the transformation plan using a transformation implementation module configured to deliver one or more interventions through multimodal interfaces, including Solar Voice Model, micro-learning, behavioral nudges, and coaching simulations, and to adapt delivery based on real-time user engagement and physiological, anatomical, bio-chemical, pathological, psychological and emotional state feedback.
[00294] In an embodiment, in step (205), plurality of outcomes are validated and the transformation process are refined through an iterative monitoring of performance and one or more behavioural indicators.
[00295] In an embodiment, step (205) serves to validate that the transformation procedures implemented in step (204) have been internalized by the user and have resulted in the intended outcomes. This step (205) ensures that the transformation is complete, measurable, and sustainable. In cases where the transformation is found to be incomplete or suboptimal, a refined iteration of the process is initiated, beginning from the most appropriate earlier step. This step (205) functions as a quality assurance and feedback mechanism, evaluating the effectiveness of the interventions by comparing the user’s current state with the baseline established in step (201) and the transformation goals defined in step (203). Further, multimodal signal analysis, behavioral tracking, and expert-level validation are employed to confirm the desired cognitive, emotional, and behavioral shifts, and in presence of any gaps, inconsistencies, or lack of evidence for identified transformation, the cycle loops back, either to re-initiate priming, refine diagnostics, or adjust the intervention strategy, thereby maintaining a closed-loop, adaptive transformation framework.
[00296] In an embodiment, the sensor integration and data acquisition module captures real-time physiological and behavioral signals post-intervention, monitores indicators such as heart rate variability, EEG, facial expressions, and voice tone. Further, the multimodal signal processing module analyzes the post-intervention signals to assess emotional stability, cognitive clarity, and behavioral alignment, and compares the current signals with pre-intervention baselines. Furthermore, the inference and recommendation engine evaluates the extent to which the transformation goals have been met and determines whether the user’s state reflects the intended outcomes. Moreover, the mentorship simulation and feedback module emulates expert-level validation to assess the depth and completeness of transformation and flags blind spots, missed signals, or incomplete installations. Further, the user profile and context management module stores the updated user state and transformation outcomes and tracks the user progress across sessions and iterations. Additionally, the parallel processing and memory management module maintains continuity of signal patterns and transformation history and supports comparative analysis across time and sessions.
[00297] In an embodiment, the detailed process flow for step (205) begins with post-intervention signal capture, wherein the sensor integration and data acquisition module collects multimodal data as the user engages in follow-up tasks or reflective exercises. These signals include both voluntary responses such as verbal feedback and involuntary responses such as micro-expressions and EEG patterns. Further, in the next phase of comparative signal analysis, the multimodal signal processing module compares current signals with baseline data from Step (201), diagnostic markers from Step (202), and target states defined in Step (203). This analysis helps to identify improvements, regressions, or unchanged patterns. Further, during the outcome validation, the inference and recommendation engine determines the extent to which the user has achieved the intended emotional, cognitive, and behavioral shifts, assesses whether the transformation is stable and sustainable, and evaluates the sufficiency of evidence to confirm completion. Following this, an expert-level feedback simulation is carried out by the Mentorship Simulation and Feedback Module, which validates the system’s conclusions and may simulate expert feedback to confirm successful transformation of the user, recommend reinforcement, or identify overlooked issues.
[00298] Finally, in the decision logic and iteration trigger phase, validated transformations are marked as complete and the user profile is updated with final outcomes. Further, the incomplete transformations of the user lead to identification of the root cause of failure, followed by re-initiation of priming (Step 201), refinement of diagnostics (Step 202), adjustment of recommendations (Step 203), or re-implementation of interventions (Step 204).In an embodiment, the data exchange and interoperation begins with the sensor integration and data acquisition module capturing and transmitting post-intervention data to the multimodal signal processing module. Further, the multimodal signal processing module provides structured analysis of the user state to the inference and recommendation engine wherein the inference and recommendation engine forwards this information to the mentorship simulation and feedback module to request expert-level validation. Furthermore, the mentorship simulation and feedback module updates the user’s transformation status and feedback in the user profile and context management module. Finally, the inference and recommendation engine communicates with the system controller to trigger iteration if necessary.
[00299] The outcome of Step (205) comprises confirmation of transformation success or identification of remaining gaps, an updated user profile reflecting validated outcomes or required refinements, a closed-loop feedback method that ensures the transformation is complete, and readiness for exit or re-entry into the transformation cycle based on user needs.
[00300] In a summary, in an embodiment, the step (205) includes, validating transformation outcomes using a mentorship simulation and feedback module, wherein post-intervention signals are compared against baseline data and transformation goals using congruence vector analysis and evolution mapping. In an embodiment, further it comprises the sub-step of iteratively refining the transformation plan based on outcome validation, user feedback, and updated signal data to ensure ecological alignment, cross-domain capability generalization, and sustained personal evolution.
[00301] The present invention offers significant advantages in the field of ecological and personalized human transformation by enabling a system (100) that dynamically integrates multimodal diagnostics, contextual reasoning, and validated transformation pathways tailored to an individual’s evolving cognitive, emotional, and behavioral landscape. Through the continuous interaction of expert knowledge base and capability map (106), user profile and context management module (102), and inference and recommendation engine (107), the system facilitates transformation plans that remain aligned with the real-world societal dynamics, individual readiness, and long-term life goals.
[00302] One of the principal advantages of the invention is the ability to perform polycontextual capability development by identifying emotionally congruent contexts for initiating transformation, even when the target capability is required in a different life domain. This feature allows the system (100) to reduce internal resistance, accelerate transformation, and generalize the developed capability across multiple domains by employing the skill transformation engine (111) and parallel processing and memory management module (109). The system (100) intelligently manages capability transfer protocols, ensuring that skills developed in emotionally rich or accessible settings become usable in the intended professional or relational environments.
[00303] The invention further provides a scope enhancement mechanism that detects latent desires, unresolved aspirations, or overlooked cognitive-emotional patterns through signal-based diagnostics and causal inference. By utilizing multimodal signal processing module (104), sensor integration and data acquisition module (103), and intelligent questioning and diagnostic engine (105), the system (100) uncovers transformation opportunities that go beyond user-stated objectives and integrates them into the capability roadmap. This ensures the transformation remains both targeted and expansive, enabling reconstruction of internal belief and broadening of aspiration.
[00304] Additionally, the invention enables ecological prediction and personalized trajectory simulation by comparing current user states with historical transformation data across similar profiles. By analyzing congruence vectors, risk indicators, and signal anomalies through the inference and recommendation engine (107) and validating recommendations through the mentorship simulation and feedback module (110), the system (100) produces transformation plans that are safe, contextually appropriate, and validated through evidence-based reasoning.
[00305] Another notable advantage is the continuous updating and self-evolution capability of the expert knowledge base (106), that enables the system (100) to remain relevant across changing societal norms, industry expectations, and psychological frameworks. Through the integration of world-level statistics, historical data, and real-time user feedback, the system (100) adapts transformation strategies to the user’s current environment, thereby preserving ecological validity and outcome sustainability.
[00306] The system (100) architecture supports longitudinal memory and session continuity through the parallel processing and memory management module (109), enabling transformation as a consistent, adaptive process. Further, the modular integration sustains a closed-loop feedback cycle in which each intervention is measured, refined, and aligned with evolving user needs.
[00307] The invention ensures that transformation is not fragmented or static, but dynamic, system-guided, and grounded in validated causal models. The invention eliminates arbitrary guesswork, supports emotionally intelligent decision-making, and provides a scalable framework for delivering domain-specific, cross-domain, and lifelong transformational outcomes.
[00308] Having generally described this invention, a further understanding can be obtained by reference to multiple examples, which are provided herein for the purpose of illustration only and are not intended to be limiting unless otherwise specified.
Example 1: The system’s understanding of the connection between the capability set and life consequences over time
[00309] In an embodiment, a core functional innovation of the present invention involves the ability of the system (100) to understand, simulate, and validate the correlation between a user’s specific capability set and the life consequences that unfold over time. This capability facilitates the diagnosis of current limitations, enables forecasting of future outcomes, supports prescription of optimal interventions, and drives accelerated transformation while maintaining ecological validity across life domains.
[00310] The system (100) is trained on a comprehensive, validated dataset comprising world-level statistics on life outcomes, capability evolution trajectories, and timelines associated with capability development. The dataset further incorporates tested outcomes derived from multiple individuals across diverse contexts, wherein the outcomes exhibit enhancement and significant time compression when compared against global statistical averages. Additionally, historical data pertaining to the influence of specific capabilities on life consequences across months and years is integrated. The system (100) further references validated transformation outcomes, wherein the time required to reach predefined milestones is reduced significantly, in some embodiments ranging between 90% to 99% of conventional durations.
[00311] This data undergoes continual refinement through supervised learning frameworks and validation via real-time user interaction. The system (100) archives and analyzes transformation journeys across users to improve predictive precision and enhance ecological alignment across personalized transformation paths.
[00312] In an embodiment, following the establishment of the correlation between the capability sets and life consequences of the user, the system (100) proposes a tailored set of capabilities directed toward substantial improvement of user quality of life. The system (100) reduces the effort and time required to achieve outcomes by selecting high-impact, context-sensitive interventions; identifies and promotes ambitious, non-normalized transformation targets such as Z-level outcomes, thereby preventing mediocrity and supporting aspirational trajectories that remain ecologically viable; and ensures synchronized exponential development across multiple life domains including health, relationships, career, and emotional well-being.
[00313] In an exemplary use case, consider a user intends to progress from an initial state A to a target state B. In this context, the system (100) determines the necessary capability set for the transition, organizes and sequences the capabilities, simulates their potential impact, and delivers them using adaptive intervention strategies. The result is a compressed timeline, enhanced ecological alignment, and an elevated probability of successful transformation. The system (100) dynamically evaluates whether the transformation target should remain at a normalized level or be upgraded to a Z-level based on potential viability. Upon deeming the Z-level outcome ecologically valid, the system (100) identifies and prescribes the full set of capabilities required to achieve it.
[00314] In an embodiment, the implementation of this functionality is supported by the expert knowledge base and capability map (106) that stores transformation protocols, capability-outcome mappings, and world-level benchmarks; the inference and recommendation engine (107) performs user data synthesis, trajectory simulations, and optimal capability identification; the skill transformation engine (111) maps capabilities to transformation goals and monitors longitudinal progress; the user profile and context management module (102) maintains contextual and historical data to support individualized recommendations; the parallel processing and memory management module (109) validates long-term capability-outcome correlations, and the mentorship simulation and feedback module (110) facilitates reasoning-based support for specialists and trainees.
[00315] Further, data is ingested from historical transformation benchmarks and real-time user signals, processed through mapping, simulation, validation, and time compression prescription generation, and delivered as a personalized capability roadmap along with predicted life consequences. The system (100) further updates user dashboards and provides reasoning output for human specialists.
[00316] In another embodiment, the system (100) assists professionals such as specialists or trainees by exposing them to the internal reasoning processes of the transformation engine. This functionality allows specialists to develop expert-level intuition by understanding how specific capabilities influence life outcomes. The system (100) serves as a co-pilot to reduce diagnostic time, enhance intervention precision, and support faster professional learning curves.
Example 2: The System Output for Specialist Intuition Development: Understanding Capability Set and Life Consequences
[00317] The system (100) generates structured outputs referred to as the “mind of the system”, which communicates the internal analytical reasoning behind a specific prescription. This output is routed to the mentorship simulation and feedback module (110), which formats the reasoning into structured content to be used by specialists, trainees, and transformation facilitators. The output includes a detailed explanation of how the system (100) identified capability gaps, evaluated transformation trajectories, prioritized certain interventions, and generated the ATC (Accelerated Time Compression) prescription.
[00318] In an embodiment, the formatted output comprises three components: a deep reasoning trace outlining the system’s (100) thinking; a case sheet for training use detailing signal interpretation and transformation mapping; and a prescription sheet outlining the capability roadmap and ecological analysis for the end user. The dual output comprising the transformation prescription for the participant and the structured reasoning for the specialist enables professionals to refine their diagnostic intuition and accelerate their skill acquisition by comparing their own reasoning processes with those of the system (100).
[00319] In an embodiment, the inference and recommendation engine (107) generates the transformation roadmap based on multimodal signal data, historical benchmarks, and capability patterns; the mentorship simulation and feedback module (110) formats the reasoning for presentation, while the skill transformation engine (111) aligns milestone tracking with professional learning; the expert knowledge base and capability map (106) provides ontologies and taxonomies that structure capability relationships; the parallel processing and memory management module (109) supports longitudinal case comparisons, and the user profile and context management module (102) supplies contextual history for personalized explanation.
[00320] Inputs to the system (100) comprises diagnostic signals from the user, historical transformation outcomes, and statistical data. The system (100) processes the information to generate the ATC prescription, validate ecological soundness, and produce formatted reasoning outputs that include the transformation prescription for the user, the reasoning trace for the specialist, structured case sheets for training, and feedback for future refinement.
Example 3: Continuous Knowledge Base Updating for Capability–Life Outcome Mapping
[00321] In an embodiment, the invention enables continuous updating and evolution of the expert knowledge base and capability map (106) by incorporating validated data across time and diverse contexts. The system (100) dynamically refines its internal mappings between capabilities and life outcomes, accommodating temporal variations, contextual shifts, and evolving societal paradigms.
[00322] Validated historical transformation data, real-time user interactions across heterogeneous demographics, world-level statistics related to industries, family structures, behavioral patterns, and evolving societal expectations are continuously ingested. The user profile and context management module (102) captures contextual metadata including geographic location, time-stamped data, device usage, and ambient environmental inputs. These inputs are processed through supervised learning pipelines, wherein the inference and recommendation engine (107) detects emerging structures, shifts in in societal dynamics, and psychological patterns such as changes in the family constructs and relationship models; evolution in business types and industry ecosystems; shifts in societal attitudes, expectations, and behavioral norms; and emerging personas and psychological archetypes. These changes are subsequently correlated with capability sets and life outcomes. The expert knowledge base and capability map (106) is updated with revised transformation protocols, refined capability gap models, and adapted ATC (Accelerated Time Compression) prescriptions. The parallel processing and memory management module (109) facilitates temporal pattern recognition and supports long-term tracking of capability-effectiveness relationships. The mentorship simulation and feedback module (110) supports expert-level validation of the newly acquired data, while the cloud infrastructure and deployment module (113) ensures scalable ingestion, synchronization, and propagation of the revised data across all system modules.
[00323] For users, transformation plans generated by the system (100) remain aligned with contemporary ecological and societal requirements. Interventions are customized for contextual relevance and temporal appropriateness. For specialists and trainees, the system (100) enables access to updated mappings, refined diagnostic trajectories, and evolving transformation case studies, enhancing decision-making accuracy. For the system (100), the functionality ensures lifelong relevance, precision, and adaptive intelligence capable of evolving with global trends and individual-level insights.
[00324] The end-to-end data flow begins with ingestion of historical transformation records, real-time user interactions, and world-level statistics and contextual metadata, which are processed through pattern recognition and causal inference and/or systemic connections, Ontology updates and capability remapping, and ecological validation and supervised learning. The outcome includes updated transformation protocols, refined capability-outcome mappings, and personalised recommendations that remain reflective of the dynamic external world and its ecological constraints.
Example 4: Ecological Prediction and Personalized Trajectory Simulation
[00325] In an embodiment, the invention enables the system (100) to perform ecological prediction and simulate personalized transformation trajectories for each user. By leveraging validated historical data, real-time contextual metadata, and longitudinal signal patterns, the system (100) generates transformation roadmaps that are not only effective but ecologically viable, contextually safe, and sustainably aligned with each individual’s physiological, cognitive, emotional, and situational profile.
[00326] In an embodiment, the system (100) performs ecological prediction by analyzing validated historical data from similar users and transformation journeys; simulates future trajectories for the specific user based on their current state, goals, and contextual parameters; avoids recommendations that have not worked in the past for that user or for similar profiles; identifies edge cases and risk factors that may lead to negative outcomes when certain changes are implemented; and generates a sequence of recommendations in an order that maximizes effectiveness and minimizes ecological disruption. For example, when an expert excels in their domain but lacks strengths in marketing and sales, setting aggressive goals in those areas could result in elevated stress levels. In such scenarios, the system (100) focuses on developing the capability for effective collaboration with a team, thereby supporting those functions and ensuring ecological alignment.
[00327] For users, the transformation roadmap delivered is designed to minimize stress, prevent overload, and support seamless integration with existing life contexts. Interventions that historically resulted in failure are pre-emptively excluded. Roadmaps are constructed to align with intrinsic readiness and external life conditions. For specialists and trainees, ecological simulations provide reasoning outputs that clarify the underlying rationale behind each prescribed intervention. Blind spots, edge cases, and unintended systemic consequences are detected and highlighted.
[00328] The practical application and benefits of the system (100) manifest across three key domains: users, specialists and trainees, and the system (100). For users, the system (100) delivers transformation plans that are safe, effective, and personalized, while avoiding interventions that have previously failed or resulted in adverse effects. Furthermore, the users benefit from a sequenced roadmap that aligns with their physiological, emotional, and contextual readiness, thereby enhancing the likelihood of sustainable outcomes. For specialists and trainees, the system (100) enables a deeper understanding of the ecological reasoning that underpins each recommendation; supports the identification of edge cases and facilitates the avoidance of high-risk interventions; and enhances diagnostic and planning accuracy by leveraging simulations that reflect system-generated insights. From the perspective of the system (100), continual refinement of its ecological prediction models is achieved through supervised learning. The system (100) further improves transformation outcomes by integrating learnings from both historical failures and successes, and upholds ethical and safety standards across diverse user contexts.
[00329] Multiple modules within the system (100) operate in concert to achieve the functionality. The Inference and Recommendation Engine (107) performs trajectory simulation, ecological validation, and personalized recommendation generation tailored to the user’s transformation journey; The Expert Knowledge Base and Capability Map (106) provides structured transformation protocols, risk models, and detailed capability outcome mappings that inform system decisions; The User Profile and Context Management Module (102) supplies historical data, contextual metadata, and transformation history, which are essential for generating personalized simulations and predictions; The Parallel Processing and Memory Management Module (109) tracks longitudinal user data and supports advanced pattern recognition across multiple sessions. The Mentorship Simulation and Feedback Module (110) is responsible for validating ecological predictions and identifying potential risks or blind spots based on system interactions; and the Skill Transformation Engine (111) aligns capability development with ecological constraints and monitors progress to ensure effective and sustainable transformation.
[00330] The overall data pipeline begins with input acquisition from user-specific historical records, contextual metadata, and global outcome statistics. During processing, trajectory simulations, ecological validations, and risk mappings are performed. Final outputs include ecologically validated transformation plans, avoidance of contraindicated interventions, feedback loops to specialists, and refinement of internal predictive models.
Example 5: Polycontextual Capability Development and Contextual Optimization
[00331] In an embodiment, the invention enables polycontextual capability development through contextual optimization, whereby a capability is cultivated across diverse life domains. The system (100) identifies the most suitable context for capability development, even when that context differs from the domain in which the capability is ultimately intended to be applied. This approach increases the speed and ecological alignment of transformation by aligning development pathways with emotionally resonant and accessible experiences.
[00332] The contextual optimization process is performed by mapping a capability to its interconnected functions across multiple domains, identifying alternative contexts conducive for effective development, simulating transformation trajectories across these contexts, and selecting the shortest and safest path for capability installation. Upon installation, the capability is generalized to all relevant domains through established contextual transfer protocols.
[00333] For instance, to develop empathy in a professional environment, the system (100) evaluates emotionally charged personal scenarios, such as family conflicts, as more effective initiation points. After developing empathy in that emotionally intense context, the system (100) generalizes the capability to the professional domain through validated transfer mechanisms. This strategy enables alignment with the user’s emotional landscape, thereby enhancing the likelihood of successful transformation.
[00334] The system (100) comprises multiple modules operating in conjunction to enable this functionality. The inference and recommendation engine (107) performs contextual mapping, trajectory simulation, and capability generalization; the expert knowledge base and capability map (106) stores capability-context mappings and generalization protocols; the user profile and context management module (102) supplies emotional states, contextual metadata, and historical user interaction patterns; the skill transformation engine (111) tracks capability development and executes cross-domain transfer mechanisms; the parallel processing and memory management module (109) maintains longitudinal patterns across life domains; the mentorship simulation and feedback module (110) validates contextual decisions and supports supervised learning pathways.
[00335] The input to the system (100) includes user-defined transformation targets, contextual and emotional metadata, and historical performance data. The processing stage involves mapping capabilities to alternative domains, simulating multiple transformation pathways, selecting optimal developmental contexts, and planning cross-domain generalization. The output includes a contextually optimized transformation roadmap, capability transfer protocols, and system feedback for ongoing refinement.
Example 6: Capability Generalization Across Domains via Connecting Capabilities
[00336] In an embodiment, the invention supports cross-domain generalization by identifying and developing connecting capabilities that enable transfer of a well-formed skill or emotional function from one domain to another. This functionality ensures that high-performance capabilities developed in one domain are leveraged in others, leading to holistic transformation.
[00337] Capability generalization involves detecting capabilities that are well-developed in a source domain, identifying underutilized or absent instances of the same capability in target domains, and predicting the required connecting capabilities to bridge the gap. The system activates behavioral circuits associated with the original capability in the new domain and validates the generalization through multimodal feedback.
[00338] For example, a user exhibiting creative excellence in art may be guided to transfer that creativity into business innovation. The system analyzes multimodal signals and identifies latent inventiveness, subsequently constructing the connecting capabilities necessary for expression within the target domain.
[00339] The system (100) utilizes the inference and recommendation engine (107) for domain mapping and connector development; the expert knowledge base and capability map (106) stores connector models and domain-specific capability ontologies; the skill transformation engine (111) manages cross-domain transfers and monitors progress; the user profile and context management module (102) supplies emotional and contextual data for transfer analysis; the parallel processing and memory management module (109) supports recognition of domain patterns; and the mentorship simulation and feedback module (110) validates transfer efficacy and provides feedback for ongoing refinement, wherein the system uses input data from capability performance metrics, contextual and emotional metadata, and historical outcomes. The system processes the- data to identify target domains, predict connecting capabilities, simulate domain transfer, and activate relevant circuits. The output includes a generalized capability roadmap, validation feedback, and updated transformation metrics.
Example 7: Scope Enhancement: Expanding the User’s Cognitive and Transformational Horizon
[00340] In an embodiment, the system (100) facilitates scope enhancement by dynamically expanding the user’s perception of achievable outcomes through multimodal signal tracking and contextual diagnostics. The process reveals hidden opportunities, reconstructs internal models, and expands the aspirational range of the user beyond initially defined transformation goals.
[00341] Scope enhancement involves identifying limitations in a user’s current worldview through real-time signal analysis and adaptive diagnostics. The system engages the user with context-specific questioning, computes physiological-emotional-cognitive congruence vectors, and identifies gaps or unresolved tensions. These insights enable construction of an expanded internal map, guiding more ambitious and aligned transformations.
[00342] The sensor integration and data acquisition module (103) captures real-time signals such as micro-muscular changes, heart rate variability, and pupil dilation. These signals are interpreted by the multimodal signal processing module (104) to identify state transitions. The intelligent questioning and diagnostic engine (105) generates adaptive queries based on detected anomalies. The inference and recommendation engine (107) synthesizes data to propose scope enhancements. The user profile and context management module (102) maintains evolving user profiles with new insights. The parallel processing and memory management module (109) supports historical comparison and longitudinal pattern recognition. The mentorship simulation and feedback module (110) validates expanded scope and reinforces transformation planning.
[00343] The data flow involves user interaction through the user interface module (101), physiological and behavioral data capture by the sensor module (103), signal processing by the multimodal signal module (104), adaptive probing by the intelligent questioning module (105), and synthesis by the inference engine (107). The updated profile is validated and looped back through the mentorship module (110) and integrated into the transformation pathway through the skill transformation engine (111) and transformation implementation module (108).
[00344] In an embodiment, the key capabilities enabled by scope enhancement include conversational discovery of overlooked insights, expansion of aspirational range, worldview reconstruction through puzzle-piece integration, evidence-driven personalization using real-time signals, and ecological impact across multiple life domains.
Example 8: System Intelligence and Multimodal Integration for dynamic personalized question generation
[00345] The system (100) comprises an integrated mechanism for dynamic personalized question generation that emulates the diagnostic precision of expert human facilitators. This functionality is achieved through the real-time interpretation of multimodal signals, including both verbal and non-verbal inputs, to generate a limited yet highly relevant set of personalized questions for each user. Unlike conventional static questionnaires or statistical generalizations, the system (100) performs calibration-driven questioning using physiological, anatomical, biochemical, pathological, psychological, and emotional responses received during user interaction.
[00346] The questioning mechanism is implemented through a collaborative operation among multiple modules. The sensor integration and data acquisition module (103) captures verbal and non-verbal signals, including brainwave activity (EEG), heart rate, heart rate variability, skin conductance, facial micro-expressions, voice pitch and tone, eye movement, pupil dilation, postural sway, muscle tension, and ideomotor movements. These real-time signals are streamed to the multimodal signal processing module (104), which applies advanced artificial intelligence models to extract structured features such as emotional tone, engagement level, cognitive load, congruence patterns, and stress indicators. These features are encoded into a unified user state vector that dynamically informs the questioning strategy.
[00347] The user profile and context management module (102) retrieves historical data including demographic information, emotional trends, transformation goals, past interactions, and contextual metadata such as time, location, and device used. This information is used to contextualize and personalize the diagnostic interaction. The intelligent questioning and diagnostic engine (105) utilizes this context in conjunction with the state vector to query a knowledge base comprising a large collection of prompts distributed across multiple life domains. The expert knowledge base and capability map (106) stores structured taxonomies of capabilities, transformation protocols, and domain-specific ontologies, which support semantic retrieval and contextual branching.
[00348] During the questioning, the intelligent questioning and diagnostic engine (105) applies a four-layer signal evaluation framework for prioritizing questions: (i) redundancy, to confirm insights across multiple modalities; (ii) congruence, to assess alignment between verbal and non-verbal expressions; (iii) scope, to measure the depth and breadth of responses; and (iv) consequences, to predict potential outcomes if unresolved issues persist. The inference and recommendation engine (107) synthesizes inputs to determine the most impactful diagnostic trajectory by applying systemic connections, causal inference, and root cause analysis.
[00349] The parallel processing and memory management module (109) supports this system-wide operation by tracking more than 200 signal streams in real time and maintaining longitudinal memory across multiple user sessions. This ensures continuity in questioning strategy and supports the refinement of diagnostic models through reinforcement learning.
[00350] The system (100) employs artificial intelligence frameworks including natural language processing for semantic interpretation of user responses, reinforcement learning for optimizing questioning policies based on historical effectiveness, multimodal fusion models for synthesizing verbal and non-verbal signals into an actionable state vector, and taxonomy-based reasoning for domain-aware question generation. Systemic connections inference models further enhance diagnostic precision by predicting underlying causes and potential transformation levers.
[00351] The dynamic questioning workflow begins with initialization, where the system (100) captures the user’s baseline state through inputs collected by the sensor integration and data acquisition module (103) and initial responses through the user interface module (101). The baseline serves as a contextual anchor for subsequent stages. Further, the intelligent questioning and diagnostic engine (105) retrieves potential questions from the expert knowledge base and capability map (106) based on the user’s current state and contextual profile stored within the user profile and context management module (102). This retrieval is informed by previously collected demographic information, transformation goals, emotional trends, prior interactions, and environmental context such as time and location.
[00352] The multimodal signal processing module (104) performs signal-driven filtering of the candidate questions by assigning scores based on relevance to the current emotional and cognitive state, historical effectiveness derived from reinforcement learning feedback maintained in the inference and recommendation engine (107), and real-time readiness indicators such as low stress and high engagement levels. As the user responds to the presented questions, the system (100) continuously monitors and evaluates physiological, anatomical, biochemical, pathological, psychological, and emotional states through dynamic signal analysis.
[00353] On detecting elevated stress levels or diminished engagement through the sensor integration and data acquisition module (103), the system (100) dynamically adapts the interaction. The intelligent questioning and diagnostic engine (105) triggers actions such as rephrasing the question, switching the mode of delivery through the user interface module (101) for example, from audio to visual or pausing or redirecting the conversation entirely. The inference and recommendation engine (107) supports branching into deeper or semantically adjacent topics based on real-time interpretation of user signals and historical data stored in the expert knowledge base and capability map (106), including Accelerated Time Compression (ATC) reports.
[00354] The system (100) further applies mechanisms for categorizing the user’s challenges. These are broadly classified into four categories: known but unresolved constraints, known but unrecognized problems or opportunities, known constraints the user attempted but failed to resolve, and unknown constraints manifesting through known consequences. These categorizations enable targeted interventions and support deeper exploration of unresolved personal and behavioral issues.
[00355] The dynamic questioning capability of the system is a result of deep integration between multimodal sensing, real-time AI inference, and structured knowledge representation. By continuously interpreting both the verbal and non-verbal responses, the system adapts its questioning strategy with expert-level precision—uncovering hidden insights, validating emotional truths, and guiding users toward meaningful transformation.¬
Reference numbers:
Components Reference Numbers
System 100
User Interface (UI) Module 101
User Profile and Context Management Module 102
Sensor Integration and Data Acquisition Module 103
Multimodal Signal Processing Module 104
Intelligent Questioning and Diagnostic Engine 105
Expert Knowledge Base and Capability Map 106
Inference and Recommendation Engine 107
Transformation Implementation Module 108
Parallel Processing and Memory Management Module 109
Mentorship Simulation and Feedback Module 110
Skill Transformation Engine 111
Security, Privacy and Compliance Module 112
Cloud Infrastructure and Deployment Module 113
,CLAIMS:We claim:
1. A system for implementing targeted and ecological permanent transformations in one or more user, the system (100) comprising:
a. a user interface (UI) module (101), configured to facilitate one or more multimodal interactions with one or more users through text, voice, gesture, and haptic inputs, and to deliver transformation content including personalized insights, questions, and interventions;
b. a user profile and context management module (102), configured to maintain a dynamic, evolving profile of the user, including demographic data, transformation goals, emotional trends, contextual metadata, and/or historical interaction data;
c. a sensor integration and data acquisition module (103), configured to collect one or more real-time physiological, anatomical, bio-chemical, pathological, psychological, emotional state, behavioral, and/or environmental data from one or more sensors, including EEG, heart rate, skin conductance, facial expressions, voice tone, and posture;
d. a multimodal signal processing module (104), operatively coupled to the sensor integration module, configured to analyze the one or more collected data using one or more artificial intelligence models to extract emotional, cognitive, physiological, anatomical, bio-chemical, pathological, psychological features and/or emotional state and generate a user state vector;
e. an intelligent questioning and diagnostic engine (105), configured to dynamically generate a limited, highly relevant, and personalized set of diagnostic questions in real time, based on multimodal signals, user profile data, and knowledge base prompts;
f. an expert knowledge base and capability map (106), comprising structured ontologies of life domains, skill taxonomies, transformation protocols, and/or capability gap models, and configured to support semantic reasoning and capability mapping;
g. an inference and recommendation engine (107), configured to synthesize multimodal signals, user profile data, and knowledge base content to identify root causes or systemic influences or systemic constraints of user challenges, detect capability gaps, simulate future trajectories, and generate personalized transformation recommendations;
h. a transformation implementation module (108), configured to deliver one or more adaptive interventions using an emotional voice, micro-learning content, behavioural nudges, habit formation tools, Metaphors, Reorganizing unconscious priorities, VR Based Simulations, Reconditioning, personalized mental activities, mental games, and/or real-time coaching simulations, and to monitor user engagement and internalization of interventions;
i. a mentorship simulation and feedback module (110), configured to emulate expert-level feedback, validate system-generated insights, detect blind spots, and/or provide learning reinforcement for both end-users and/or professionals-in-training; and
j. a skill transformation engine (111), configured to generate a personalized capability development roadmap, track progress towards enhancing the milestones, and apply Accelerated Time Compression (ATC) models to reduce transformation and milestone achievement time compared to conventional methods.

2. The system (100) as claimed in claim 1, wherein the system (100) further comprises:
a. a parallel processing and memory management module (109), configured to track more than one concurrent signals, maintain long-term memory across sessions, and support temporal pattern recognition and continuity of transformation;
b. a security, privacy, and compliance module (112), configured to enforce data protection, encryption, consent management, and/or compliance with global data privacy regulations; and
c. a cloud infrastructure and deployment module (113), configured to support scalable, containerized deployment of the system across cloud, edge, and /or hybrid environments;
wherein the system (100) is further configured to:
d. perform dynamic personalized questioning by selecting and adapting questions in real time based on multimodal signal congruence, redundancy, scope, and/or consequence analysis;
e. generate evolution mapping outputs that track identity-level changes, emotional maturity, and transformation milestones across time and life domains;
f. generate a Personalized Evolution Chart, comprising a dynamically computed simulation of the user’s transformation journey including capability set derivation, ecological validation, and/or ATC prescription;
g. apply Accelerated Time Compression (ATC) models to simulate superior life outcomes in significantly reduced time frame based on capability mapping, system stimulated capability development ecological validation; and
h. generate an Impact Chart with Contrast, configured to compare the user’s transformation outcomes against global statistical baselines across adjustments or transformations across one of skill development, capability development, changes in unconscious patterning, shifts in thinking, consequence, and/or evolution levels, thereby quantifying the improbability, drastically reduced time frames, significance, and systemic value of achieved changes.

3. The system (100) as claimed in claim 1, wherein the system (100) further configured to:
a. understand and simulate the connection between capability sets and life consequences over time, enabling predictive modelling of future outcomes and strategic intervention planning;
b. output structured reasoning and transformation logic to support specialist intuition development, including annotated case sheets and transformation prescriptions for professional learning and calibration;
c. enable polycontextual capability development and contextual optimization by identifying optimal contexts for skill acquisition and generalizing developed capabilities across multiple life domains; and
d. facilitate capability generalization across domains via connecting capabilities, allowing transfer of well-formed skills from one domain to another through system stimulated development of capabilities for bridging.

4. The system (100) as claimed in claim 1, wherein the system (100) further configured to:
a. perform scope enhancement by expanding the user’s perception of what is possible and desirable, through multimodal signal-driven discovery of latent choices, aspirations, potential and transformation opportunities;
b. continuously update its knowledge base for capability–life outcome mapping using validated historical data, real-time user interactions, and evolving societal trends;
c. perform ecological prediction and personalized trajectory simulation to ensure that all recommended interventions are safe, sustainable, and/or contextually aligned with the user’s physiological, anatomical, bio-chemical, pathological, psychological and emotional state, and/or environmental conditions.

5. A method for implementing targeted and ecological permanent transformations in one or more users, the method (200) comprises the steps of:
a. preparing one or more users through capability priming and state calibration by aligning one or more internal and external factors for initiating a transformation process (201);
b. evaluating the users through a multimodal diagnostic and signal driven assessment to identify one or more challenges, capability gaps, and priority areas (202);
c. recommending one or more personalized transformation pathways for the users (203);
d. implementing one or more adaptive interventions by delivering one or more skill modules and practices suited to the user’s transformation needs (204); and
e. validating plurality of outcomes and refining the transformation process through an iterative monitoring of performance and one or more behavioural indicators (205).

6. The method (200) as claimed in claim 5, wherein preparing one or more users comprises a step of acquiring multimodal input data from the individual through a user interface module and sensor integration and data acquisition module, wherein the input data comprises one or more verbal responses, non-verbal responses, physiological, anatomical, bio-chemical, pathological, psychological and/or emotional state signals.
7. The method (200) as claimed in claim 5, wherein evaluating the users comprises the steps of :
a. processing the multimodal input data using a multimodal signal processing module configured to extract plurality of emotional, cognitive, and behavioral features from the verbal and non-verbal responses, and generating a user state vector;
b. dynamically generating a personalized set of diagnostic questions using an intelligent questioning and diagnostic engine, wherein the questions are selected based on real-time signal evaluation, contextual relevance, and historical user profile data, and categorized using a four-layer framework comprising redundancy, congruence, scope, and/or consequences;

8. The method (200) as claimed in claim 5, wherein recommending the personalized transformation pathways further comprises the steps of :
a. synthesizing the individual’s responses and signal features using an inference and recommendation engine to identify capability gaps, transformation opportunities, and root causes or systemic influences or systemic constraints of user challenges, and generating a personalized transformation plan using:
i. a capability gap model;
ii. a transformation framework comprising consequences compounding over time following certain set of adjustments; and
iii. trajectory simulation models including evolution mapping and personalized evolution chart; and
b. generating an Accelerated Time Compression (ATC) prescription, comprising a prioritized set of capabilities and transformation set designed to reduce the time required for skill development, capability acceleration, enhanced life outcomes and computing an impact chart with contrast to compare the user’s projected outcomes against global benchmarks and to store in a skill transformation engine.

9. The method (200) as claimed in claim 5, wherein the validation of outcomes further comprises the step of iteratively refining the transformation plan based on outcome validation, user feedback, and updated signal data to ensure ecological alignment, cross-domain capability generalization, and sustained personal evolution.

10. The method (200) as claimed in claim 5, wherein the implementation of adaptive interventions further comprises implementing the transformation plan using a transformation implementation module configured to deliver one or more interventions through multimodal interfaces, including emotional voice , micro-learning, behavioral nudges, and coaching simulations, and to adapt delivery based on real-time user engagement and physiological, anatomical, bio-chemical, pathological, psychological and/or emotional state feedback;
11. The method (200) as claimed in claim 5, wherein the validation of outcomes includes validating transformation outcomes using a mentorship simulation and feedback module, wherein one or more post-intervention signals are compared against baseline data and transformation goals using congruence vector analysis and evolution mapping.