Description:Field of the Invention

The present invention relates to artificial intelligence for business transformation, particularly to deep learning systems for modeling, simulating, and reinventing strategic business models across interdisciplinary domains.

Background
The background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
Strategic innovation in business has traditionally relied upon static frameworks, linear planning methods, and retrospective analysis. Classical approaches such as the Business Model Canvas, SWOT analysis, and Porter’s Five Forces provide foundational insights but lack the flexibility and adaptiveness required for modern, volatile business ecosystems. These tools often rely on expert intuition, manual benchmarking, or case-driven generalizations that are inadequate when applied to fast-changing sectors like digital platforms, fintech, or decentralized commerce. Further, they lack a mechanism for integrating real-time data, simulating alternate configurations, or accounting for complex, non-linear dependencies between organizational elements.
Existing attempts to computationally support business model innovation are often limited to generic strategy recommendation systems or rule-based expert systems. These systems lack the granularity, semantic richness, and domain specificity needed for enterprise-grade implementation. While some tools incorporate AI in the form of market analysis or customer behavior prediction, few offer a holistic transformation engine that evaluates the entire business model architecture under constraints of feasibility, disruption potential, profitability, and external risk. Prior art also lacks mechanisms to simulate the cascading impact of business model changes across functional areas such as HR, operations, technology, and finance.
Moreover, interdisciplinary insights from behavioral economics, design thinking, and organizational theory remain underutilized in existing strategy design tools. Thus, there is a clear need for a modular, adaptive, and intelligent system that can absorb unstructured and structured strategic data, generate and simulate alternative business models, refine them using domain knowledge, and orchestrate their deployment in an actionable manner. The present invention addresses these shortcomings through an integrated neural framework capable of revolutionizing how businesses design, test, and evolve their strategic architectures.
All references, including publications, patent applications, and patents, cited herein are hereby incorporated by reference to the same extent as if each reference were individually and specifically indicated to be incorporated by reference and were set forth in its entirety herein.
Summary
Various objects, features, and advantages of the disclosed subject matter can be more fully appreciated with reference to the following detailed description of the disclosed subject matter when considered in connection with the following drawings, in which like reference numerals identify like elements.
The present invention relates to artificial intelligence for business transformation, particularly to deep learning systems for modeling, simulating, and reinventing strategic business models across interdisciplinary domains.

The present invention discloses an artificial intelligence-based system designed to reimagine and optimize strategic business models using deep neural networks and interdisciplinary knowledge fusion. The system comprises a strategic input acquisition module that aggregates a diverse range of structured and unstructured data relevant to an enterprise's value proposition, operations, market context, and innovation history. These inputs are passed through a semantic mapping engine that creates structured, contextual representations of strategic elements. This engine translates qualitative narratives, operational metrics, and economic signals into concept graphs and embeddings for computational use.
The transformed input is processed by a neural transformation layer, which employs deep learning models to explore feasible and innovative business model configurations. These candidate models are evaluated in a decision simulation module, where stress testing, agent-based modeling, and reinforcement learning simulations are used to estimate the performance of the proposed strategies under real-world scenarios. The module considers long-term viability, adaptability to disruption, and return on transformation investments.
A dedicated interdisciplinary feedback engine interfaces with domain-specific experts, behavioral science models, and macroeconomic forecasting modules to refine model selection. This enables human-machine collaborative curation of viable business model blueprints. Once a candidate model is validated, the deployment orchestration layer converts it into a stepwise execution framework, aligned with enterprise constraints, resource availabilities, and change management strategies. The system thus supports continuous model evolution, real-time KPI monitoring, and organizational alignment, enabling enterprises to iterate strategic innovations rapidly and at scale. The invention provides a closed-loop, AI-driven mechanism for strategic renewal and business transformation.

Brief Description of the Drawings

The features and advantages of the present disclosure would be more clearly understood from the following description taken in conjunction with the accompanying drawings in which:
FIG. 1 is a system architecture diagram illustrating the modular structure of the strategic innovation management system comprising data acquisition, contextual modeling, strategic inference, decision synthesis, and adaptive feedback components.
FIG. 2 is a method flow diagram showing the operational sequence for processing data inputs through contextual modeling and neural inference to output business model configurations and perform adaptive refinement.
Detailed Description
The following is a detailed description of exemplary embodiments to illustrate the principles of the invention. The embodiments are provided to illustrate aspects of the invention, but the invention is not limited to any embodiment. The scope of the invention encompasses numerous alternatives, modifications and equivalent; it is limited only by the claims.
In view of the many possible embodiments to which the principles of the present discussion may be applied, it should be recognized that the embodiments described herein with respect to the drawing figures are meant to be illustrative only and should not be taken as limiting the scope of the claims. Therefore, the techniques as described herein contemplate all such embodiments as may come within the scope of the following claims and equivalents thereof.
The detailed description is described with reference to the accompanying figures. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. The use of the same reference numbers in different instances in the description and the figures may indicate similar or identical items.
Pursuant to the "Detailed Description" section herein, whenever an element is explicitly associated with a specific numeral for the first time, such association shall be deemed consistent and applicable throughout the entirety of the "Detailed Description" section, unless otherwise expressly stated or contradicted by the context.
The present invention relates to artificial intelligence for business transformation, particularly to deep learning systems for modeling, simulating, and reinventing strategic business models across interdisciplinary domains.

Pursuant to the "Detailed Description" section herein, whenever an element is explicitly associated with a specific numeral for the first time, such association shall be deemed consistent and applicable throughout the entirety of the "Detailed Description" section, unless otherwise expressly stated or contradicted by the context.
The disclosed invention pertains to a system and method for facilitating strategic innovation and business model reinvention using advanced artificial intelligence techniques. The system integrates neural network-based inference, contextual data modeling, and adaptive feedback learning into a unified architecture, enabling dynamic and predictive strategic decision-making within complex business environments.
The system begins with a data acquisition module designed to assimilate data from a wide variety of internal and external sources. Internally, it connects to enterprise databases including financial management systems, CRM platforms, supply chain dashboards, human capital repositories, and sales records. Externally, it extracts real-time and historical data from competitor filings, macroeconomic indicators, regulatory databases, social sentiment platforms, and innovation repositories. The acquired data is normalized, deduplicated, and transformed into structured formats suitable for downstream neural processing.
The normalized data is passed to a contextual modeling engine that constructs a multi-dimensional latent feature space. This engine includes temporal encoding layers, semantic embedding modules, and transformation matrices that capture interdependencies across strategic, operational, and external dimensions. The contextual encoder ensures that long-range historical dependencies and cross-functional correlations are retained in the feature vector representation, forming a comprehensive view of the business's internal state and external environment.
This latent feature set is then forwarded to the strategic pattern inference module. This module comprises various deep learning models such as autoencoders trained to detect structural inefficiencies, graph neural networks configured to evaluate stakeholder influence maps and resource networks, and attention-based models capable of isolating weak signals of emerging trends. The combination of these architectures enables the identification of latent innovation patterns, new value configurations, and ecosystem opportunities that may be obscured by surface-level data analysis.
The outputs of the inference module are passed into a decision synthesis engine. This engine integrates business constraints, strategic priorities, and operational thresholds to synthesize a set of viable business model configurations. Multi-objective optimization algorithms are used to identify trade-offs among financial return, innovation feasibility, and implementation risk. Each proposed configuration includes a blueprint of value proposition, key activities, revenue streams, cost structures, and channel alignments. Scenario simulation layers allow users to perturb model inputs and evaluate outcome distributions under various assumptions.
An adaptive feedback optimization layer monitors the performance of deployed strategies by collecting actualized KPIs and comparing them against predicted benchmarks. It applies reinforcement learning techniques or Bayesian performance updating to adjust neural network weights and decision synthesis logic. Over time, this enables the system to improve its strategic foresight and policy alignment, ensuring continuous relevance in volatile markets.
In a first embodiment, the system is deployed in a global retail enterprise undergoing digital transformation. The system ingests omnichannel customer data, inventory turnover rates, and logistic cost data. The inference module identifies inefficiencies in the value chain and proposes subscription-based business model variants. The feedback loop evaluates pilot performance and continuously recalibrates recommendations.
In a second embodiment, the system is used by a financial services firm assessing new market entry. Macroeconomic data, regulatory landscape, and demographic profiles are integrated. The system simulates strategic options such as joint ventures, digital-only offerings, or direct expansion, evaluating risks under geopolitical uncertainty.
In a third embodiment, the system is applied by a manufacturing company facing disruption from additive manufacturing technologies. The system processes supplier reliability scores, R&D innovation rates, and intellectual property maps to generate viable reinvention pathways including licensing models, vertical integration, and on-demand production schemes.
In all embodiments, the neural decision engine provides traceability of recommendations, enabling users to visualize the reasoning pathway, confidence levels, and projected performance bands. This transparency supports executive confidence and auditability. The disclosed invention thus transforms traditional static strategy practices into dynamic, AI-enhanced innovation systems that enable enterprise agility, resilience, and sustained competitive advantage.

FIG. 1 illustrates a system architecture for an AI-driven strategic innovation management platform configured for dynamic business model reinvention. The architecture is divided into multiple functional modules, each responsible for a distinct computational task. A data acquisition module is situated at the input interface, configured to retrieve, harmonize, and normalize structured and unstructured data from enterprise sources, including financial records, operational systems, customer interaction logs, competitor databases, and market research feeds. The normalized data flows into a contextual modeling engine comprising semantic encoders, temporal extractors, and latent space projection units. This engine encodes organizational state and environmental context into multidimensional feature vectors. These vectors are input into a strategic inference module comprising deep neural architectures such as graph neural networks, transformer encoders, and autoencoders. The inference module identifies latent innovation opportunities, inefficiencies, and capability misalignments. The resulting insights are relayed to the decision synthesis engine, which uses optimization algorithms to generate alternative business model configurations under predefined strategic objectives and constraints. Finally, an adaptive feedback layer continuously monitors real-world strategic implementation performance and adjusts model parameters, decision rules, and optimization thresholds to reflect evolving business realities and improve subsequent recommendations.
FIG. 2 presents a method flow diagram representing the sequential operations executed by the disclosed invention. The process begins with the collection of raw organizational and environmental data. This input undergoes preprocessing to resolve semantic inconsistencies and to format it for computational encoding. The data is then passed into a contextual encoding phase that performs semantic alignment, time-series embedding, and dimensionality reduction. The encoded representation is fed into a deep inference model trained to extract latent strategic signals, including unrecognized opportunity clusters, weak signal trendlines, and innovation bottlenecks. The output of the inference model proceeds to a decision synthesis phase, which formulates candidate business models aligned with firm-specific objectives. These include resource compatibility, risk exposure levels, innovation ambition, and stakeholder value optimization. After deployment of selected strategic actions, the system collects performance data, analyzes variances from predicted outcomes, and retrains the models using reinforcement signals to improve future inference precision and configuration quality. This method enables real-time, adaptive, and explainable business strategy formulation and evolution.
Further, while operations are depicted in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. In certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are contained in the above discussions, these should not be construed as limitations on the scope of the subject matter described herein, but rather as descriptions of features that may be specific to particular embodiments. Certain features that are described in this specification in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable sub-combination. Moreover, although features may be described above as acting in certain combinations and even initially claimed as such, one or more features from a claimed combination can in some cases be excised from the combination, and the claimed combination may be directed to a sub-combination or variation of a sub-combination.
The term “memory,” as used herein relates to a volatile or persistent medium, such as a magnetic disk, or optical disk, in which a computer can store data or software for any duration. Optionally, the memory is non-volatile mass storage such as physical storage media. Furthermore, a single memory may encompass and in a scenario wherein computing system is distributed, the processing, memory and/or storage capability may be distributed as well.
Throughout the present disclosure, the term ‘server’ relates to a structure and/or module that include programmable and/or non-programmable components configured to store, process and/or share information. Optionally, the server includes any arrangement of physical or virtual computational entities capable of enhancing information to perform various computational tasks.
Throughout the present disclosure, the term “network” relates to an arrangement of interconnected programmable and/or non-programmable components that are configured to facilitate data communication between one or more electronic devices and/or databases, whether available or known at the time of filing or as later developed. Furthermore, the network may include, but is not limited to, one or more peer-to-peer network, a hybrid peer-to-peer network, local area networks (LANs), radio access networks (RANs), metropolitan area networks (MANS), wide area networks (WANs), all or a portion of a public network such as the global computer network known as the Internet, a private network, a cellular network and any other communication system or systems at one or more locations.
Throughout the present disclosure, the term “process”* relates to any collection or set of instructions executable by a computer or other digital system so as to configure the computer or the digital system to perform a task that is the intent of the process.
Throughout the present disclosure, the term ‘Artificial intelligence (AI)’ as used herein relates to any mechanism or computationally intelligent system that combines knowledge, techniques, and methodologies for controlling a bot or other element within a computing environment. Furthermore, the artificial intelligence (AI) is configured to apply knowledge and that can adapt it-self and learn to do better in changing environments. Additionally, employing any computationally intelligent technique, the artificial intelligence (AI) is operable to adapt to unknown or changing environment for better performance. The artificial intelligence (AI) includes fuzzy logic engines, decision-making engines, preset targeting accuracy levels, and/or programmatically intelligent software.
Claims

I/We Claims

Claim 1
A system for business model reinvention using artificial intelligence and deep neural networks, the system comprising:
a strategic input acquisition module configured to receive multi-modal data streams, said streams including historical financial performance, customer segmentation datasets, market trend indicators, operational cost structures, and competitive intelligence;
a semantic mapping engine operatively connected to said strategic input acquisition module, said mapping engine configured to transform raw strategic data into structured concept embeddings representing business elements such as value proposition, revenue streams, key resources, and customer relationships;
a neural transformation layer coupled to said semantic mapping engine, said transformation layer comprising one or more deep neural network architectures configured to generate candidate business model configurations through multi-objective optimization across innovation, feasibility, profitability, and adaptability criteria;
a decision simulation module receiving candidate business model configurations from said neural transformation layer, said simulation module configured to evaluate alternative strategies using scenario-based reasoning, stress testing, and predictive forecasting over defined temporal intervals;
an interdisciplinary feedback engine operatively connected to said decision simulation module, said feedback engine configured to incorporate expert domain knowledge, behavioral insights, and policy constraints to refine and validate selected business model alternatives;
and a deployment orchestration layer configured to translate the selected business model structure into actionable transformation plans, said plans including resource reallocation directives, technology adoption pathways, and organizational alignment initiatives.
Claim 2
The system as claimed in claim 1, wherein said neural transformation layer includes a variational autoencoder configured to explore latent representations of successful and unsuccessful business configurations across industry benchmarks.
Claim 3
The system as claimed in claim 1, wherein said semantic mapping engine applies natural language processing techniques to extract key strategic constructs from qualitative sources including business reports, founder interviews, customer feedback, and analyst commentary.
Claim 4
The system as claimed in claim 1, wherein said decision simulation module includes a reinforcement learning sub-module configured to simulate long-term outcomes of business model shifts by approximating reward trajectories and organizational resilience metrics.
Claim 5
The system as claimed in claim 1, wherein said interdisciplinary feedback engine integrates outputs from psychological profiling engines, macroeconomic policy simulations, and design thinking heuristics to guide selection and validation.
Claim 6
The system as claimed in claim 1, wherein said deployment orchestration layer includes a prioritization engine that sequences execution steps based on resource availability, market readiness, and cultural alignment assessments.
Claim 7
The system as claimed in claim 1, wherein the system further comprises a strategic KPI monitoring interface configured to track transformation success in real-time using leading indicators mapped to the deployed business model elements.

/
DATED 07 July 2025 PALLAVI SINHA
IN/PA- 4068
AGENT FOR THE APPLICANT

Reinventing Business Models with AI and Deep Neural Networks: An Interdisciplinary Approach to Strategic Innovation Management

An AI-based strategic innovation system is disclosed for business model reinvention using deep neural network architectures. The system includes a data acquisition module for integrating enterprise and market data, a contextual modeling engine for generating latent representations, and a strategic inference module for identifying innovation patterns. A decision synthesis engine outputs optimal business model scenarios, and an adaptive feedback layer refines model parameters based on real-world outcomes. The system supports scenario simulation, closed-loop learning, and organizational transformation through data-driven strategic formulation and iterative performance alignment.

, C , Claims:I/We Claims

Claim 1
A system for business model reinvention using artificial intelligence and deep neural networks, the system comprising:
a strategic input acquisition module configured to receive multi-modal data streams, said streams including historical financial performance, customer segmentation datasets, market trend indicators, operational cost structures, and competitive intelligence;
a semantic mapping engine operatively connected to said strategic input acquisition module, said mapping engine configured to transform raw strategic data into structured concept embeddings representing business elements such as value proposition, revenue streams, key resources, and customer relationships;
a neural transformation layer coupled to said semantic mapping engine, said transformation layer comprising one or more deep neural network architectures configured to generate candidate business model configurations through multi-objective optimization across innovation, feasibility, profitability, and adaptability criteria;
a decision simulation module receiving candidate business model configurations from said neural transformation layer, said simulation module configured to evaluate alternative strategies using scenario-based reasoning, stress testing, and predictive forecasting over defined temporal intervals;
an interdisciplinary feedback engine operatively connected to said decision simulation module, said feedback engine configured to incorporate expert domain knowledge, behavioral insights, and policy constraints to refine and validate selected business model alternatives;
and a deployment orchestration layer configured to translate the selected business model structure into actionable transformation plans, said plans including resource reallocation directives, technology adoption pathways, and organizational alignment initiatives.
Claim 2
The system as claimed in claim 1, wherein said neural transformation layer includes a variational autoencoder configured to explore latent representations of successful and unsuccessful business configurations across industry benchmarks.
Claim 3
The system as claimed in claim 1, wherein said semantic mapping engine applies natural language processing techniques to extract key strategic constructs from qualitative sources including business reports, founder interviews, customer feedback, and analyst commentary.
Claim 4
The system as claimed in claim 1, wherein said decision simulation module includes a reinforcement learning sub-module configured to simulate long-term outcomes of business model shifts by approximating reward trajectories and organizational resilience metrics.
Claim 5
The system as claimed in claim 1, wherein said interdisciplinary feedback engine integrates outputs from psychological profiling engines, macroeconomic policy simulations, and design thinking heuristics to guide selection and validation.
Claim 6
The system as claimed in claim 1, wherein said deployment orchestration layer includes a prioritization engine that sequences execution steps based on resource availability, market readiness, and cultural alignment assessments.
Claim 7
The system as claimed in claim 1, wherein the system further comprises a strategic KPI monitoring interface configured to track transformation success in real-time using leading indicators mapped to the deployed business model elements.

/
DATED 07 July 2025 PALLAVI SINHA
IN/PA- 4068
AGENT FOR THE APPLICANT

Reinventing Business Models with AI and Deep Neural Networks: An Interdisciplinary Approach to Strategic Innovation Management