Description:Field of the Invention

The present disclosure generally relates to artificial intelligence-based decision support systems. Further, the present disclosure particularly relates to a strategic planning and risk optimization system.
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.
The field of strategic management has witnessed substantial transformation due to the integration of computational tools in decision-making frameworks. Advancements in artificial intelligence and data science have enabled enterprises to analyse vast volumes of information in real time. Complex environments characterised by volatility, uncertainty, complexity, and ambiguity have necessitated enhanced systems for strategy planning and risk-based decision making. Strategic planning processes typically involve assessment of external and internal environments, formulation of objectives, and evaluation of potential actions. In recent times, systems incorporating data-driven and model-driven decision support functionalities have become increasingly prevalent across diverse industry sectors.
A commonly known technique includes the application of rule-based decision support systems that use predetermined logical conditions to generate strategic recommendations. Such systems typically rely on historical data and expert knowledge to define static decision rules for different business scenarios. Although such rule-based decision support systems are capable of handling routine decision-making tasks, major drawbacks are observed in their application to dynamic or uncertain environments. Static rule sets frequently fail to capture evolving patterns in business metrics or environmental variables, thereby reducing strategic responsiveness. Furthermore, the inability to learn or adapt to real-time feedback restricts utility in long-term planning and risk anticipation.
Another widely utilised technique involves the use of business intelligence dashboards and descriptive analytics platforms. Such platforms are structured to visualise historical performance metrics and key indicators using charts, graphs, and scorecards. Business intelligence platforms often enable detection of performance trends and anomalies based on aggregated data. However, such platforms remain restricted to descriptive analytics, without the capability to simulate strategic alternatives or project future outcomes based on complex interdependencies. In addition, manual interpretation of graphical outputs is often required, introducing subjective bias and delaying response time in critical decision-making scenarios.
Limitations associated with rule-based decision support systems and business intelligence platforms include static modelling, lack of simulation capabilities, and absence of adaptive learning from real-time feedback. Other conventional systems also exhibit drawbacks such as poor scalability, inability to process unstructured information, and inadequate integration of probabilistic risk evaluation in strategy formulation. Decision-makers frequently face challenges in balancing conflicting objectives, quantifying uncertainty, and generating optimal strategies across variable future conditions. Strategic inefficiencies and exposure to unforeseen risks persist in the absence of systems capable of multi-dimensional analysis and outcome-based scenario simulation.
In light of the above discussion, there exists an urgent need for solutions that overcome the problems associated with conventional systems and/or techniques for strategic planning and risk optimization in complex and uncertain decision environments.
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 disclosure generally relates to artificial intelligence-based decision support systems. Further, the present disclosure particularly relates to a strategic planning and risk optimization system.
An objective of the present disclosure is to enable strategic decision-making in complex environments characterised by uncertainty, variability, and multi-objective constraints. The system of the present disclosure aims to generate and evaluate multiple future scenarios, estimate associated risks, and recommend optimal strategic actions based on data-driven forecasting and simulation.
In an aspect, the present disclosure provides a strategic planning and risk optimization system comprising a data acquisition interface adapted to receive input datasets from heterogeneous sources including financial records, operational parameters, and market indicators; a feature transformation engine operatively associated with said data acquisition interface, said feature transformation engine arranged to normalize, encode, and structure said input datasets for analytical processing; a model training assembly comprising one or more machine learning and deep learning structures adapted to generate forecasting models based on said structured datasets, said forecasting models trained to estimate risk probabilities and project performance metrics under variable conditions; a simulation environment arranged to receive said forecasting models and configured to simulate a plurality of strategic scenarios using said forecasting models; an outcome evaluation unit operatively linked to said simulation environment, said outcome evaluation unit structured to assess each scenario using multi-objective criteria including risk exposure, cost impact, and goal alignment; and a decision recommendation interface configured to present strategy options ranked by calculated trade-offs derived from said outcome evaluation unit.
Furthermore, the strategic planning and risk optimization system enables integration of multiple decision dimensions and facilitates scenario-driven analysis for strategy refinement. Moreover, forecasting reliability and objective alignment are improved through real-time evaluation and simulation capabilities.
In another aspect, the data acquisition interface is further disposed to access unstructured data formats comprising textual documents, emails, and open-source reports, and includes a semantic interpreter configured to extract decision-relevant features from said unstructured data using natural language processing.
Further, such a semantic interpreter enables inclusion of qualitative insights and enhances completeness of input information for decision analysis.
In another aspect, the feature transformation engine further comprises a temporal pattern extractor disposed to identify time-dependent features such as seasonal variation, trend shifts, or cyclic behavior to improve forecasting accuracy during model training.
Moreover, said temporal pattern extractor enables dynamic trend recognition and enhances predictive performance under variable temporal patterns.
In another aspect, the model training assembly is further structured to incorporate reinforcement learning-based structures trained by iterative interaction with a simulated economic environment, such that long-term strategy optimization is facilitated.
Further, such reinforcement learning-based structures enable outcome-based learning from strategy feedback and support optimisation over extended planning horizons.
In another aspect, the simulation environment is structured to include a scenario synthesizer comprising probabilistic scenario generation techniques and stochastic sampling methods for modeling uncertain decision environments.
Moreover, said scenario synthesizer enables robust strategy testing against diverse uncertainty conditions and strengthens decision resilience.
In another aspect, the outcome evaluation unit further comprises a dynamic weighting mechanism adapted to adjust objective importance in real time based on enterprise priorities, user preferences, or regulatory thresholds.
Further, such a dynamic weighting mechanism enables adaptive decision alignment with evolving enterprise or regulatory requirements.
In another aspect, the decision recommendation interface further includes a visual analytics dashboard configured to display strategic pathways, potential risks, and statistical confidence levels using graphical metaphors and trend overlays.
Moreover, said visual analytics dashboard enables intuitive comprehension of strategy outcomes and enhances transparency in decision-making.
In another aspect, a conflict resolution arrangement is integrated within the outcome evaluation unit, said conflict resolution arrangement configured to resolve contradictory outcomes across multiple objectives using game-theoretic or Pareto-efficient filtering structures.
Further, such a conflict resolution arrangement enables balanced resolution of trade-offs and supports multi-criteria decision-making under competing priorities.
In another aspect, the forecasting models are continuously retrained using a feedback assimilation engine disposed to collect outcome deviations, prediction errors, and operational feedback after implementation of selected strategies.
Moreover, said feedback assimilation engine enables continuous improvement in model accuracy and supports adaptive learning from strategic execution outcomes.
In another aspect, the decision recommendation interface is further coupled with an execution control unit structured to transmit selected strategies into enterprise resource planning systems or management control systems for downstream operational execution.
Further, such an execution control unit enables seamless integration of strategic decisions into enterprise systems and facilitates end-to-end operational alignment.

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 illustrates a block diagram of a strategic planning and risk optimization system, in accordance with the embodiments of the present disclosure.
FIG. 2 illustrates a data flow diagram of a strategic planning and risk optimization system, in accordance with the embodiments of the present disclosure.
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 disclosure generally relates to artificial intelligence-based decision support systems. Further, the present disclosure particularly relates to a strategic planning and risk optimization system.
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 term data acquisition interface as used throughout the present disclosure relates to a system interface structured to collect or receive input datasets originating from a plurality of heterogeneous data sources. The data acquisition interface is adapted to receive structured datasets comprising financial records, operational parameters, and market indicators. In addition, the data acquisition interface is optionally structured to receive semi-structured and unstructured datasets including industry reports, free-text documents, and public databases. The data acquisition interface is operatively connected to internal enterprise data repositories, external APIs, third-party data aggregators, and IoT-based sensor platforms. The data acquisition interface is further configured to handle asynchronous data streams and batch inputs, enabling broad compatibility with various enterprise data formats. The data acquisition interface enables real-time integration of external and internal data streams into the strategic analysis framework. The completeness, accuracy, and diversity of collected data enable comprehensive situational awareness and data richness necessary for downstream transformation, modelling, and decision processes. The data acquisition interface improves the system by increasing adaptability to dynamic environments and expanding the scope of input intelligence available for risk-informed decision-making.
The term feature transformation engine as used throughout the present disclosure relates to a processing component operatively associated with the data acquisition interface and structured to convert raw data into a structured analytical format. The feature transformation engine is arranged to perform data cleaning, normalization, encoding, dimensionality reduction, and feature extraction on received datasets. The feature transformation engine is adapted to convert categorical, temporal, numerical, and text-based attributes into formats suitable for machine learning model consumption. The feature transformation engine further identifies correlations, removes redundancy, and generates derived attributes using statistical transformation and logical encoding operations. Optionally, the feature transformation engine is configured to integrate domain-specific feature engineering operations for enterprise-specific planning needs. The feature transformation engine enables a consistent, structured, and relevant dataset which forms the basis for accurate model training and simulation. Technical advantages enabled by the feature transformation engine include reduced model bias, improved input consistency, and enhanced learning quality, which collectively improve performance, robustness, and interpretability of the downstream forecasting and simulation processes.
The term model training assembly as used throughout the present disclosure relates to a processing structure comprising one or more machine learning and deep learning structures adapted to generate forecasting models based on the transformed input datasets. The model training assembly receives structured datasets from the feature transformation engine and applies supervised, unsupervised, or reinforcement learning techniques to estimate parameters, generate predictive weights, and derive functional models. The model training assembly is trained to estimate risk probabilities, predict performance metrics, and generate outcome forecasts under variable external and internal conditions. The model training assembly is optionally configured to perform hyperparameter optimization, model validation, and cross-validation during training iterations. Ensemble learning structures may be included to combine multiple models for enhanced prediction confidence. The forecasting models generated by the model training assembly form the computational basis for simulating and assessing strategy outcomes in variable scenarios. The model training assembly enables accurate and adaptive decision intelligence by learning from high-dimensional, multi-source datasets and mapping input patterns to strategic outcomes. Enhanced accuracy, pattern recognition, and scalability are technical benefits provided by the model training assembly to the overall system.
The term simulation environment as used throughout the present disclosure relates to a computational structure arranged to receive forecasting models generated by the model training assembly and configured to simulate a plurality of strategic scenarios. The simulation environment synthesizes scenario inputs based on internal policy variables, external uncertainties, and operational constraints. The simulation environment applies probabilistic models and parameter variation to examine potential outcomes resulting from different decision paths. Optionally, the simulation environment comprises a scenario synthesizer configured to generate scenario inputs using stochastic methods and probabilistic sampling. Each scenario is processed using the forecasting models to project expected consequences under simulated strategic choices. The simulation environment enables decision-makers to assess multiple plausible futures and to explore the impact of uncertainty on outcomes. The simulation environment supports comparative simulation by altering controllable variables such as budget allocation, operational resources, or investment timelines. Technical benefits enabled by the simulation environment include quantifiable scenario outcomes, reduced uncertainty in decision paths, and improved strategic readiness through predictive simulation of alternative conditions.
The term outcome evaluation unit as used throughout the present disclosure relates to an analytical component operatively linked to the simulation environment and structured to assess each simulated scenario using multi-objective evaluation criteria. The outcome evaluation unit receives scenario outputs and applies quantitative scoring mechanisms to assess outcomes based on enterprise-defined objectives. The evaluation criteria include risk exposure, cost impact, strategic goal alignment, and operational feasibility. Optionally, the outcome evaluation unit comprises a dynamic weighting mechanism adapted to alter the importance of individual objectives based on user preferences, contextual variables, or regulatory constraints. The outcome evaluation unit performs score aggregation, conflict resolution, and optimization filtering to identify strategy configurations that meet or exceed acceptable thresholds. The outcome evaluation unit enables prioritization and elimination of inferior or infeasible strategy options. The integration of objective trade-off evaluation allows for transparency and rationale-based decision selection. The outcome evaluation unit improves the system by enabling structured evaluation, supporting real-time trade-off analysis, and aligning scenario outcomes with enterprise goals and stakeholder priorities.
The term decision recommendation interface as used throughout the present disclosure relates to an interface component configured to present ranked strategic options based on calculated trade-offs derived from the outcome evaluation unit. The decision recommendation interface receives outcome scores and rankings and generates interpretable summaries for enterprise decision-makers. Optionally, the decision recommendation interface comprises a visual analytics dashboard configured to display projected performance, comparative risk indicators, and confidence intervals for each recommended strategy using graphical representations. The decision recommendation interface may further include an interaction interface structured to accept user feedback, decision preferences, or override inputs. Optionally, said decision recommendation interface is connected to enterprise systems such as ERP platforms to enable downstream execution of selected strategies. The decision recommendation interface enables efficient dissemination of strategic insights, reduces analysis overhead for decision-makers, and enhances strategy traceability through visual and documented outputs. Technical advantages include improved decision efficiency, enhanced interpretability of complex data, and seamless transition from analysis to action.
In an embodiment, the data acquisition interface is further disposed to access unstructured data formats comprising textual documents, emails, and open-source reports, and includes a semantic interpreter configured to extract decision-relevant features from said unstructured data using natural language processing. The data acquisition interface collects unstructured data from digital repositories, communication systems, publicly available databases, and enterprise document archives. The semantic interpreter is adapted to process natural language constructs, extract entities, identify relationships, and convert free-text content into structured formats usable for downstream processing. Optionally, the semantic interpreter comprises part-of-speech tagging, syntactic parsing, and domain-specific ontology matching. The semantic interpreter enables extraction of strategic indicators embedded in qualitative data, such as stakeholder sentiment, regulatory changes, or market discourse. Technical advantages include enhancement of data coverage, enrichment of input attributes, and increased contextual relevance of extracted information, thereby enabling comprehensive representation of operational and environmental factors influencing strategic decision-making.
In another embodiment, the feature transformation engine further comprises a temporal pattern extractor disposed to identify time-dependent features such as seasonal variation, trend shifts, or cyclic behavior to improve forecasting accuracy during model training. The temporal pattern extractor receives timestamped input datasets and applies decomposition techniques, sliding window analysis, and frequency-domain transformations to identify and encode temporal dynamics. Optionally, the temporal pattern extractor includes change-point detection algorithms and statistical smoothing methods to isolate structural shifts and periodic phenomena. The extracted temporal features are encoded in formats compatible with the model training assembly. The temporal pattern extractor enables accurate characterization of time-evolving variables and improves model responsiveness to seasonality, volatility, and external cycles. Technical advantages include increased model sensitivity to business cycles, improved long-term trend capture, and reduced prediction error across variable time horizons, thereby improving system performance under dynamic operational conditions.
In another embodiment, the model training assembly is further structured to incorporate reinforcement learning-based structures trained by iterative interaction with a simulated economic environment, such that long-term strategy optimization is facilitated. The reinforcement learning-based structures operate through exploration-exploitation frameworks and are trained using policy gradients, Q-learning, or actor-critic methods. The simulated economic environment supplies feedback signals to the model training assembly in response to strategy outputs, enabling dynamic learning based on scenario outcomes. The reinforcement learning structures adapt policies to maximize cumulative rewards over simulated decision horizons. Optionally, reward functions are defined using multi-objective weighting derived from the outcome evaluation unit. The reinforcement learning-based structures enable strategy refinement through repeated simulated engagement and support convergence to optimal long-term policies. Technical advantages include improved model generalization, increased decision robustness under uncertainty, and enhanced alignment of strategic outputs with enterprise objectives over extended temporal scales.
In another embodiment, the simulation environment is structured to include a scenario synthesizer comprising probabilistic scenario generation techniques and stochastic sampling methods for modeling uncertain decision environments. The scenario synthesizer generates a distribution of plausible decision environments by sampling input variables based on historical volatility, expert-defined ranges, and uncertainty distributions. Scenario generation techniques include Monte Carlo simulations, Bayesian networks, and copula-based dependency modeling. Optionally, the scenario synthesizer is coupled with external simulation engines or synthetic data generators. The generated scenarios are processed using the forecasting models to evaluate system behavior across alternative realities. The scenario synthesizer enables exploration of extreme cases, black swan events, and risk-sensitive transitions. Technical advantages include strengthened contingency planning, enhanced system readiness under unexpected conditions, and support for resilient strategy development in complex and uncertain operational contexts.
In another embodiment, the outcome evaluation unit further comprises a dynamic weighting mechanism adapted to adjust objective importance in real time based on enterprise priorities, user preferences, or regulatory thresholds. The dynamic weighting mechanism is configured to receive external triggers, user input, or policy changes and to dynamically update the importance coefficients assigned to evaluation criteria. The weighting mechanism is optionally rule-based, data-driven, or hybrid in nature and may incorporate decision trees, weight optimization models, or learning-based adaptation strategies. The dynamic weighting mechanism ensures that strategy assessments remain context-sensitive and adaptable to shifting stakeholder requirements. The outcome evaluation unit thereby enables flexible prioritization of evaluation metrics without requiring system reconfiguration. Technical advantages include adaptive scoring, context-aligned decision outcomes, and enhanced responsiveness to stakeholder interests and regulatory compliance requirements.
In another embodiment, the decision recommendation interface further includes a visual analytics dashboard configured to display strategic pathways, potential risks, and statistical confidence levels using graphical metaphors and trend overlays. The visual analytics dashboard presents comparative outputs of evaluated strategies using interactive visuals such as heatmaps, bar charts, scenario trees, and temporal plots. The visual analytics dashboard is optionally structured to enable user filtering, sensitivity analysis, and confidence interval adjustment. The interface may integrate key performance indicators, threshold markers, and deviation tracking for each alternative. The visual analytics dashboard enhances interpretability of complex data outputs and facilitates rapid decision-making. Technical advantages include reduced cognitive load, intuitive navigation of multidimensional outcomes, and improved transparency in strategy selection and justification.
In another embodiment, a conflict resolution arrangement is integrated within the outcome evaluation unit, said conflict resolution arrangement configured to resolve contradictory outcomes across multiple objectives using game-theoretic or Pareto-efficient filtering structures. The conflict resolution arrangement processes strategy evaluations that exhibit non-dominance or trade-off inversions among competing objectives. Game-theoretic techniques include Nash equilibrium modeling, cooperative negotiation frameworks, or multi-agent decision strategies. Pareto-efficient filtering eliminates suboptimal options and retains only those strategies that cannot be improved in one criterion without degrading another. Optionally, utility-based ranking or stakeholder-weighted compromise scoring may be employed. The conflict resolution arrangement enables equitable and rational selection among conflicting strategic alternatives. Technical advantages include preservation of decision fairness, systematic elimination of inferior choices, and facilitation of compromise in multi-stakeholder decision contexts.
In another embodiment, the forecasting models are continuously retrained using a feedback assimilation engine disposed to collect outcome deviations, prediction errors, and operational feedback after implementation of selected strategies. The feedback assimilation engine receives real-world performance data following strategy execution and compares observed outcomes to forecasted expectations. The engine processes errors and deviations and updates model parameters through incremental or batch learning techniques. Optionally, the feedback assimilation engine includes drift detection, anomaly tagging, and model recalibration subsystems. Continuous retraining ensures that the forecasting models remain current with evolving operational and environmental patterns. The feedback assimilation engine improves forecast precision, mitigates model degradation, and supports continual system adaptation to real-world dynamics. Technical advantages include improved forecasting reliability, reduced long-term prediction bias, and self-correcting model performance under live operating conditions.
In another embodiment, the decision recommendation interface is further coupled with an execution control unit structured to transmit selected strategies into enterprise resource planning systems or management control systems for downstream operational execution. The execution control unit interfaces with existing enterprise infrastructure using standard communication protocols or API-based integration. The unit formats strategic recommendations into executable task sequences and transmits actionable commands to relevant enterprise systems such as workflow managers, scheduling engines, or resource allocators. Optionally, execution verification and audit tracking functionalities are included. The execution control unit enables seamless translation of strategic insights into operational action. Technical advantages include reduced latency between planning and execution, improved alignment between strategy and implementation, and enhanced system-wide coherence across planning and operational domains. In an embodiment, said data acquisition interface is adapted to receive structured and unstructured datasets from heterogeneous sources, including financial records, operational parameters, market indicators, textual documents, and emails. Said data acquisition interface is further configured with a semantic interpreter capable of extracting decision-relevant features from natural language content using natural language processing. Such semantic interpretation transforms qualitative and informal data into structured analytical inputs, increasing the variety and contextual depth of information available for downstream processing. Inclusion of unstructured data sources enhances input completeness and provides access to early indicators embedded in textual content such as regulatory changes, market sentiment, or stakeholder communication. This results in broader situational awareness and more context-sensitive forecasting and strategy generation.
In an embodiment, said feature transformation engine comprises a temporal pattern extractor disposed to derive time-dependent features such as seasonal variation, trend shifts, or cyclic behavior from structured input datasets. Said temporal pattern extractor supports generation of features that capture the dynamics of operational or financial performance over time. Forecasting models trained on temporally enriched datasets show improved capacity to anticipate future conditions influenced by historical patterns. The extraction of temporal dependencies contributes to more accurate forecasts, particularly in environments subject to seasonal cycles, periodic resource constraints, or market volatility. As a result, strategy simulations reflect time-based risk factors more accurately, leading to more informed planning decisions.
In an embodiment, said model training assembly is structured to incorporate reinforcement learning-based structures trained through iterative interaction with a simulated economic environment. Said reinforcement learning structures adapt strategic decision policies by learning from repeated trial-outcome sequences and optimizing based on cumulative return. Strategies are refined by evaluating actions across multiple simulated conditions and by adjusting decisions to maximize long-term benefit rather than immediate performance. This learning process produces outcomes that reflect delayed effects of decisions and supports formulation of strategies that perform reliably under extended time horizons and uncertain environments. Integration of reinforcement learning thus contributes to improved policy stability and adaptability across complex planning scenarios.
In an embodiment, said simulation environment includes a scenario synthesizer comprising probabilistic scenario generation techniques and stochastic sampling methods. Said scenario synthesizer generates alternative decision environments by simulating input variability and modeling uncertain conditions. Such scenario diversity enables exploration of both common and extreme operational cases, improving the robustness of strategy assessment. Forecasting models are applied across synthesized conditions to evaluate how strategies perform under a wide range of assumptions and stressors. Use of probabilistic modeling and random sampling increases the range of conditions tested, contributing to more resilient and comprehensive evaluation of decision alternatives.
In an embodiment, said outcome evaluation unit comprises a dynamic weighting mechanism adapted to adjust the importance assigned to individual decision criteria in real time. Said weighting mechanism receives updates based on enterprise goals, user-defined preferences, or evolving regulatory constraints and recalibrates objective functions accordingly. By modifying relative weightings of risk, cost, and goal alignment, said evaluation unit supports strategy rankings that remain aligned with changing priorities. This configuration enables responsive adaptation of decision outputs and supports continuous alignment of strategy assessments with operational requirements and stakeholder expectations.
In an embodiment, said decision recommendation interface includes a visual analytics dashboard configured to present strategy pathways, risk exposure, and statistical confidence using graphical representations and overlays. Said dashboard enables stakeholders to interact with, compare, and interpret strategy outcomes without reliance on manual data extraction or interpretation. Graphical metaphors such as heatmaps, trend plots, and decision trees support clear representation of complex evaluation outputs. This facilitates understanding of trade-offs, identification of risk areas, and prioritization of feasible options based on comparative performance.
In an embodiment, said outcome evaluation unit includes a conflict resolution arrangement configured to resolve contradictory assessments of strategies based on multi-objective scoring. Said arrangement applies game-theoretic methods and Pareto-efficient filtering techniques to identify strategy alternatives that offer optimal compromises across conflicting priorities. Strategies that do not meet efficiency or fairness criteria are eliminated, while balanced options are elevated for recommendation. Integration of conflict resolution supports coherent decision-making where multiple criteria compete, enabling generation of ranked outcomes that reflect the most balanced trade-off positions.
In an embodiment, said forecasting models are continuously retrained using a feedback assimilation engine structured to collect execution outcomes, including performance deviations and prediction errors. Said engine processes operational feedback after implementation of recommended strategies and incrementally updates model parameters to improve alignment with real-world data. Ongoing retraining using live performance metrics maintains relevance of forecasting outputs and reduces degradation caused by changing external conditions. This configuration enables continuous learning and adjustment of predictive structures, resulting in increased stability and improved accuracy in subsequent planning cycles.
In an embodiment, said decision recommendation interface is coupled with an execution control unit structured to transmit selected strategies into enterprise resource planning systems or control platforms. Said execution control unit converts strategic outputs into system-executable formats, triggering downstream implementation processes in relevant enterprise functions. Integration with planning infrastructure supports immediate translation of analysis into action and reduces delays associated with manual decision transfer. Direct execution from strategic recommendation improves coordination across business units and enables consistent alignment of planning and operational workflows.
FIG. 2 illustrates a data flow diagram of a strategic planning and risk optimization system, in accordance with the embodiments of the present disclosure. The data flow diagram comprises external sources, process blocks, and data stores. The external sources include financial records, operational parameters, market indicators, and unstructured inputs, all of which are received by a data acquisition interface. The process blocks represent sequential components including the feature transformation engine, the model training assembly, the simulation environment, the outcome evaluation unit, the decision recommendation interface, the execution control system, and the feedback assimilation engine. Each process receives, transforms, or evaluates information before passing the data to subsequent blocks. The data stores comprise a structured data store, a forecast model store, a scenario outcomes store, and an operational feedback store. Said data flow diagram depicts the logical movement of information between external inputs, processing components, and storage units, thereby enabling strategic scenario simulation, risk evaluation, and data-informed decision recommendation based on real-time and historical enterprise data.
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 operab

I/We Claims

1. A strategic planning and risk optimization system comprising a data acquisition interface adapted to receive input datasets from heterogeneous sources including financial records, operational parameters, and market indicators; a feature transformation engine operatively associated with said data acquisition interface, said feature transformation engine arranged to normalize, encode, and structure said input datasets for analytical processing; a model training assembly comprising one or more machine learning and deep learning structures adapted to generate forecasting models based on said structured datasets, said forecasting models trained to estimate risk probabilities and project performance metrics under variable conditions; a simulation environment arranged to receive said forecasting models and configured to simulate a plurality of strategic scenarios using said forecasting models; an outcome evaluation unit operatively linked to said simulation environment, said outcome evaluation unit structured to assess each scenario using multi-objective criteria including risk exposure, cost impact, and goal alignment; and a decision recommendation interface configured to present strategy options ranked by calculated trade-offs derived from said outcome evaluation unit.
2. The system as claimed in claim 1, wherein said data acquisition interface is further disposed to access unstructured data formats comprising textual documents, emails, and open-source reports, and includes a semantic interpreter configured to extract decision-relevant features from said unstructured data using natural language processing.
3. The system as claimed in claim 1, wherein said feature transformation engine further comprises a temporal pattern extractor disposed to identify time-dependent features such as seasonal variation, trend shifts, or cyclic behavior to improve forecasting accuracy during model training.
4.The system as claimed in claim 1, wherein said model training assembly is further structured to incorporate reinforcement learning-based structures trained by iterative interaction with a simulated economic environment, such that long-term strategy optimization is facilitated.
5.The system as claimed in claim 1, wherein said simulation environment is structured to include a scenario synthesizer comprising probabilistic scenario generation techniques and stochastic sampling methods for modeling uncertain decision environments.
6. The system as claimed in claim 1, wherein said outcome evaluation unit further comprises a dynamic weighting mechanism adapted to adjust objective importance in real time based on enterprise priorities, user preferences, or regulatory thresholds.
7. The system as claimed in claim 1, wherein said decision recommendation interface further includes a visual analytics dashboard configured to display strategic pathways, potential risks, and statistical confidence levels using graphical metaphors and trend overlays.
8. The system as claimed in claim 1, wherein a conflict resolution arrangement is integrated within said outcome evaluation unit, said conflict resolution arrangement configured to resolve contradictory outcomes across multiple objectives using game-theoretic or Pareto-efficient filtering structures.
9. The system as claimed in claim 1, wherein said forecasting models are continuously retrained using a feedback assimilation engine disposed to collect outcome deviations, prediction errors, and operational feedback after implementation of selected strategies.
10. The system as claimed in claim 1, wherein said decision recommendation interface is further coupled with an execution control unit structured to transmit selected strategies into enterprise resource planning systems or management control systems for downstream operational execution.

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

The Synergy of AI and Management Science in Complex Environments: ML and DL Tools for Strategic Planning and Risk Optimization

The present disclosure provides a strategic planning and risk optimization system comprising a data acquisition interface adapted to receive input datasets from heterogeneous sources including financial records, operational parameters, and market indicators; a feature transformation engine operatively associated with said data acquisition interface, said feature transformation engine arranged to normalize, encode, and structure said input datasets for analytical processing; a model training assembly comprising one or more machine learning and deep learning structures adapted to generate forecasting models based on said structured datasets, said forecasting models trained to estimate risk probabilities and project performance metrics under variable conditions; a simulation environment arranged to receive said forecasting models and configured to simulate a plurality of strategic scenarios using said forecasting models; an outcome evaluation unit operatively linked to said simulation environment, said outcome evaluation unit structured to assess each scenario using multi-objective criteria; and a decision recommendation interface configured to present ranked strategy options.

, Claims:I/We Claims

1. A strategic planning and risk optimization system comprising a data acquisition interface adapted to receive input datasets from heterogeneous sources including financial records, operational parameters, and market indicators; a feature transformation engine operatively associated with said data acquisition interface, said feature transformation engine arranged to normalize, encode, and structure said input datasets for analytical processing; a model training assembly comprising one or more machine learning and deep learning structures adapted to generate forecasting models based on said structured datasets, said forecasting models trained to estimate risk probabilities and project performance metrics under variable conditions; a simulation environment arranged to receive said forecasting models and configured to simulate a plurality of strategic scenarios using said forecasting models; an outcome evaluation unit operatively linked to said simulation environment, said outcome evaluation unit structured to assess each scenario using multi-objective criteria including risk exposure, cost impact, and goal alignment; and a decision recommendation interface configured to present strategy options ranked by calculated trade-offs derived from said outcome evaluation unit.
2. The system as claimed in claim 1, wherein said data acquisition interface is further disposed to access unstructured data formats comprising textual documents, emails, and open-source reports, and includes a semantic interpreter configured to extract decision-relevant features from said unstructured data using natural language processing.
3. The system as claimed in claim 1, wherein said feature transformation engine further comprises a temporal pattern extractor disposed to identify time-dependent features such as seasonal variation, trend shifts, or cyclic behavior to improve forecasting accuracy during model training.
4.The system as claimed in claim 1, wherein said model training assembly is further structured to incorporate reinforcement learning-based structures trained by iterative interaction with a simulated economic environment, such that long-term strategy optimization is facilitated.
5.The system as claimed in claim 1, wherein said simulation environment is structured to include a scenario synthesizer comprising probabilistic scenario generation techniques and stochastic sampling methods for modeling uncertain decision environments.
6. The system as claimed in claim 1, wherein said outcome evaluation unit further comprises a dynamic weighting mechanism adapted to adjust objective importance in real time based on enterprise priorities, user preferences, or regulatory thresholds.
7. The system as claimed in claim 1, wherein said decision recommendation interface further includes a visual analytics dashboard configured to display strategic pathways, potential risks, and statistical confidence levels using graphical metaphors and trend overlays.
8. The system as claimed in claim 1, wherein a conflict resolution arrangement is integrated within said outcome evaluation unit, said conflict resolution arrangement configured to resolve contradictory outcomes across multiple objectives using game-theoretic or Pareto-efficient filtering structures.
9. The system as claimed in claim 1, wherein said forecasting models are continuously retrained using a feedback assimilation engine disposed to collect outcome deviations, prediction errors, and operational feedback after implementation of selected strategies.
10. The system as claimed in claim 1, wherein said decision recommendation interface is further coupled with an execution control unit structured to transmit selected strategies into enterprise resource planning systems or management control systems for downstream operational execution.

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

The Synergy of AI and Management Science in Complex Environments: ML and DL Tools for Strategic Planning and Risk Optimization