Description:Field of the Invention

The present invention relates to AI-based decision-making, particularly to uncertainty-aware neural frameworks for predictive modeling in dynamically evolving business environments.

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.
Decision-making in dynamic business environments remains fraught with uncertainty due to the fluctuating nature of markets, supply chains, customer behavior, and regulatory variables. Traditional enterprise forecasting systems rely heavily on deterministic models that assume stable input-output relationships, linear dependencies, or shallow learning capabilities. These approaches often neglect temporal volatility, probabilistic dependencies, and cascading effects triggered by external macroeconomic shifts or unanticipated disruptions. As a result, businesses operating under high degrees of complexity—such as multinational corporations, digital marketplaces, or logistics networks—face a critical gap between predictive analytics and real-time, actionable decision execution.
Prior art in the space of business intelligence platforms has focused primarily on data visualization dashboards, static rule engines, and basic regression-based forecasting models. While such tools provide descriptive insights or point estimates, they are unable to adequately model uncertainty, nor can they generate probabilistic forecasts or dynamically adapt to unexpected contextual changes. Machine learning enhancements have been partially explored, yet many of these systems lack embedded risk modeling or scenario-sensitive response generation. Furthermore, prior frameworks fail to incorporate continuous feedback learning loops, making them brittle under real-time operational variation.
To address these deficiencies, a need exists for a system capable of integrating diverse datasets, encoding contextual patterns, quantifying uncertainty with mathematical rigor, and recommending decisions that are optimized for probabilistic success. Such a system must continuously evolve, learn from outcome feedback, and generate interpretable, scalable, and adaptive decision policies. The present invention fills this unmet need by offering a robust neural decision framework that models future business states probabilistically and orchestrates risk-aligned actions through AI-driven policy engines.
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 AI-based decision-making, particularly to uncertainty-aware neural frameworks for predictive modeling in dynamically evolving business environments.

The present disclosure provides a neural framework for managing uncertainty in predictive decision-making within dynamic business environments. The system architecture comprises five principal components: a data aggregation module, a contextual encoding engine, a neural uncertainty quantification module, a decision orchestration layer, and an adaptive feedback mechanism. The system begins by acquiring heterogeneous data streams from operational systems, financial records, external news feeds, customer interaction platforms, and regulatory databases. This data is harmonized and temporally aligned by the contextual encoding engine, which transforms inputs into latent vectors representing multidimensional business state features.
The encoded data is then processed by a neural uncertainty quantification module, which includes Bayesian neural networks, variational autoencoders, and temporal modeling layers. These models output predictive distributions, confidence intervals, and scenario likelihoods, rather than simple point estimates. Based on these outputs, the decision orchestration layer formulates optimal action plans under probabilistic uncertainty by evaluating policy risk trade-offs, potential opportunity losses, and cost metrics. These decisions may be autonomous or routed through an optional human-in-the-loop interface.
A closed-loop adaptive learning layer observes the divergence between predicted and actual outcomes over time, using reinforcement learning, Bayesian updating, or performance-based reweighting of inputs and decisions. This enables the system to dynamically recalibrate its models and policies in response to contextual shifts or structural business changes. The system supports scenario simulation and outcome perturbation testing, enhancing foresight in volatile environments. Overall, the disclosed framework offers robust, scalable, and uncertainty-aware predictive decision-making with continuous learning and real-time policy 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 is a system architecture diagram illustrating the structural configuration of the neural decision framework, including data aggregation, contextual encoding, uncertainty quantification, decision orchestration, and adaptive feedback components.
FIG. 2 is a method flow diagram showing the operational sequence of steps for predictive modeling and decision-making under uncertainty using the neural decision framework.
FIG. 3 is a data flow diagram illustrating the directional movement and transformation of data through key modules, from data ingestion to decision execution and feedback learning.
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 AI-based decision-making, particularly to uncertainty-aware neural frameworks for predictive modeling in dynamically evolving business environments.

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 provides a modular and extensible neural framework designed to address the persistent challenge of decision-making under uncertainty in dynamic and data-intensive business settings. The system is constructed with an emphasis on probabilistic modeling, contextual feature learning, and adaptive policy optimization. It integrates multiple components that work in concert to ingest, encode, predict, decide, and learn from a continuously evolving operational landscape.
The system initiates with a data aggregation module, which serves as the foundational intake layer responsible for acquiring data from multiple enterprise systems and external sources. These inputs may include structured data from relational databases such as transactional records, key performance indicators, and resource utilization logs. It also includes unstructured or semi-structured data from market bulletins, customer reviews, financial news articles, and environmental feeds. A semantic harmonization engine within the data aggregation module assigns source reliability scores, resolves conflicting schema definitions, and temporally synchronizes asynchronous datasets to construct coherent event timelines.
The processed data is forwarded to the contextual encoding engine, which implements a deep encoding stack composed of temporal convolutional layers, transformer encoders, and multi-head attention modules. These layers extract patterns of recurrence, causal dependencies, and latent correlations from the input tensors. Historical events are assigned attention scores based on their predictive utility, enabling the network to emphasize relevant segments of data while discounting informational noise. The encoder produces a high-dimensional latent state vector that encodes a unified, time-aware representation of the evolving business environment.
This latent representation is fed into the neural uncertainty quantification module, which may be composed of Bayesian neural networks, deep ensembles, or stochastic variational inference layers. These models are trained to output predictive probability distributions over future business metrics, such as revenue projections, risk of supply disruption, churn likelihood, or capital expenditure deviations. Each prediction includes both a mean estimate and a confidence interval, allowing downstream layers to reason over multiple potential outcomes rather than a singular deterministic forecast.
The decision orchestration layer receives the forecast distributions and applies multi-objective decision logic to determine optimal courses of action. It incorporates risk weighting, utility maximization, and policy compliance constraints to synthesize actions with minimal expected regret. For example, when forecasting a likely stockout event, the orchestration layer evaluates replenishment strategies based on cost constraints, supplier lead times, and customer service level agreements, selecting the one with the highest risk-adjusted return. Where regulatory or strategic oversight is necessary, the decisions are passed to a human-in-the-loop interface, which visualizes the decision tree, confidence levels, and impact metrics for manual review and override.
Real-world outcomes are tracked and compared against model predictions using monitoring tools integrated into the adaptive feedback layer. This layer employs reinforcement learning agents or error-tracking systems to compute reward signals, which are then backpropagated into the model for parameter tuning. The model may also retrain periodically on updated datasets or respond immediately to concept drift using online learning approaches. As business dynamics shift—whether due to macroeconomic disruption, policy reform, or customer preference evolution—the system recalibrates its internal weights, re-evaluates historical dependencies, and redefines decision boundaries.
In one embodiment, the system is deployed in a financial services enterprise, where predictive models estimate credit risk across volatile portfolios. The neural module outputs probabilistic default curves, which guide credit exposure limits and interest rate adjustments in real time. In a second embodiment, the system is integrated into a global supply chain operation, forecasting delivery delays due to geopolitical instability or climate events. Scenario simulations inform logistics routing under uncertain transit conditions. In a third embodiment, the framework is embedded in a digital marketing platform that models user engagement volatility across regions, optimizing campaign spend allocation under uncertain clickthrough rate expectations.
In each use case, the architecture remains consistent: data is ingested, encoded, modeled probabilistically, and acted upon via policy engines. The system's capability to simulate future scenarios, learn from actual versus expected discrepancies, and adapt to evolving operational realities ensures superior resilience and foresight in complex business environments. This architecture creates a closed-loop, uncertainty-aware, decision intelligence platform that transforms traditional reactive enterprises into adaptive, forward-looking decision ecosystems.
FIG. 1 illustrates a high-level system architecture for the neural decision framework described herein. The system comprises several interoperable modules, each performing distinct functional roles to facilitate predictive modeling and uncertainty management in dynamic business contexts. The data aggregation module is positioned at the input stage, responsible for collecting structured and unstructured data from enterprise sources such as ERP systems, CRM platforms, transactional databases, and external information feeds. This module forwards unified data to the contextual encoding engine, which performs semantic and temporal feature extraction through neural encoding techniques. The output of the encoding engine is transmitted to the neural uncertainty quantification module, which applies Bayesian or variational deep learning architectures to generate probabilistic forecasts. These forecasts are then processed by the policy-aligned decision orchestration layer, which determines optimal business actions based on risk-weighted cost-benefit analysis. The final output is relayed to downstream operational systems via execution interfaces. An adaptive feedback layer monitors real-world outcomes and retrains models and policies in accordance with evolving business performance metrics, thus closing the learning loop.
FIG. 2 depicts a method flow diagram representing the functional steps executed by the neural decision framework in real-time or batch-mode operation. The process commences with the acquisition and harmonization of heterogeneous business data. This data is then passed through a preprocessing pipeline where missing values are imputed, and categorical variables are encoded. The preprocessed data is subsequently processed by a neural uncertainty quantification model that outputs predictive distributions for critical business KPIs. These probabilistic forecasts are interpreted by a decision orchestration unit that considers organizational constraints and utility functions to derive actionable recommendations. These actions are then operationalized in business environments, and actual outcomes are recorded. The system periodically retrains its internal models by comparing predicted and actual outcomes using reinforcement learning or other adaptive learning techniques to refine future decision quality.
FIG. 3 presents a data flow diagram showing the unidirectional and feedback flows of data through the framework. Structured data from financial systems, operational reports, and customer records flows into the data aggregation module. Simultaneously, unstructured data such as economic indicators, regulatory updates, or social sentiment is assimilated into the same pipeline. These diverse data streams are transformed into a standardized format and passed to the contextual encoder. The encoder generates a latent representation, which is supplied to the neural quantifier that produces probability-based predictions. These predictions are evaluated in a decision optimization engine and subsequently routed to action execution layers. The results of implemented actions are captured and cycled back into the learning subsystem to improve future predictions. Each transformation stage incorporates mechanisms to preserve semantic integrity, reduce noise, and enable traceable decision intelligence throughout the system lifecycle.
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

1. An artificial intelligence-based neural decision framework for managing uncertainty in dynamic business environments, the framework comprising:
a data aggregation module configured to receive, normalize, and integrate multi-source data inputs from enterprise systems, including but not limited to financial ledgers, operational logs, supply chain feeds, customer transactions, and external socioeconomic datasets;
a contextual encoding engine operatively coupled to said data aggregation module, said contextual encoding engine being configured to extract semantic and temporal features, transform heterogeneous input modalities into unified latent representations, and maintain temporal continuity across business events;
a neural uncertainty quantification module configured to receive said latent representations, said module comprising one or more deep neural architectures including probabilistic encoders and variational layers for generating predictive distributions, forecast intervals, and uncertainty scores for prospective business metrics;
a policy-aligned decision orchestration layer operatively connected to said neural uncertainty quantification module, said orchestration layer being configured to translate forecast distributions into actionable decision vectors based on predefined business goals, risk thresholds, and cost constraints;
and an adaptive feedback layer configured to update the operational weights of said neural uncertainty quantification module and decision orchestration layer in response to realized outcome discrepancies, performance deviations, and business environment changes, thereby enabling continuous alignment and learning.
2. The framework as claimed in claim 1, wherein said neural uncertainty quantification module comprises a Bayesian deep learning architecture that outputs posterior probability distributions over key future business indicators.
3. The framework as claimed in claim 1, wherein said contextual encoding engine includes a multi-head temporal attention mechanism configured to weight historical business events in accordance with their influence on projected outcomes, recency bias, and sectoral volatility.
4. The framework as claimed in claim 1, wherein said decision orchestration layer incorporates a multi-objective cost-benefit optimization algorithm, said algorithm being configured to resolve conflicts among competing decision policies through constraint relaxation and probabilistic dominance evaluation.
5. The framework as claimed in claim 1, wherein said adaptive feedback layer includes a reinforcement learning loop that modifies network parameters using reward gradients derived from post-decision success metrics.
6. The framework as claimed in claim 1, wherein said data aggregation module further comprises a data trustworthiness scoring engine configured to assign confidence weights to input records based on source reliability, anomaly detection, and temporal consistency.
7. The framework as claimed in claim 1, further comprising a scenario simulation interface configured to construct and evaluate a plurality of decision pathways, each subjected to stochastic perturbations and modeled via Monte Carlo simulations to compute expected utility profiles.
8. The framework as claimed in claim 1, wherein said neural uncertainty quantification module is trained on a combination of historical supervised datasets, synthetic perturbation datasets, and domain-specific policy archives for robust generalization under novel conditions.
9. The framework as claimed in claim 1, wherein said decision orchestration layer includes a human-in-the-loop override protocol, said protocol being configured to generate interpretable summaries of recommended decisions and accept manual approvals or rejections.
10. The framework as claimed in claim 1, wherein said system is deployed over a distributed computing infrastructure supporting elastic model scaling, cross-domain data federation, and decentralized inference optimization under high-frequency streaming workloads.

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

Managing Uncertainty with Neural Decision Frameworks: AI and ML for Predictive Modelling in Dynamic Business Environments

A neural decision framework for managing uncertainty in dynamic business environments is disclosed. The system includes a data aggregation module for integrating heterogeneous sources, a contextual encoding engine for semantic and temporal feature extraction, and a neural uncertainty quantification module that generates probabilistic forecasts. A decision orchestration layer formulates action vectors under uncertainty, guided by policy constraints and risk profiles. An adaptive learning layer continuously updates models using feedback from realized outcomes. The system supports scenario simulation, human-in-the-loop intervention, and scalable deployment for predictive business optimization.

, Claims:I/We Claims

1. An artificial intelligence-based neural decision framework for managing uncertainty in dynamic business environments, the framework comprising:
a data aggregation module configured to receive, normalize, and integrate multi-source data inputs from enterprise systems, including but not limited to financial ledgers, operational logs, supply chain feeds, customer transactions, and external socioeconomic datasets;
a contextual encoding engine operatively coupled to said data aggregation module, said contextual encoding engine being configured to extract semantic and temporal features, transform heterogeneous input modalities into unified latent representations, and maintain temporal continuity across business events;
a neural uncertainty quantification module configured to receive said latent representations, said module comprising one or more deep neural architectures including probabilistic encoders and variational layers for generating predictive distributions, forecast intervals, and uncertainty scores for prospective business metrics;
a policy-aligned decision orchestration layer operatively connected to said neural uncertainty quantification module, said orchestration layer being configured to translate forecast distributions into actionable decision vectors based on predefined business goals, risk thresholds, and cost constraints;
and an adaptive feedback layer configured to update the operational weights of said neural uncertainty quantification module and decision orchestration layer in response to realized outcome discrepancies, performance deviations, and business environment changes, thereby enabling continuous alignment and learning.
2. The framework as claimed in claim 1, wherein said neural uncertainty quantification module comprises a Bayesian deep learning architecture that outputs posterior probability distributions over key future business indicators.
3. The framework as claimed in claim 1, wherein said contextual encoding engine includes a multi-head temporal attention mechanism configured to weight historical business events in accordance with their influence on projected outcomes, recency bias, and sectoral volatility.
4. The framework as claimed in claim 1, wherein said decision orchestration layer incorporates a multi-objective cost-benefit optimization algorithm, said algorithm being configured to resolve conflicts among competing decision policies through constraint relaxation and probabilistic dominance evaluation.
5. The framework as claimed in claim 1, wherein said adaptive feedback layer includes a reinforcement learning loop that modifies network parameters using reward gradients derived from post-decision success metrics.
6. The framework as claimed in claim 1, wherein said data aggregation module further comprises a data trustworthiness scoring engine configured to assign confidence weights to input records based on source reliability, anomaly detection, and temporal consistency.
7. The framework as claimed in claim 1, further comprising a scenario simulation interface configured to construct and evaluate a plurality of decision pathways, each subjected to stochastic perturbations and modeled via Monte Carlo simulations to compute expected utility profiles.
8. The framework as claimed in claim 1, wherein said neural uncertainty quantification module is trained on a combination of historical supervised datasets, synthetic perturbation datasets, and domain-specific policy archives for robust generalization under novel conditions.
9. The framework as claimed in claim 1, wherein said decision orchestration layer includes a human-in-the-loop override protocol, said protocol being configured to generate interpretable summaries of recommended decisions and accept manual approvals or rejections.
10. The framework as claimed in claim 1, wherein said system is deployed over a distributed computing infrastructure supporting elastic model scaling, cross-domain data federation, and decentralized inference optimization under high-frequency streaming workloads.

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

Managing Uncertainty with Neural Decision Frameworks: AI and ML for Predictive Modelling in Dynamic Business Environments