Description:Field of the Invention

The invention relates to AI-enabled enterprise systems, specifically to neural network-based platforms for continuous improvement in business process performance and workflow efficiency.

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.
Traditional methods for improving organizational performance and business processes rely on static analysis techniques, manual audits, and periodic performance reviews. Such approaches are inherently reactive and typically focus on post-hoc diagnostics rather than predictive or prescriptive intelligence. These methods often require significant human intervention, leading to subjective interpretations, inconsistent outcomes, and prolonged change cycles. Existing tools such as Lean Six Sigma, business process reengineering, and key performance indicator dashboards, while conceptually robust, are constrained by their limited ability to process real-time data streams or adapt to evolving organizational contexts.
While conventional enterprise resource planning (ERP) and workflow management systems offer performance logging and visualization, they are not equipped with advanced analytics capabilities capable of inferring causality, detecting latent inefficiencies, or prescribing optimization strategies. The emergence of robotic process automation has partially addressed execution efficiency, yet lacks strategic decision-making intelligence or predictive foresight. Furthermore, most existing tools do not integrate feedback loops or learning mechanisms that dynamically adjust recommendations based on observed outcomes.
Recent advances in artificial intelligence, particularly deep learning and neural network architectures, offer significant potential to address these limitations. However, the application of such techniques within enterprise process improvement remains fragmented, with limited systemic integration across data, models, and decision logic. There exists a technological gap between isolated machine learning models and a full-stack AI system capable of end-to-end performance analysis, intervention synthesis, and iterative learning. The present invention addresses this gap by introducing a neural framework that continuously evaluates business processes, predicts inefficiencies, prescribes corrective actions, and refines its recommendations based on real-world feedback.
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 invention relates to AI-enabled enterprise systems, specifically to neural network-based platforms for continuous improvement in business process performance and workflow efficiency.

The disclosed invention provides a neural network-based system for continuous business process improvement in enterprise environments. The system comprises five integrated modules that collectively enable real-time performance analysis, predictive diagnostics, prescriptive intervention synthesis, and feedback-driven model refinement. The data acquisition module ingests performance-related data from internal enterprise platforms and external sources. These include ERP logs, HR analytics, CRM updates, user feedback, and market intelligence. The collected data is preprocessed and structured into event logs and feature maps that feed into the process modeling engine.
The process modeling engine constructs abstract representations of workflows using dependency graphs, task sequences, and resource allocation maps. It applies process mining techniques to infer operational structures and identify task relationships. These representations are input into the neural performance inference module, which is composed of deep learning layers capable of analyzing historical and real-time data to predict bottlenecks, detect inefficiencies, and recognize compliance violations. The neural module incorporates convolutional and recurrent layers to model both structural and temporal process characteristics.
Based on inference outputs, the prescriptive recommendation engine generates actionable strategies tailored to organizational goals. These may include task reassignment, policy modification, automation triggers, or new workflow configurations. The recommendation engine uses multi-objective optimization algorithms to balance competing objectives such as cost, cycle time, workload distribution, and risk mitigation. The proposed interventions are optionally simulated to predict impact before deployment.
A feedback optimization layer closes the loop by monitoring post-intervention performance. It captures actual versus predicted outcomes, quantifies deviation magnitudes, and uses reinforcement learning to refine model parameters and decision weights. Over successive cycles, this adaptive learning process improves the precision and utility of recommendations, enabling continuous performance enhancement. The system is designed for scalable deployment across cloud or hybrid infrastructures, allowing secure, enterprise-wide integration and real-time AI-driven 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 deployment architecture diagram illustrating how the AI-based system for continuous business process improvement is integrated across enterprise infrastructure, including data sources, processing layers, and feedback control loops deployed over distributed cloud components.
FIG. 2 is a neural network model diagram illustrating the internal architecture of the neural performance inference module, detailing the use of convolutional and recurrent layers, anomaly detection units, and output mapping for efficiency prediction and deviation analysis.
FIG. 3 is a method flow diagram showing the end-to-end operational flow of the system, from data acquisition through contextual modeling, neural inference, prescriptive generation, and feedback-based 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 invention relates to AI-enabled enterprise systems, specifically to neural network-based platforms for continuous improvement in business process performance and workflow efficiency.

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 discloses an artificial intelligence-powered platform that facilitates the continuous improvement of business processes within enterprise environments using a neural network-based architecture. The system integrates performance monitoring, predictive diagnostics, prescriptive optimization, and adaptive feedback learning into a closed-loop operational framework capable of real-time analysis and strategic intervention.
The system begins with a data acquisition module configured to collect and harmonize data from multiple sources within and outside the organization. Internally, it integrates with enterprise resource planning (ERP) systems, customer relationship management (CRM) software, human capital management platforms, and workflow orchestration tools. Externally, it may access industry benchmark databases, compliance repositories, and sentiment analysis feeds derived from employee surveys and user-generated content. The module employs schema alignment, metadata tagging, and temporal alignment techniques to normalize incoming data streams for downstream processing.
The preprocessed data is routed to a process modeling engine designed to abstract organizational workflows into structured models. This engine constructs dependency graphs, event log sequences, and resource-task matrices to capture the flow and structure of operational processes. Process mining techniques, including probabilistic clustering, sequence alignment, and transition probability estimation, are employed to derive accurate process representations from historical event logs. These models serve as the foundation for performance evaluation and anomaly detection.
These abstract models are then ingested by a neural performance inference module comprising layered neural architectures. The module includes convolutional neural networks (CNNs) for pattern recognition across spatial process dimensions and recurrent neural networks (RNNs), particularly LSTM cells, for capturing temporal dependencies in process evolution. The combined architecture is trained on historical workflow data to detect inefficiencies, forecast bottlenecks, and identify latent performance risks. An anomaly detection sub-module identifies emergent process deviations and flags unusual execution patterns for further analysis.
The outputs from the neural inference layer are forwarded to the prescriptive recommendation engine. This module applies multi-objective optimization logic to synthesize actionable interventions aligned with organizational goals and constraints. It evaluates proposed changes—such as task reassignment, policy updates, or resource realignment—against objectives like cost containment, compliance assurance, cycle time reduction, and stakeholder impact mitigation. Where applicable, the engine simulates the projected impact of these interventions under varying scenarios using stochastic process models before generating actionable strategies.
The execution of selected interventions is tracked by the closed-loop feedback optimization layer, which monitors actual business performance metrics post-implementation. This module computes the delta between forecasted and observed outcomes, applies error-gradient analysis, and updates neural weights using reinforcement learning algorithms. The retraining cycle ensures the system evolves with changing organizational dynamics, refining both inference accuracy and prescriptive precision over time. This adaptive functionality enables continuous learning and improvement without requiring manual recalibration.
In a first embodiment, the system is deployed in a logistics firm to monitor fleet routing efficiency, identify route congestion patterns, and recommend re-routing or reallocation of delivery slots to improve turnaround times. In a second embodiment, the system supports a financial services organization in monitoring transaction approval workflows, detecting fraudulent activity patterns, and optimizing compliance review pipelines. In a third embodiment, the system is used within a manufacturing enterprise to evaluate assembly line performance, detect idle time segments, and recommend automated sequencing adjustments to improve throughput.
Each embodiment benefits from the closed-loop learning cycle, wherein system-generated recommendations are validated by real-world performance outcomes and used to update model inference rules and recommendation strategies. The system may be deployed on cloud-native infrastructures to enable parallel inference across business units, with federated learning support to preserve data locality and compliance integrity. By unifying data ingestion, contextual modeling, predictive diagnostics, prescriptive intervention, and adaptive refinement, the disclosed system facilitates a robust framework for real-time, AI-driven business process improvement across industries.
FIG. 1 presents a deployment architecture diagram for the artificial intelligence-based system configured for continuous business process improvement across an enterprise environment. The system architecture is modular and horizontally scalable, supporting distributed data ingestion, real-time model inference, and enterprise-wide deployment. At the foundational level, the system interfaces with a range of enterprise data sources, including ERP databases, CRM systems, HCM platforms, document repositories, and external benchmarking services. These data sources are channeled into a distributed data acquisition layer deployed across hybrid cloud environments, where data harmonization, normalization, and semantic tagging are conducted in parallel.
The harmonized data is processed within a containerized process modeling engine that resides on scalable compute clusters. This engine transforms the raw data streams into formal workflow models through process mining and sequence encoding. The resulting process representations are forwarded to a neural inference layer hosted on GPU-accelerated nodes. Here, high-throughput inference is performed using pre-trained deep learning models, which analyze the encoded workflows and generate predictions regarding bottlenecks, compliance deviations, and performance gaps.
In the upper tier, a prescriptive optimization engine is deployed within the enterprise service bus or orchestration framework, where it synthesizes corrective recommendations and communicates them to operational dashboards and middleware interfaces for intervention deployment. The feedback optimization subsystem operates asynchronously to monitor execution data and retrain the neural models based on outcome deviations, leveraging federated learning mechanisms to preserve data locality and minimize compliance risks. This distributed deployment supports real-time intelligence, continuous learning, and seamless integration with enterprise ecosystems.
FIG. 2 depicts the neural network model diagram of the neural performance inference module within the disclosed system. The architecture is designed to process structured and semi-structured representations of business process logs, task sequences, and resource allocations. Input data is passed through an initial embedding layer that maps event-level attributes such as task type, performer ID, timestamp, and resource cost into dense vector representations. These vectors are processed by a convolutional block that identifies local execution patterns and transition anomalies across tasks.
The output of the convolutional layer is fed into a bi-directional recurrent network composed of long short-term memory (LSTM) cells. This layer models the temporal dependencies and forward-backward relations between process events, capturing sequential inefficiencies and compliance lags. A contextual attention mechanism follows the recurrent layer, assigning variable weight to features based on their relevance to outcome prediction. The resulting feature maps are passed through an anomaly detection unit using a variational autoencoder to flag deviations from known patterns.
Finally, the output is passed through fully connected layers to generate structured predictions including probability of task delay, risk of process failure, and compliance deviation scores. These predictions serve as key input signals to the prescriptive recommendation engine. The model is trained using a hybrid loss function that balances reconstruction error, prediction accuracy, and anomaly classification confidence, ensuring robust performance across unseen business conditions.
FIG. 3 shows a method flow diagram illustrating the operational execution of the disclosed system. The process initiates with enterprise data ingestion, where structured logs and free-text records are pulled from internal and external systems. The data is processed through semantic aligners and formatted into temporal event logs. These logs are parsed by the process modeling engine, which constructs structured process models through mining and dependency graph generation.
The process models are passed to the neural inference engine, where predictions regarding performance inefficiencies, timing anomalies, and risk forecasts are generated using the aforementioned neural architecture. These outputs are then used by the prescriptive recommendation engine to create intervention strategies. The system evaluates these interventions against multiple objectives including throughput improvement, compliance alignment, and workload balance.
Selected strategies are deployed via orchestration systems and monitored for efficacy. Execution results are collected and compared against predicted benchmarks. Deviations are quantified and used to update the underlying model weights using a reinforcement learning loop, ensuring that future predictions and prescriptions improve in precision and alignment with organizational goals. The method is designed to repeat continuously, enabling a self-correcting, intelligent performance improvement system.
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.
An artificial intelligence-based system for continuous business process improvement using a neural network framework, the system comprising:
a data acquisition module configured to collect structured and unstructured organizational performance data from internal systems including enterprise resource planning platforms, customer relationship management systems, human capital management systems, and external business intelligence feeds;
a process modeling engine operatively linked to said data acquisition module, said engine being configured to generate abstract process representations, dependency graphs, and temporal event logs that capture the structure and dynamics of enterprise workflows;
a neural performance inference module configured to receive process models and associated performance indicators, said module comprising deep neural architectures trained to identify process inefficiencies, bottlenecks, compliance deviations, and latent improvement opportunities based on historical and real-time inputs;
a prescriptive recommendation engine operatively connected to said neural performance inference module, said recommendation engine being configured to synthesize actionable process interventions including task reassignment, workflow reconfiguration, resource reallocation, and procedural modification based on optimization goals and business constraints;
and a closed-loop feedback optimization layer configured to monitor post-intervention outcomes, measure improvement metrics, and dynamically update model parameters and decision logic to refine future recommendations.
Claim 2.
The system of claim 1, wherein said neural performance inference module comprises a combination of convolutional neural networks and recurrent neural networks trained on historical process execution data to capture both spatial dependencies and temporal evolution of workflow patterns.
Claim 3.
The system of claim 1, wherein said data acquisition module further comprises a natural language processing unit configured to extract key performance indicators, compliance rules, and sentiment metrics from policy documents, audit reports, and user feedback data.
Claim 4.
The system of claim 1, wherein said prescriptive recommendation engine includes a multi-objective optimizer that balances trade-offs among cost reduction, cycle time improvement, compliance adherence, and employee workload equity.
Claim 5.
The system of claim 1, wherein said process modeling engine includes a process mining layer configured to extract process models from event logs using sequence alignment and probabilistic clustering algorithms.
Claim 6.
The system of claim 1, wherein said neural performance inference module includes an anomaly detection submodule trained to identify emerging process deviations not present in the training data distribution.
Claim 7.
The system of claim 1, wherein said feedback optimization layer applies reinforcement learning techniques to tune recommendation logic based on observed KPI improvement deltas and deviation penalties.
Claim 8.
The system of claim 1, wherein said recommendation engine includes a simulation interface to evaluate the expected impact of proposed interventions under variable business conditions using stochastic process models.
Claim 9.
The system of claim 1, wherein said data acquisition module integrates with external benchmarking databases to contextualize performance indicators against industry standards and competitor baselines.
Claim 10.
The system of claim 1, wherein said neural network framework is deployed on a distributed cloud infrastructure configured to enable scalable model training, real-time inference, and secure enterprise-wide integration.

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

Transforming Organizational Performance with AI and ML: A Neural Network Framework for Continuous Business Process Improvement

An AI-based system for continuous business process improvement is disclosed, comprising a data acquisition module, process modeling engine, neural inference module, prescriptive recommendation engine, and feedback optimization layer. The system collects internal and external organizational data, constructs process models, applies deep learning to detect inefficiencies, and generates optimization strategies. A feedback loop monitors implementation results and refines model parameters for adaptive improvement. The invention enables predictive, prescriptive, and iterative enhancement of enterprise workflow performance.

, Claims:I/We Claims

Claim 1.
An artificial intelligence-based system for continuous business process improvement using a neural network framework, the system comprising:
a data acquisition module configured to collect structured and unstructured organizational performance data from internal systems including enterprise resource planning platforms, customer relationship management systems, human capital management systems, and external business intelligence feeds;
a process modeling engine operatively linked to said data acquisition module, said engine being configured to generate abstract process representations, dependency graphs, and temporal event logs that capture the structure and dynamics of enterprise workflows;
a neural performance inference module configured to receive process models and associated performance indicators, said module comprising deep neural architectures trained to identify process inefficiencies, bottlenecks, compliance deviations, and latent improvement opportunities based on historical and real-time inputs;
a prescriptive recommendation engine operatively connected to said neural performance inference module, said recommendation engine being configured to synthesize actionable process interventions including task reassignment, workflow reconfiguration, resource reallocation, and procedural modification based on optimization goals and business constraints;
and a closed-loop feedback optimization layer configured to monitor post-intervention outcomes, measure improvement metrics, and dynamically update model parameters and decision logic to refine future recommendations.
Claim 2.
The system of claim 1, wherein said neural performance inference module comprises a combination of convolutional neural networks and recurrent neural networks trained on historical process execution data to capture both spatial dependencies and temporal evolution of workflow patterns.
Claim 3.
The system of claim 1, wherein said data acquisition module further comprises a natural language processing unit configured to extract key performance indicators, compliance rules, and sentiment metrics from policy documents, audit reports, and user feedback data.
Claim 4.
The system of claim 1, wherein said prescriptive recommendation engine includes a multi-objective optimizer that balances trade-offs among cost reduction, cycle time improvement, compliance adherence, and employee workload equity.
Claim 5.
The system of claim 1, wherein said process modeling engine includes a process mining layer configured to extract process models from event logs using sequence alignment and probabilistic clustering algorithms.
Claim 6.
The system of claim 1, wherein said neural performance inference module includes an anomaly detection submodule trained to identify emerging process deviations not present in the training data distribution.
Claim 7.
The system of claim 1, wherein said feedback optimization layer applies reinforcement learning techniques to tune recommendation logic based on observed KPI improvement deltas and deviation penalties.
Claim 8.
The system of claim 1, wherein said recommendation engine includes a simulation interface to evaluate the expected impact of proposed interventions under variable business conditions using stochastic process models.
Claim 9.
The system of claim 1, wherein said data acquisition module integrates with external benchmarking databases to contextualize performance indicators against industry standards and competitor baselines.
Claim 10.
The system of claim 1, wherein said neural network framework is deployed on a distributed cloud infrastructure configured to enable scalable model training, real-time inference, and secure enterprise-wide integration.

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

Transforming Organizational Performance with AI and ML: A Neural Network Framework for Continuous Business Process Improvement