MUMBAI, India, Sept. 22 -- Intellectual Property India has published a patent application (202641108958 A) filed by Madhankumar C; Mrs. R. Devika - Dhanalakshmi Srinivasan University; Dr. G. M. Vinothani - Sdnb Vaishnav College For Women; Ms Monali Parikh - Parul University; Ms Ashwini Arvind Amtavane - Walchand College Of Arts And Science; Dr. Ramya Hebbar - Nitte Meenakshi Institute Of Technology Nmit; and Ms Jaishri J - Rathinam Technical Campus on September 10, 2026, for Generative Ai-Based Intelligent E-Governance Platform For Real-Time Citizen Services And Public Policy Decision Support Using Federated Learning And Knowledge Graph Analytics.
Inventors include Mrs. R. Devika - Dhanalakshmi Srinivasan University; Dr. G. M. Vinothani - Sdnb Vaishnav College For Women; Ms Monali Parikh - Parul University; Ms Ashwini Arvind Amtavane - Walchand College Of Arts And Science; Dr. Ramya Hebbar - Nitte Meenakshi Institute Of Technology Nmit; and Ms Jaishri J - Rathinam Technical Campus.
The application for the patent was published on September 18, 2026, under issue no. 38/2026.
Abstract: Generative AI-Based Intelligent e-Governance Platform for Real-Time Citizen Services and Public Policy Decision Support Using Federated Learning and Knowledge Graph Analytics ABSTRACT The present invention relates to an intelligent e-governance platform utilizing Generative Artificial Intelligence (Generative AI), Federated Learning, Knowledge Graph Analytics, Natural Language Processing (NLP), and real-time data analytics for improving digital citizen services and supporting public policy decision-making. The proposed platform provides a unified and intelligent architecture for processing citizen-service requests, government information, administrative records, policy documents, public-service data, and other authorized governmental information sources while maintaining privacy-aware distributed learning across multiple departments and administrative units. The system comprises a citizen interaction layer, multi-source government data acquisition module, data preprocessing and semantic processing module, federated learning engine, knowledge graph construction and analytics engine, Generative AI engine, policy intelligence module, explainable artificial intelligence module, service orchestration module, security and privacy layer, and an administrative decision-support interface. The platform receives citizen queries, applications, complaints, service requests, feedback, and other authorized interactions through web portals, mobile applications, conversational interfaces, digital service centers, and other communication channels. Natural Language Processing techniques are employed to understand multilingual citizen requests, identify relevant entities and service categories, extract intent, classify requests, and retrieve associated governmental information. The knowledge graph engine represents relationships among government departments, public services, schemes, regulations, policies, eligibility conditions, documents, administrative processes, geographic regions, and service dependencies to enable semantic search, contextual reasoning, and relationship-based information retrieval. The Generative AI engine utilizes the processed information and knowledge graph context to generate context-aware responses, service guidance, document summaries, application assistance, policy explanations, and administrative recommendations. The generated outputs may be grounded using authorized governmental knowledge sources and evidence references to reduce unsupported responses. The platform further incorporates a Federated Learning mechanism in which participating government departments or administrative units train machine-learning models locally using their respective authorized datasets without directly transferring sensitive raw data to a centralized repository. Model parameters, gradients, or privacy-preserving updates are securely aggregated to develop improved shared models while reducing unnecessary exposure of departmental or citizen-related information. The policy intelligence module analyzes structured and unstructured governmental information to identify policy trends, service-demand patterns, implementation gaps, resource requirements, citizen-service bottlenecks, and potential policy impacts. Knowledge graph analytics enables the system to trace relationships among policies, departments, services, beneficiaries, geographic regions, and administrative constraints. Predictive and generative analytics may further provide scenario-based policy insights and recommendations to authorized government decision-makers. An explainable AI mechanism provides human-readable explanations for analytical outputs by identifying relevant data sources, knowledge-graph relationships, contributing factors, policy provisions, confidence indicators, and reasoning paths. A real-time monitoring module continuously evaluates service requests, system performance, emerging citizen-service requirements, and policy-related indicators to generate alerts and prioritized information. The security and privacy layer incorporates authentication, authorization, encryption, secure model aggregation, access control, audit logging, privacy-preserving processing, and controlled data sharing mechanisms. Accordingly, the invention provides a privacy-aware, intelligent, scalable, and explainable e-governance platform capable of improving real-time citizen-service delivery, reducing administrative processing complexity, enabling cross-departmental knowledge integration, supporting multilingual digital interaction, and providing evidence-linked decision support for authorized public policy and governance functions.
Disclaimer: Curated by HT Syndication.
