MUMBAI, India, Sept. 28 -- Intellectual Property India has published a patent application (202641111505 A) filed by Dr. Budampati V S Sowmya; Dr. K. K. Srimitra; Dr. Bhavna Jethalal Vyas; Dr. Srishti Chandak; Dr. Shailendra Kumar; Dr. Madhu Kumari; Dr. Janhavi Shridhar Bedekar; Dr. Kamatam Kiran; Veeresh K M; P. Divya; Dharmendra Kalita; Dr. Reena Gaur; Dr. Rekha Soni; Dr. Rittu Bala; and Dr. Rajesh Sharma on September 17, 2026, for A Secure Artificial Intelligence–enabled Integrated System For Smart Education, Financial Technology, Administrative Governance, Knowledge Management And Multidisciplinary Research Analytics.
Inventors include Dr. Budampati V S Sowmya; Dr. K. K. Srimitra; Dr. Bhavna Jethalal Vyas; Dr. Srishti Chandak; Dr. Shailendra Kumar; Dr. Madhu Kumari; Dr. Janhavi Shridhar Bedekar; Dr. Kamatam Kiran; Veeresh K M; P. Divya; Dharmendra Kalita; Dr. Reena Gaur; Dr. Rekha Soni; Dr. Rittu Bala; and Dr. Rajesh Sharma.
The application for the patent was published on September 25, 2026, under issue no. 39/2026.
Abstract: The invention provides a secure artificial intelligence-enabled integrated system comprising a secure data ingestion layer, identity and access management layer, data normalization and semantic integration layer, knowledge graph, AI orchestration engine, domain-specific AI modules, analytics engine, workflow engine, provenance and audit subsystem, security and privacy subsystem, and user interaction layer. The secure data ingestion layer receives information from a plurality of heterogeneous sources including, without limitation, learning management systems, student information systems, enterprise resource planning systems, banking or payment interfaces, accounting systems, human-resource systems, administrative databases, institutional repositories, libraries, research databases, scientific datasets, documents, sensor systems and external information services. The data normalization and semantic integration layer converts heterogeneous records into a common semantic representation and assigns metadata including source identity, timestamp, ownership, sensitivity classification, provenance and confidence. The knowledge graph represents entities and relationships derived from the normalized information. The knowledge graph may represent relationships between students, courses, instructors, assessments, financial transactions, departments, employees, policies, documents, research publications, datasets, projects, grants, laboratories and other entities. The AI orchestration engine receives a request or event and determines one or more appropriate AI processing operations based on contextual parameters. The orchestration engine may select one or more machine-learning models, large-language-model services, statistical models, rules engines, retrieval mechanisms or analytical algorithms. A security subsystem evaluates access privileges before information is supplied to an AI processing component. The security subsystem may implement identity verification, multifactor authentication, role-based access control, attribute-based access control, purpose-based access control, encryption, tokenization, masking, differential access and risk-based authorization. The system further generates an immutable or tamper-evident provenance record associating an AI output with the input data, data transformations, knowledge-graph elements, models, model versions, rules, prompts or processing instructions, authorization decisions and human approvals used to produce the output. The system thereby provides a common technical infrastructure for multiple institutional functions while maintaining separation, controlled interoperability and traceability between domains.
Disclaimer: Curated by HT Syndication.
