MUMBAI, India, Sept. 28 -- Intellectual Property India has published a patent application (202641114150 A) filed by Dr. Chethan Chandra S Basavaraddi; Dr. Sapna S Basavaraddi; Dr. Sudhanshu Shekhar; Dr. Krishnakhi Choudhury; Dr. Ambili K N; Manoj K M; Dr. Priyanka Shrivastava; Dr. Kamatam Kiran; Dr. Etlam Mahadev Reddy; Manimugdha Medhi; Anup Nautiyal; Dr. Himanshi Babbar; Priyanka Kale; Ms. Neelam Rao Bharti; and Vennapusa Madhu Sudhan Reddy on September 23, 2026, for A Unified Secure Ai Framework For Smart Education, Fintech, Digital Governance, Blockchain Law And Management For Multidisciplinary Research.
Inventors include Dr. Chethan Chandra S Basavaraddi; Dr. Sapna S Basavaraddi; Dr. Sudhanshu Shekhar; Dr. Krishnakhi Choudhury; Dr. Ambili K N; Manoj K M; Dr. Priyanka Shrivastava; Dr. Kamatam Kiran; Dr. Etlam Mahadev Reddy; Manimugdha Medhi; Anup Nautiyal; Dr. Himanshi Babbar; Priyanka Kale; Ms. Neelam Rao Bharti; and Vennapusa Madhu Sudhan Reddy.
The application for the patent was published on September 25, 2026, under issue no. 39/2026.
Abstract: ABSTRACT The present invention discloses a unified secure artificial intelligence framework for multidisciplinary research across smart education, FinTech, digital governance, blockchain law, and management. The framework comprises a data acquisition layer, data integration layer, secure processing layer, privacy layer, blockchain provenance layer, AI orchestration layer, domain-specific AI engines, multidisciplinary research engine, explainable AI layer, analytics and visualization layer, and audit layer. Heterogeneous datasets are securely acquired and transformed into an interoperable representation. A research query is classified into one or more domains, and corresponding AI engines are dynamically selected for processing. Outputs from multiple domain-specific AI engines are combined to generate cross- domain analytical results and knowledge representations. Cryptographic provenance records provide integrity verification, while privacy mechanisms protect sensitive information. Explainable AI functionality provides contributing factors, model information, confidence information, and provenance associated with generated outputs. The framework thereby provides a unified computational environment for secure, traceable, explainable, and multidisciplinary AI-assisted research and decision- support.
Disclaimer: Curated by HT Syndication.
