MUMBAI, India, Oct. 26 -- Intellectual Property India has published a patent application (202611112945 A) filed by Kunal Kumar Bagre on September 21, 2026, for System And Method For Context-Aware Enterprise Knowledge Retrieval Using Semantic Context-Ranking And Retrieval-Augmented Generation.
Inventor includes Kunal Kumar Bagre.
The application for the patent was published on October 23, 2026, under issue no. 43/2026.
Abstract: ABSTRACT [505] The present invention introduces an advanced context-aware enterprise knowledge retrieval framework that integrates retrieval-augmented generation with semantic context-ranking mechanisms, facilitating precise knowledge acquisition, dynamic relevance optimisation, and robust answer confirmation while maintaining seamless enterprise integration and operational accuracy for consistent knowledge-access applications. [510] The comprehensive retrieval framework employs adaptive machine-learning algorithms and semantic-embedding protocols, utilising embedded inference arrays and resource-efficient ranking systems to ensure timely knowledge identification, enhanced contextual understanding, and optimal answer reliability while maintaining continuous relevance-monitoring capabilities across distributed enterprise repositories. [515] The integrated methodology combines multi-source document ingestion techniques with artificial-intelligence-driven semantic ranking systems, leveraging variable-confidence relevance signals and multi-factor context indicators to optimise retrieval procedures and generation workflows for maximum answer accuracy and minimal hallucination uncertainty during critical enterprise queries. [520] The novel responsive retrieval architecture features engineered high-throughput embedding components with specialised context-fingerprinting protocols, enabling complex multi-stage relevance verification while ensuring retrieval consistency and performance optimisation across various enterprise applications without compromising system reliability. [525] The innovative design incorporates strategic validation mechanisms for enhanced relevance identification and knowledge security, utilising optimised multi-model systems and adaptive scoring technology to ensure legitimate source attribution while maintaining functionality across diverse enterprise environments and query scenarios. [530] Implementation methodology emphasises scalable knowledge integration and efficient retrieval sequences, implementing interactive grounding measures and semantic- ranking algorithms to achieve superior relevance determination, enhanced answer identification, and unsupported-response prevention while ensuring technological simplicity during enterprise knowledge access. [535] The system demonstrates exceptional adaptability through comprehensive integration of semantic identification protocols and intelligent retrieval technologies, validating its effectiveness across various enterprise knowledge configurations and query scenarios while maintaining consistent retrieval performance and operational efficiency under diverse conditions. [540] The developed framework enables sustainable and reliable delivery of grounded enterprise answers through streamlined, AI-powered retrieval systems, providing significant advantages over traditional keyword approaches through adaptive validation mechanisms, semantic identification protocols, and improved source assignment while maintaining superior answer accuracy during critical enterprise knowledge access.
Disclaimer: Curated by HT Syndication.
