MUMBAI, India, Oct. 12 -- Intellectual Property India has published a patent application (202641119367 A) filed by Rns Institute Of Technology, Bengaluru; Priyanshu Kumar Rncs; Sai Sarvesh Rrncs; N S Shashidharrncs; Dr. Aravinda Sridhararao Professor; Mr. Naveen B Massistant Professor; and Suhan Ganeshrncs on October 06, 2026, for An Intelligent Repository Discovery And Collaboration Platform Using Semantic Search And Machine Learning.
Inventors include Mr. Naveen B Massistant Professor; Dr. Aravinda Sridhararao Professor; N S Shashidharrncs; Priyanshu Kumarrncs; Sai Sarvesh Rrncs; and Suhan Ganeshrncs.
The application for the patent was published on October 09, 2026, under issue no. 41/2026.
Abstract: Software development platforms host millions of repositories across various domains, making it increasingly difficult for developers to discover relevant projects efficiently. Existing repository discovery systems primarily rely on keyword-based search mechanisms, which often fail to understand the context and intent of user queries. As a result, developers are required to manually analyze repository documentation, structure, and dependencies, leading to increased time consumption and reduced productivity. Furthermore, inconsistent tagging, incomplete documentation, and varying repository quality further complicate the discovery process The proposed invention introduces an intelligent semantic repository discovery system using machine learning and natural language processing techniques to improve repository search and understanding. The system collects repository data including README files, source code structure, programming languages, dependencies, issues, and contributor activity. The collected data is preprocessed and transformed into semantic representations using embedding techniques. These representations allow the system to understand contextual meaning rather than relying solely on keyword matching. The invention further incorporates a multi-factor ranking mechanism that evaluates repositories based on semantic relevance, repository quality, dependency similarity, contributor activity, and project maturity. This ranking mechanism improves search accuracy and ensures that developers receive more meaningful and relevant results. Additionally, the system provides structured repository insights, including domain classification, technology stack identification, and potential contribution areas.The proposed system improves repository discovery accuracy, reduces manual effort, enhances developer productivity, and promotes efficient collaboration. The invention can be integrated with existing repository platforms or deployed as an independent discovery platform, thereby improving developer experience and facilitating efficient open-source collaboration
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