I-Corps: Translation Potential of Artificial Intelligence Powered Matchmaking and Resource Discovery for University-Driven Innovations

Award ID: 2526499

This I-Corps project focuses on an online platform that uses advanced large language model reasoning and natural language processing to match university discoveries with investors, industry partners, and community organizations. The platform collects and studies the full national patent record, open access research papers, and verified inventories of laboratory equipment and other institutional resources. By revealing connections that are currently hidden in separate data silos, the technology addresses the costly delays that slow the transition of research results into new products and services. Across the country thousands of promising inventions remain idle each year because inventors and potential sponsors cannot find one another quickly. Accelerating these matches strengthens the scientific enterprise, supports economic growth, and broadens access to knowledge. Faster adoption of breakthrough health treatments, clean energy devices, and advanced manufacturing methods improves public welfare, creates jobs, and enhances national competitiveness in emerging technology sectors. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. This solution is based on the development of a recommendation engine that combines artificial intelligence reasoning with continuously updated knowledge graphs generated from patent filings, scholarly literature, and institutional asset records. The technology is based on interviews with technology transfer professionals, investors, corporate researchers, and community leaders. Insights gained from these engagements guide iterative refinements to algorithms, data pipelines, and user experiences, ensuring that the platform remains accurate, transparent, and straightforward to adopt. Metrics such as search accuracy, match quality, and time to first contact inform technical milestones, while market feedback informs business viability. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Program Director: Ruth Shuman