Translation Readiness Index: Measuring the Semantic Proximity of Research to Patented Science

Paul X. McCarthy, Rasika Amarasiri and Xian Gong

Papers from arXiv.org

Abstract: Universities, funders, and investors often need to spot research with translational potential early, long before downstream outcomes like licenses, startups, or patents emerge. We introduce the Translation Readiness Index (TRI), a scalable text-based metric that estimates a publication's semantic proximity to patent-linked science using only its title and abstract. Trained on over 20,000 scientific papers, contrasting papers paired with U.S. patents for the same invention against non-patent papers from the same journals, TRI uses domain-specific document embeddings to detect latent linguistic signals. Patent-paired papers consistently use an action-oriented "language of invention", whereas non-patent papers favor observational framing. Using only titles and abstracts, the model accurately distinguishes patent-paired research from comparison papers (ROC-AUC = 0.774). External validation across independent datasets shows that higher TRI scores strongly align with real-world translational activity. High scores correlate with industry coauthorship, author patent histories, and independent commercial-potential benchmarks. At the institutional level across leading global universities, average TRI correlates significantly with university-industry collaboration (r = 0.364, p

Date: 2026-06, Revised 2026-08
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