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

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 < 0.001). TRI provides an automated early-stage screening tool to prioritize research for expert review, measuring semantic proximity to patented science rather than guaranteed commercial outcomes.