Linguistic metrics for patent disclosure: Evidence from university versus corporate patents
Nancy Kong, Uwe Dulleck, Adam B. Jaffe, Shupeng Sun, Sowmya Vajjala
Research Policy · 2023 · Volume 52 · Issue 2
DOI: 10.1016/j.respol.2022.104670
Abstract
Encouraging disclosure is important for the patent system, yet the technical information in patent applications is often inadequate. We use algorithms from computational linguistics to quantify the effectiveness of disclosure in patent applications. Relying on the expectation that universities have more ability and incentive to disclose their inventions than corporations, we analyze 64 linguistic measures of patent applications, and show that university patents are more readable by 0.4 SD of a synthetic measure of readability. Results are robust to controlling for non-disclosure-related invention heterogeneity. The linguistic metrics are evaluated by a panel of “expert” student engineers and further examined by USPTO 112(a) – lack of disclosure – rejection. The ability to quantify disclosure opens new research paths and potentially facilitates improvement of disclosure.
Keywords
Readability, Patent disclosure, Corporate patents, Computational linguistic analysis, University patents
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