Linguistic metrics for patent disclosure: Evidence from university versus corporate patents

Nancy Kong, Uwe Dulleck, Adam Jaffe, Shupeng Sun and Sowmya Vajjala

Research Policy, 2023, vol. 52, issue 2

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: Patent disclosure; Computational linguistic analysis; Readability; University patents; Corporate patents (search for similar items in EconPapers)
JEL-codes: K11 O31 O34 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Related works:
Working Paper: Linguistic Metrics for Patent Disclosure: Evidence from University versus Corporate Patents (2020)
Working Paper: Linguistic Metrics for Patent Disclosure: Evidence from University Versus Corporate Patents (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:respol:v:52:y:2023:i:2:s0048733322001913

DOI: 10.1016/j.respol.2022.104670

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