| Download | - View final version: Linguistic metrics for patent disclosure: evidence from university versus corporate patents (PDF, 739 KiB)
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| DOI | Resolve DOI: https://doi.org/10.1016/j.respol.2022.104670 |
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| Author | Search for: Kong, NancyORCID identifier: https://orcid.org/0000-0002-2246-8363; Search for: Dulleck, Uwe; Search for: Jaffe, Adam B.; Search for: Sun, Shupeng; Search for: Vajjala, Sowmya1 |
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| Affiliation | - National Research Council of Canada. Digital Technologies
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| Format | Text, Article |
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| Subject | patent disclosure; computational linguistic analysis; readability; university patents; corporate patents |
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| 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. |
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| Publication date | 2022-12-05 |
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| Publisher | Elsevier |
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| Licence | |
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| In | |
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| Language | English |
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| Peer reviewed | Yes |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | d4f25eb7-f163-4ca0-958a-c3c72d8c5bed |
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| Record created | 2023-04-24 |
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| Record modified | 2023-04-27 |
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