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.
•We propose a method using computational linguistics to quantify patent disclosure.•We show that university patents disclose 0.4 SD more than corporate patents.•University-corporate disclosure gap is larger among more-experienced applicants.•Corporations that focus on licensing disclose 0.3 SD more than other corporations.•Expert panel evaluations and USPTO 112(a) rejections show modest support.
