Replication Data for: Linguistic Metrics for Patent Disclosure: Evidence from University Versus Corporate Patents

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 features 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. Testing the usefulness of linguistic metrics with disclosure and readability evaluations by an engineering student ``expert'' panel and by examining USPTO 112 (a)---lack of disclosure---rejection, we find modest support for our approach. The ability to quantify disclosure opens new research paths and potentially facilitates improvement of disclosure.