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

57 Pages Posted: 1 Oct 2020

See all articles by Nancy Kong

Nancy Kong

University of Queensland

Uwe Dulleck

Queensland University of Technology - School of Economics and Finance

shupeng sun

Government of Queensland - Queensland Treasury

Sowmya Vajjala

affiliation not provided to SSRN

Adam B. Jaffe

Brandeis University; Motu Economic and Public Policy Research; National Bureau of Economic Research (NBER)

Multiple version iconThere are 2 versions of this paper

Date Written: 2020

Abstract

Encouraging inventors to disclose new inventions is an important economic justification for the patent system, yet the technical information contained in patent applications is often inadequate and unclear. This paper proposes a novel approach to measure disclosure in patent applications using algorithms from computation allinguistics. Borrowing methods from the literature on second language acquisition, we analyze core linguistic features of 40,949 U.S. applications in three patent categories related to nanotechnology, batteries, and electricity from 2000 to 2019. Relying on the expectation that universities have more incentives to disclose their inventions than corporations for either incentive reasons or for different source documents that patent attorneys can draw on, we confirm the relevance and usefulness of the linguistic measures by showing that university patents are more readable. Combining the multiple measures using principal component analysis, we find that the gap in disclosure is 0.4 SD, with a wider gap between top applicants. Our results do not change after accounting for the heterogeneity of inventions by controlling for cited-patent fixed effects. We also explore whether one pathway by which corporate patents become less readable is use of multiple examples to mask the “best mode” of inventions. By confirming that computational linguistic measures are useful indicators of readability of patents, we suggest that the disclosure function of patents can be explored empirically in a way that has not previously been feasible.

Keywords: patent disclosure, computational linguistic analysis, readability

JEL Classification: K110, O310, O340

Suggested Citation

Kong, Nancy and Dulleck, Uwe and sun, shupeng and Vajjala, Sowmya and Jaffe, Adam B., Linguistic Metrics for Patent Disclosure: Evidence from University Versus Corporate Patents (2020). CESifo Working Paper No. 8571, Available at SSRN: https://ssrn.com/abstract=3702123

Nancy Kong (Contact Author)

University of Queensland ( email )

St Lucia
Brisbane, Queensland 4072
Australia

Uwe Dulleck

Queensland University of Technology - School of Economics and Finance ( email )

GPO Box 2434
2 George Street
Brisbane, Queensland 4001
Australia

Shupeng Sun

Government of Queensland - Queensland Treasury

Sowmya Vajjala

affiliation not provided to SSRN

No Address Available

Adam B. Jaffe

Brandeis University ( email )

Waltham, MA 02454-9110
United States
781-736-2251 (Phone)
781-736-2263 (Fax)

HOME PAGE: http://www.brandeis.edu/global/people/faculty/jaff

Motu Economic and Public Policy Research ( email )

Level 1, 93 Cuba Street
P.O. Box 24390
Wellington, 6142
New Zealand

HOME PAGE: http://motu.org.nz

National Bureau of Economic Research (NBER)

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

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