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

Kong, N; Dulleck, U; Jaffe, AB; Sun, S; Vajjala, S

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

Kong, N Dulleck, U Jaffe, AB Sun, S Vajjala, S

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Publisher:: ELSEVIER
Publication Type:: Journal Article
Citation:: Research Policy, 2023, 52, (2)
Issue Date:: 2023-03-01

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Field	Value	Language
dc.contributor.author	Kong, N
dc.contributor.author	Dulleck, U
dc.contributor.author	Jaffe, AB
dc.contributor.author	Sun, S
dc.contributor.author	Vajjala, S
dc.date.accessioned	2023-11-07T04:35:13Z
dc.date.available	2023-11-07T04:35:13Z
dc.date.issued	2023-03-01
dc.identifier.citation	Research Policy, 2023, 52, (2)
dc.identifier.issn	0048-7333
dc.identifier.issn	1873-7625
dc.identifier.uri	http://hdl.handle.net/10453/173202
dc.description.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.
dc.language	English
dc.publisher	ELSEVIER
dc.relation.ispartof	Research Policy
dc.relation.isbasedon	10.1016/j.respol.2022.104670
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	1402 Applied Economics, 1503 Business and Management, 1505 Marketing
dc.subject.classification	Science Studies
dc.subject.classification	3507 Strategy, management and organisational behaviour
dc.subject.classification	3801 Applied economics
dc.title	Linguistic metrics for patent disclosure: Evidence from university versus corporate patents
dc.type	Journal Article
utslib.citation.volume	52
utslib.for	1402 Applied Economics
utslib.for	1503 Business and Management
utslib.for	1505 Marketing
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Health
pubs.organisational-group	/University of Technology Sydney/Faculty of Health/Centre for Health Economics Research and Evaluation
utslib.copyright.status	open_access	*
dc.date.updated	2023-11-07T04:35:12Z
pubs.issue	2
pubs.publication-status	Published
pubs.volume	52
utslib.citation.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.

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http://hdl.handle.net/10453/173202