In-Text Patent Citations: A User’s Guide
Kevin Bryan (),
Yasin Ozcan and
No 25742, NBER Working Papers from National Bureau of Economic Research, Inc
We introduce, validate, and provide a public database of a new measure of the knowledge inventors draw on: scientific references in patent specifications. These references are common and algorithmically extractable. Critically, they are very different from the “front page” prior art commonly used to proxy for inventor knowledge. Only 24% of front page citations to academic articles are in the patent text, and 31% of in-text citations are on the front page. We explain these differences by describing the legal rules and practice governing citation. Empirical validations suggest that in-text citations appear to more accurately measure real knowledge flows, consistent with their legal role.
JEL-codes: O3 (search for similar items in EconPapers)
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Published as Kevin A. Bryan & Yasin Ozcan & Bhaven Sampat, 2020. "In-text patent citations: A user's guide," Research Policy, vol 49(4).
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Journal Article: In-text patent citations: A user's guide (2020)
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