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000047930 24500 $$aThat’s classified!$$bInventing a new patent taxonomy
000047930 264_1 $$aOxford, UK :$$bOxford University Press,$$c2021.
000047930 336__ $$atext$$2rdacontent
000047930 337__ $$acomputer$$2rdamedia
000047930 338__ $$aonline resource$$bcr$$2rdacarrier
000047930 4901_ $$aIndustrial and Corporate Change$$vVolume 30, Issue 3
000047930 5203_ $$aInnovation researchers currently make use of various patent classification schemas, which are hard to replicate. Using machine learning techniques, we construct a transparent, replicable and adaptable patent taxonomy, and a new automated methodology for classifying patents. We contrast our new schema with existing ones using a long-run historical patent dataset. We find quantitative analyses of patent characteristics are sensitive to the choice of classification; our interpretation of regression coefficients is schema dependent. We suggest much of the innovation literature should be carefully interpreted in light of our findings.
000047930 542__ $$fhttps://academic.oup.com/pages/using-the-content/citation
000047930 590__ $$aPublished online: 2021
000047930 650_0 $$aPatents
000047930 650_0 $$aInnovation
000047930 650_0 $$aPatents$$xClassification
000047930 7001_ $$aBillington, Stephen D.,$$vauthor.
000047930 7001_ $$aHanna, Alan J.,$$eauthor.
000047930 7731_ $$tIndustrial and Corporate Change,$$wINCC
000047930 830_0 $$aIndustrial and Corporate Change$$vINCC 2021, 30(3),  678-705
000047930 85641 $$uhttps://doi.org/10.1093/icc/dtaa049$$yRead the Article
000047930 904__ $$aArticle
000047930 980__ $$aINCC