TY  - BOOK
N2  - Innovation 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.
AB  - Innovation 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.
T1  - That’s classified!Inventing a new patent taxonomy
AU  - Billington, Stephen D.,
AU  - Hanna, Alan J.,
JF  - Industrial and Corporate Change,
VL  - Volume 30, Issue 3
LA  - eng
ID  - 47930
KW  - Patents
KW  - Innovation
KW  - Patents
SN  - 0960-6491 (Print)
SN  - 1464-3650 (Online)
TI  - That’s classified!Inventing a new patent taxonomy
LK  - https://doi.org/10.1093/icc/dtaa049
UR  - https://doi.org/10.1093/icc/dtaa049
ER  -