Abstract 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.
|Number of pages||28|
|Journal||Industrial and Corporate Change|
|Early online date||22 Nov 2020|
|Publication status||Published (in print/issue) - 27 Dec 2020|
- Machine Learning
- Patent Classification
- Economic History