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-dependant. We suggest much of the innovation literature should be carefully interpreted in light of our findings.
Original language | English |
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Journal | Industrial and Corporate Change |
Early online date | 22 Nov 2020 |
DOIs | |
Publication status | Early online date - 22 Nov 2020 |
Keywords
- Innovation
- Invention
- Machine Learning
- Patents
- Patent Classification
- Taxonomy
- Economic History