That's Classified! Inventing a New Patent Taxonomy

Abstract

We investigate how patent classification influences the interpretation of patent statistics. 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 then contrast our new schema with existing ones using a long-run historical patent dataset. We find quantitative analysis 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.