Text-based Technological Signatures and Similarities: How to create them and what to do with them.
Daniel Stefan Hain, Roman Jurowetzki, Tobias Olaf Buchmann, Patrick Wolf · arXiv (Cornell University) · 2020
This paper describes a new approach to measure technological similarity between patents by leveraging their textual description. Using embedding techniques from natural language processing, we represent their description as a high dimensional numerical vector, thus capturing their technological signature. Deploying an almost near linear-scaling approximate nearest neighbor matching techniques, we are able to compute technological similarity scores for all existing patents. This enables us to represent the whole patent universe as a technological network. We validate both technological signature and similarity in various ways, and demonstrate their usefulness to create patent quality indicators, measure knowledge flows, and map technological change.