  This paper describes an efficiently scalable approach to measure
technological similarity between patents by combining embedding techniques from
natural language processing with nearest-neighbor approximation. Using this
methodology we are able to compute existing similarities between all patents,
which in turn enables us to represent the whole patent universe as a
technological network. We validate both technological signature and similarity
in various ways, and demonstrate at the case of electric vehicle technologies
their usefulness to measure knowledge flows, map technological change, and
create patent quality indicators. Thereby the paper contributes to the growing
literature on text-based indicators for patent analysis. We provide thorough
documentations of the method, including all code, indicators, and intermediate
outputs at https://github.com/daniel-hain/patent_embedding_research.
