Mapping Innovations in Artificial Intelligence through Patents: A Social Data Science Perspective

This paper is about mapping innovations in Artificial Intelligence (AI) with the help of patents. It leverages the use of Social Data Science techniques on two levels: (1) we perform a content analysis of all the abstracts of the available patents of an extensive database; (2) we use a Latent Dirichlet Allocation (LDA) technique on these patents’ abstracts to extract which categories best describe each subfield of AI. Our goal is to have a deeper perspective and a deeper understanding of the various developments in AI through time and geography. The database used in this paper is one of the most comprehensive, with a total of 55,109 patents. To contextualize this study, the literature review uses information from 29,225 articles. In both cases, the analysis of such amount of information could not be possible without dedicated computing power.