The Language of Innovation

Predicting innovation is a peculiar problem in data science. Following its definition, an innovation is always a never-seen-before event, leaving no room for traditional supervised learning approaches. Here we propose a strategy to address the problem in the context of innovative patents, by defining innovations as never-seen-before associations of technologies and exploiting self-supervised learning techniques. We think of technological codes present in patents as a vocabulary and the whole technological corpus as written in a specific, evolving language. We leverage such structure with techniques borrowed from Natural Language Processing by embedding technologies in a high dimensional euclidean space where relative positions are representative of learned semantics. Proximity in this space is an effective predictor of specific innovation events, that outperforms a wide range of standard link-prediction metrics. The success of patented innovations follows a complex dynamics characterized by different patterns which we analyze in details with specific examples. The methods proposed in this paper provide a completely new way of understanding and forecasting innovation, by tackling it from a revealing perspective and opening interesting scenarios for a number of applications and further analytic approaches.

TACCHELLA Andrea;  NAPOLETANO Andrea;  PIETRONERO Luciano; 

2020-05-29

PUBLIC LIBRARY SCIENCE

JRC120210

1932-6203 (online),   

https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0230107,    https://publications.jrc.ec.europa.eu/repository/handle/JRC120210,   

10.1371/journal.pone.0230107 (online),