Classifying Patents Based on their Semantic Content

Abstract

In this paper, we extend some usual techniques of classification resulting from a large-scale data-mining and network approach. This new technology, which in particular is designed to be suitable to big data, is used to construct an open consolidated database from raw data on 4 million patents taken from the US patent office from 1976 onward. To build the pattern network, not only do we look at each patent title, but we also examine their full abstract and extract the relevant keywords accordingly. We refer to this classification as semantic approach in contrast with the more common technological approach which consists in taking the topology when considering US Patent office technological classes. Moreover, we document that both approaches have highly different topological measures and strong statistical evidence that they feature a different model. This suggests that our method is a useful tool to extract endogenous information.

28 pages ; 9 figures ; 5 Supplementary Materials

References in corpus (3)

• Community detection in networks: Modularity optimization and maximum likelihood are equivalent
• Extending the definition of modularity to directed graphs with overlapping communities
• Invention as a Combinatorial Process: Evidence from U.S. Patents

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