Classifying Patents Based on their Semantic Content

TL;DR

This paper introduces a semantic classification method for patents using large-scale data mining and network analysis, leveraging full abstracts and keywords to create a new patent database and compare it with traditional technological classifications.

Contribution

The paper presents a novel semantic approach to patent classification that differs from traditional methods by analyzing textual content and network topology, supported by statistical evidence.

Findings

Semantic classification captures different patent features than technological classification.

The constructed patent database includes 4 million patents from 1976 onward.

Semantic and technological approaches exhibit distinct topological properties.

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.