Classifying Patents Based on their Semantic Content
This provides a novel tool for extracting endogenous information from patent data, which could benefit researchers and analysts in intellectual property and innovation studies, though it appears incremental as an extension of existing data-mining techniques.
The researchers tackled patent classification by developing a semantic approach that analyzes patent titles and abstracts to extract keywords, creating a consolidated database of 4 million US patents from 1976 onward. They found that this semantic method produces significantly different topological measures and statistical models compared to traditional technological classification approaches.
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.