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2017 ; 12
(4
): e0176310
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Classifying patents based on their semantic content
#MMPMID28445550
Bergeaud A
; Potiron Y
; Raimbault J
PLoS One
2017[]; 12
(4
): e0176310
PMID28445550
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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.
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