Title of article :
A three-phase method for patent classification
Author/Authors :
Yen-Liang Chen، نويسنده , , Yuan-Che Chang، نويسنده ,
Issue Information :
دوماهنامه با شماره پیاپی سال 2012
Pages :
14
From page :
1017
To page :
1030
Abstract :
An automatic patent categorization system would be invaluable to individual inventors and patent attorneys, saving them time and effort by quickly identifying conflicts with existing patents. In recent years, it has become more and more common to classify all patent documents using the International Patent Classification (IPC), a complex hierarchical classification system comprised of eight sections, 128 classes, 648 subclasses, about 7200 main groups, and approximately 72,000 subgroups. So far, however, no patent categorization method has been developed that can classify patents down to the subgroup level (the bottom level of the IPC). Therefore, this paper presents a novel categorization method, the three phase categorization (TPC) algorithm, which classifies patents down to the subgroup level with reasonable accuracy. The experimental results for the TPC algorithm, using the WIPO-alpha collection, indicate that our classification method can achieve 36.07% accuracy at the subgroup level. This is approximately a 25,764-fold improvement over a random guess.
Keywords :
K nearest neighbors (KNN) , Vector space model (VSM) , k-means , Support vector machines (SVM) , Patent classification , IPC taxonomy
Journal title :
Information Processing and Management
Serial Year :
2012
Journal title :
Information Processing and Management
Record number :
1229296
Link To Document :
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