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Title:
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PRIOR ART CANDIDATE SEARCH ON BASE OF STATISTICAL AND SEMANTIC PATENT ANALYSIS |
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Author(s):
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Dmitriy M. Korobkin, Sergey S. Fomenkov, Alla G. Kravets and Sergey. G. Kolesnikov |
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ISBN:
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978-989-8533-66-1 |
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Editors:
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Yingcai Xiao and Ajith P. Abraham |
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Year:
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2017 |
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Edition:
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Single |
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Keywords:
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Prior-art patent search, patent examination, LDA, semantic analysis, natural language processing, SAO, big data |
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Type:
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Full Paper |
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First Page:
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231 |
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Last Page:
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238 |
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Language:
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English |
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Cover:
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Full Contents:
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click to dowload 
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Paper Abstract:
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In paper authors proposed a methodology to solve problem of prior art patent search, consists of statistical and semantic analysis of patent documents, and calculation of semantic similarity between application and patents on base of Subject-Action-Object (SAO) triples. The paper considers a description of statistical analysis based on LDA method and MapReduce paradigm. On the step of semantic analysis authors applied a new method for building semantic representation of SAO on base of Meaning-Text Theory. On the step of semantic similarity calculation we compare the SAOs from application and patent claims. We developed an software for the patent examination task, which is designed to reduce the time that an expert spends for the prior-art candidate search. This research was financially supported by the Russian Fund of Basic Research (grants No. 15-07-09142 A, No. 15-07-06254 A, No. 16-07-00534 ?). |
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