Abstract
In the paper, authors proposed a methodology to solve the problem of prior art patent search, consists of a statistical and semantic analysis of patent documents, machine translation of patent application and calculation of semantic similarity between application and patents. The paper considers different variants of statistical analysis based on LDA method. On the step of the semantic analysis, authors applied a new method for building a semantic network on the base of Meaning-Text Theory. Prior art search also needs pre-translation of the patent application using machine translation tools. On the step of semantic similarity calculation, we compare the semantic trees for application and patent claims. We developed an automated system for the patent examination task, which is designed to reduce the time that an expert spends for the prior-art search and is adopted to deal with a large amount of patent information.
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Acknowledgement
This research was partially supported by the Russian Foundation of Basic Research (grants No. 15-07-09142 A, No. 15-07-06254 A, No. 16-07-00534 A).
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Korobkin, D., Fomenkov, S., Kravets, A., Kolesnikov, S. (2017). Methods of Statistical and Semantic Patent Analysis. In: Kravets, A., Shcherbakov, M., Kultsova, M., Groumpos, P. (eds) Creativity in Intelligent Technologies and Data Science. CIT&DS 2017. Communications in Computer and Information Science, vol 754. Springer, Cham. https://doi.org/10.1007/978-3-319-65551-2_4
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