Abstract
Software defined network (SDN) has gained a great attention in academic field for its separation of the control plane and the data plain to get a programmable network. In the study, we propose an SDN architecture for a scenario of intelligent patent prior art search system. Different from the current mainstream patent retrieval system, where patent prior art search is executed by means of traditional keywords matches under a fixed network topology, our proposed patent prior art search system based on SDN architecture can provide systematic and security analysis of patent text when encountering big data flows of patent applications. We also propose a new Phrase-based patent text representation model (PPTR), where the whole patent text is represented as a Bag of Phrases and then embedded into vector for patent prior art search, which could maintain the integrity of semantic units of patent text. Our experiments show that the proposed PPTR model achieves the best performance compared with traditional approaches of patent prior art search, and it is also expected that SDN architecture is a promising platform framework for other patent mining tasks.
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Acknowledgements
This work was supported by the Public Technology Research Project of Zhejiang Province (No. LGF19G010001), and the Technology Reserach Special Project of the Ministry of Public Security (No. 2019GABJC36).
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Geng, B., Wang, F. An SDN architecture for patent prior art search system based on phrase embedding. Autom Softw Eng 29, 58 (2022). https://doi.org/10.1007/s10515-022-00360-y
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DOI: https://doi.org/10.1007/s10515-022-00360-y