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| Identifying High-Quality Technology Patents Based on Deep Learning and Multi-Category Polling Mechanism——Case Study of Patent Applications |
Zhao Xuefeng1,Wu Delin1,Wu Weiwei2( ),Sun Zhuoluo1,Hu Jinjin1,Lian Ying3,Shan Jiayu4 |
1School of Management, Harbin Institute of Technology, Shenzhen, Shenzhen 518055, China
2School of Management, Harbin Institute of Technology, Harbin 150006, China
3Shenzhen Ward Intellectual Property Agency, Shenzhen 518000, China
4Shenzhen Yingfeng Intellectual Property Consulting Co., Ltd, Shenzhen 518000, China |
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Abstract [Objective] This paper addresses the issues of the traditional single classification method, which cannot effectively identify high-quality “bottleneck” technology patents. [Methods] We developed a multi-category polling model (LSTM-Seq-BERT) with LSTM, Word2Vec, and BERT to identify high-quality “bottleneck” patents from the application documents. Moreover, we constructed a corresponding multi-level label system for the model with IPC number as the primary classification labels and authorization status as the secondary classification labels. [Results] The accuracy of identifying high-quality “bottleneck” technology patents was increased to 88.1%. [Limitations] We only utilized patents from the Hongkong-Macau-Guangdong Greater Bay Area, resulting in data imbalance. [Conclusions] The proposed model can enhance the accuracy of identifying high-quality “bottleneck” technology patents and possesses practical value.
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Received: 13 July 2022
Published: 08 October 2023
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| Fund:National Natural Science Foundation of China(72072047);Philosophy and Social Sciences Research Planning Project of Heilongjiang Province(19GLB087);Humanities Social Sciences Research, Ministry of Education(20YJC630090) |
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Corresponding Authors:
Wu Weiwei，ORCID：0000-0003-3769-3122，E-mail： wuweiwei@hit.edu.cn。
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