一种基于特征拼接、标签迁移及深度学习组合的专利价值评估方法

doi:10.3772/j.issn.1000-0135.2023.06.003

情报学报

2023, Vol. 42

Issue (6): 663-680 DOI: 10.3772/j.issn.1000-0135.2023.06.003

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一种基于特征拼接、标签迁移及深度学习组合的专利价值评估方法

赵雪峰¹, 胡瑾瑾¹, 吴德林¹, 吴伟伟², 孙安东³, 赵涛⁴

1.哈尔滨工业大学（深圳）经济管理学院，深圳 518000
2.哈尔滨工业大学经济与管理学院，哈尔滨 150000
3.深圳沃德知识产权代理事务所，深圳 518000
4.深圳盈峰知识产权咨询有限公司，深圳 518000

Patent Valuation Method Based on a Combination of Feature Stitching, Label Migration, and Deep Learning

Zhao Xuefeng¹, Hu Jinjin¹, Wu Delin¹, Wu Weiwei², Sun Andong³, Zhao Tao⁴

1.School of Economics and Management, Harbin Institute of Technology, Shenzhen, Shenzhen 518000
2.School of Management, Harbin Institute of Technology, Harbin 150000
3.Shenzhen Ward Intellectual Property Agency, Shenzhen 518000
4.Shenzhen Yingfeng Intellectual Property Consulting Co., Ltd, Shenzhen 518000

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摘要专利价值评估对打击非正常申请、净化市场环境具有重要的现实意义。本文以特征拼接、标签迁移及深度学习组合为中心构建专利价值评估方法，并基于2010—2020年广东省专利申请探究评估方法实际表现，引入多组对比模型进行实验分析。研究结果表明：①拼接著录事项信息，可构建出技术特征显现更强的专利研究对象，避免因指标类研究对象未足够体现专利技术本质而引发评估准确率不高的现象；②以专利法律视角量化出更具专利价值代表性的价值标签体系，在延展专利标签体系研究深度的同时，解决因引用率、下载访问量等传统标签与专利实际价值不匹配而造成的价值评估错误问题；③以高精细度词向量为构建原理，组建以BERT（bidirectional encoder representations from transformers）及LSTM（long short-term memory）为核心的专利价值评估模型，有效解决传统模型特征因提取能力不足而产生评估准确率偏低的弊端。本文从研究对象有效性、标签体系性及模型构建评估率三个方面提出优化改进策略，为专利价值评估提供了新工具，具有较强的实际应用价值。

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	赵雪峰
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	孙安东
	赵涛

关键词 ：特征拼接, BERT, 标签迁移, 专利, 深度学习

收稿日期: 2022-07-08

基金资助:国家自然科学基金面上项目“大数据能力驱动的突破性技术创新行为触发路径与演化机制”（72072047）；教育部人文社会科学研究青年基金项目“基于企业行为理论的创业导向对突破性技术创新行为影响的传导机制研究”（20YJC630090）；中央高校基本科研业务费专项资金项目“面向新动能塑造的技术管理能力对突破性创新行为的作用机制研究”（HIT.HSS.ESD202310）。

作者简介: 赵雪峰，男，1993年生，博士研究生，研究方向为知识产权管理及专利价值分析；胡瑾瑾，女，1997年生，博士研究生，研究方向为数字经济；吴德林，男，1963年生，博士，教授，博士生导师，研究方向为信息有用性；吴伟伟，男，1978年生，博士，教授，博士生导师，研究方向为技术管理与创新管理，E-mail：wuweiwei@hit.edu.cn；孙安东，男，1992年生，本科，研究方向为专利价值分析；赵涛，男，1996年生，本科，研究方向为专利价值分析；

引用本文:

赵雪峰, 胡瑾瑾, 吴德林, 吴伟伟, 孙安东, 赵涛. 一种基于特征拼接、标签迁移及深度学习组合的专利价值评估方法[J]. 情报学报, 2023, 42(6): 663-680.
Zhao Xuefeng, Hu Jinjin, Wu Delin, Wu Weiwei, Sun Andong, Zhao Tao. Patent Valuation Method Based on a Combination of Feature Stitching, Label Migration, and Deep Learning. 情报学报, 2023, 42(6): 663-680.

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https://qbxb.istic.ac.cn/CN/10.3772/j.issn.1000-0135.2023.06.003 或 https://qbxb.istic.ac.cn/CN/Y2023/V42/I6/663

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