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数据分析与知识发现  2021, Vol. 5 Issue (3): 60-68     https://doi.org/10.11925/infotech.2096-3467.2020.1028
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中文专利侵权检测研究综述*
吕学强,罗艺雄,李家全,游新冬()
北京信息科技大学网络文化与数字传播重点实验室 北京 100101
Review of Studies on Detecting Chinese Patent Infringements
Lv Xueqiang,Luo Yixiong,Li Jiaquan,You Xindong()
Beijing Key Laboratory of Internet Culture & Digital Dissemination Research, Beijing Information Science &Technology University, Beijing 100101, China
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摘要 

【目的】 分析并总结专利侵权检测的相关研究,为下一步研究提供理论基础和发展趋势。【文献范围】 利用知网和Bing Scholar以“专利侵权”、“Patent Infringement”、“专利相似度”和“Patent Similarity”等关键词进行检索,经过手工筛选获得代表性文献53篇。【方法】 总结基于聚类、基于向量空间模型、基于SAO(Subject-Action-Object)结构、基于深度学习和基于专利结构等专利侵权检测方法;在分析现有方法优缺点的基础上,总结优化专利侵权检测的方向。【结果】 专利侵权检测旨在从大量专利文献中检索出小批量的侵权风险较高的专利,从而减少需要人工进行专利侵权判定的专利数量。专利侵权检测通过计算专利间相似度来判断专利侵权的风险,相似度主要使用不同粒度的统计信息计算得到。【局限】 由于标准数据集的缺失,未能对专利侵权检测相关方法进行量化比较。【结论】 提出从引入预训练模型、融合专利不同组成部分计算相似度和构建高质量的专利侵权检测数据集等方向开展该主题后继研究的建议。

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吕学强
罗艺雄
李家全
游新冬
关键词 专利相似度专利侵权检测深度学习人工智能    
Abstract

[Objective] This paper reviews research on detecting patent infringements, aiming to provide theoretical frameworks and development trends for future studies. [Coverage] We retrieved 53 representative literatures from CNKI and Bing Scholar using the keywords of “Patent Infringement” or “Patent Similarity”. [Methods] First, we summarized the methods for detecting patent infringement based on clustering, vector space model, SAO (Subject-Action-Object) structure, deep learning and patent structure. Then, we compared the advantages and disadvantages of popular methods for detecting patent infringements. Finally, we explored some possible optimization solutions for the existing methods. [Results] Patent infringement detection aims to retrieve small number of patents with higher risks of infringement from a large number of patent documents. It reduces the number of patents requiring manual judgments. Our method decides the risk of patent infringement by calculating their similarities based on statistical information of different granularities. [Limitations] Due to the lack of standard data sets, we could not quantitatively compare the methods for detecting patent infringements. [Conclusions] We could optimize patent infringement detection with pre-training models, calculating similarity of different patent components, and constructing high-quality data sets.

Key wordsPatent Similarity    Patent Infringement Detection    Deep Learning    Artificial Intelligence
收稿日期: 2020-10-01      出版日期: 2021-04-12
ZTFLH:  TP391  
基金资助:*国家自然科学基金项目(61671070)
通讯作者: 游新冬     E-mail: youxindong@bistu.edu.cn
引用本文:   
吕学强,罗艺雄,李家全,游新冬. 中文专利侵权检测研究综述*[J]. 数据分析与知识发现, 2021, 5(3): 60-68.
Lv Xueqiang,Luo Yixiong,Li Jiaquan,You Xindong. Review of Studies on Detecting Chinese Patent Infringements. Data Analysis and Knowledge Discovery, 2021, 5(3): 60-68.
链接本文:  
https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2020.1028      或      https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/Y2021/V5/I3/60
方法 优点 缺点 综述的文献篇数
基于聚类的方法 无监督学习方法,无需要人工标注。 聚类的类别有限,类别个数无法预先设定,在大量的专利文本中无法有效减少人工工作量。 3
基于向量空间模型的方法 向量工具模型的构建过程简单易行,向量维度特征的粒度及权值可调节。 无法表示维度特征间的联系及大于维度特征粒度的信息。
向量维度与语料规模正相关,大规模语料下构建的VSM中向量维度高且稀疏,使得进一步的相似度计算更复杂。
4
基于SAO结构的方法 可从语义层面检测相似度,计算工作量较小。 基于SAO结构的专利相似度计算准确性依赖于SAO结构抽取的准确性,有监督学习有助于筛选出符合需求的SAO结构,但需要人工标注的成本。 6
基于深度学习的方法 深度学习模型学习样本数据经验的容量上限远超出其他模型,准确性较高。 可解释性差,且高质量标注数据需要大量人力参与。 8
基于专利结构的方法 准确性较高,同时更具有针对性,可根据专利不同组成部分的特点采用不同的方法。 各个部分的相似度对专利侵权的权重不一样,需要一定数量的实验进行权重确定,计算工作量较大。 7
Table 1  现有专利侵权检测方法优缺点对比
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