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Data Analysis and Knowledge Discovery  2021, Vol. 5 Issue (3): 60-68    DOI: 10.11925/infotech.2096-3467.2020.1028
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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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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     
Received: 01 October 2020      Published: 12 April 2021
ZTFLH:  TP391  
Fund:National Natural Science Foundation of China(61671070)
Corresponding Authors: You Xindong     E-mail: youxindong@bistu.edu.cn

Cite this article:

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.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2020.1028     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/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
Advantages and Disadvantages of Patent Infringement Detection Methods
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