As can be seen in the emergence of non-practicing entities, patent infringement lawsuits are very significant events for companies, both financially and technologically. Thus, the importance of patent infringement analysis has been emphasized to support a decision-making process of potential stakeholders. Since identifying patent infringement needs to consider various factors, the most appropriate method is to review the expert analysis in each case. However, as the size of valuable data continues to grow in recent years, the need for automated quantitative analysis that enables to perform such processes without experts has increased. Thus, this research aims to develop an automated approach for patent infringement using Subject-Action-Object structure-based text mining technique and SAO2Vec, which focus on the functions of technology in patent documents and product documents. The proposed framework consists of three modules. In the first module, the types of companies in which patent infringement can occur are defined, and then lists of companies selected by various databases are identified. In the second module, vectors of the SAO structures are derived from the patent documents of the selected company using the Doc2Vec-based SAO2Vec. In the last module, the results of the first and second modules are used to calculate the patent infringement indicators. To validate the suggested approach, we applied it to the case of Nintendo, which had recently become an issue in patent infringement lawsuits. We found that the proposed indicators were a statistically good indicator to judge the patent infringement, identifying the pairs of patents that have a high possibility of patent infringement.

从非执业实体的出现可以看出，专利侵权诉讼对于公司在财务和技术上都是非常重要的事件。因此，已经强调了专利侵权分析的重要性，以支持潜在利益相关者的决策过程。由于确定专利侵权需要考虑各种因素，因此最合适的方法是审查每种情况下的专家分析。但是，随着近年来有价值数据的规模不断增长，对无需专家即可执行此类过程的自动化定量分析的需求不断增加。因此，本研究旨在使用基于主体－行动－主体结构的文本挖掘技术和SAO2Vec，开发一种自动的专利侵权方法，重点介绍专利文件和产品文件中的技术功能。拟议的框架包括三个模块。在第一个模块中，定义了可能发生专利侵权的公司类型，然后识别由各种数据库选择的公司列表。在第二个模块中，使用基于Doc2Vec的SAO2Vec从所选公司的专利文件中获取SAO结构的向量。在最后一个模块中，第一个和第二个模块的结果用于计算专利侵权指标。为了验证所建​​议的方法，我们将其应用于任天堂的案例，该案例最近在专利侵权诉讼中成为一个问题。我们发现建议的指标是判断专利侵权的统计上好的指标，