Patent similarity measurement, as one of the fundamental building blocks for patent analysis, is able to derive technical intelligence efficiently, but also can detect the risk of infringement and evaluate whether the invention meets the criteria of novelty and innovation. However, traditional approaches make implicitly several assumptions, such as bag of words in each component, semantic direction irrelevance and so on. In order to relax these assumptions, this study proposes an improved methodology on the basis of entities and semantic relations (functional and non-functional relations), which takes semantic direction of each sequence structure and the word order information of each component into consideration. Meanwhile, an algorithm for calculating the global importance of each sequence structure is put forward. Finally, to verify the effectiveness and performance of the improved semantic analysis, a case study is conducted on the thin film head subfield in the field of hard disk drive. Extensive experimental results show that our approach is significantly more accurate.

专利相似性衡量作为专利分析的基本组成部分之一，可以有效地获取技术情报，但也可以检测侵权风险并评估发明是否符合新颖性和创新性标准。但是，传统方法隐含地做出了几种假设，例如每个组成部分中的单词袋，语义方向无关紧要等。为了放松这些假设，本研究在实体和语义关系（功能和非功能关系）的基础上提出了一种改进的方法，该方法考虑了每个序列结构的语义方向和每个组成部分的词序信息。同时，提出了一种计算每个序列结构全局重要性的算法。最后，硬盘驱动器领域中的薄膜头子领域。大量的实验结果表明，我们的方法明显更准确。