Measuring patent similarity with SAO semantic analysis

Xuefeng Wang (), Huichao Ren, Yun Chen, Yuqin Liu, Yali Qiao and Ying Huang
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Xuefeng Wang: Beijing Institute of Technology
Huichao Ren: Beijing Institute of Technology
Yun Chen: Beijing Institute of Technology
Yuqin Liu: Beijing Institute of Graphic Communication
Yali Qiao: Beijing Institute of Technology
Ying Huang: Beijing Institute of Technology

Scientometrics, 2019, vol. 121, issue 1, No 1, 23 pages

Abstract: Abstract Patents are not only an important aspect of intellectual property rights, but they are also one of the only ways to protect technological inventions. However, in recent years, the number of patents has been increasing dramatically and, as a result, both patent applicants and patent examiners are finding it more difficult to conduct the due diligence step of the patent registration process. Therefore, the lack of a quick and easy way to accurately measure patent similarity has become a significant obstacle to protecting intellectual property. Currently, there are three main ways to measure patent similarity: IPC code analysis, citation analysis, and keyword analysis. None of these approaches are able to fully reflect the semantics in a patent’s content. As an emerging methodology, subject–action–object (SAO) semantic analysis does reflect semantics, but most approaches treat each identified relationship as equally important, which does not necessarily provide an accurate measure of patent similarity. To offer this power to SAO analysis, this article introduces a new indicator called DWSAO as a reflection of the weight of each SAO semantic structure. Further, we present a semantic analysis framework that incorporates the DWSAO index for finding similar patents based on the weight of each SAO structure in the patent. A case study on the similarity of patents in the field of robotics was used to verify the reliability of the method. The results highlight the detailed meanings derived from the method, the accuracy of the outcomes, and the practical significance of using this approach.

Keywords: Patent similarity measurement; Text mining; Subject–action–object (SAO); Semantic analysis; Robot docking stations (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (14)

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DOI: 10.1007/s11192-019-03191-z

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