Core Patent Portfolio Identification and Application in Professional Technical Field
Zeng Wen1,Wang Yuefen2,3()
1School of Intellectual Property, Nanjing University of Science and Technology, Nanjing 210094, China 2School of Management, Tianjin Normal University, Tianjin 300380, China 3Institute for Big Data Science, Tianjin Normal University, Tianjin 300380, China
[Objective] This paper constructs identification methods for core patent portfolio and then examines their application with the help of large-scale datasets. [Methods] Through cross-combination, we constructed five identification models for the patents, which included six features of the patents. We then compared our methods’ performance with datasets of artificial intelligence. [Results] Different combined methods yielded highly consistent results when applied to various datasets. Meanwhile, as the number of core patents increased, the duplicated rates between the two methods gradually decreased. For example, the core patent duplication rates of method ① and method ④ dropped from 80% to 47%. [Limitations] We only investigated the common identification requirements. More research is needed to study those for specific and individualized areas. [Conclusions] The five constructed methods can be applied to different scenarios. For the rapidly developing field of artificial intelligence, the entropy weight method combining grey relational analysis and the entropy weight method with TOPSIS may yield better results.
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