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http://dx.doi.org/10.7469/JKSQM.2022.50.1.21

A Study on the Improvement of the Defense-related International Patent Classification using Patent Mining  

Kim, Kyung-Soo (Technology Evaluation Center, WIPS Co., Ltd.)
Cho, Nam-Wook (Dept. of Industrial Engineering, Seoul National University of Science and Technology)
Publication Information
Journal of Korean Society for Quality Management / v.50, no.1, 2022 , pp. 21-33 More about this Journal
Abstract
Purpose: As most defense technologies are classified as confidential, the corresponding International Patent Classifications (IPCs) require special attention. Consequently, the list of defense-related IPCs has been managed by the government. This paper aims to evaluate the defense-related IPCs and propose a methodology to revalidate and improve the IPC classification scheme. Methods: The patents in military technology and their corresponding IPCs during 2009~2020 were utilized in this paper. Prior to the analysis, patents are divided into private and public sectors. Social network analysis was used to analyze the convergence structure and central defense technology, and association rule mining analysis was used to analyze the convergence pattern. Results: While the public sector was highly cohesive, the private sector was characterized by easy convergence between technologies. In addition, narrow convergence was observed in the public sector, and wide convergence was observed in the private sector. As a result of analyzing the core technologies of defense technology, defense-related IPC candidates were identified. Conclusion: This paper presents a comprehensive perspective on the structure of convergence of defense technology and the pattern of convergence. It is also significant because it proposed a method for revising defense-related IPCs. The results of this study are expected to be used as guidelines for preparing amendments to the government's defense-related IPC.
Keywords
Defense Technology; International Patent Classification; Patent Mining; Social Network Analysis; Association Rule Mining; Evidence-Based Policy;
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Times Cited By KSCI : 5  (Citation Analysis)
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