Main path analysis for technological development using SAO structure and DEMATEL based on keyword causality

• Published in: Scientometrics 2023-04, Vol.128 (4), p.2079-2104
• Main Authors: Oh, Myeongji, Jang, Hyejin, Kim, Sunhye, Yoon, Byungun
• Format: Article
• Language: English
• Subjects: Autonomous cars; Autonomous vehicles; Causality; Computer Science; Decision making; Information Storage and Retrieval; Library Science
• ISSN: 0138-9130; 1588-2861
• DOI: 10.1007/s11192-023-04652-2

Main path analysis (MPA) is a method for efficiently analyzing technological trends, which change rapidly in competitive environments. In general, MPA is based on citation networks, and it is used to derive the most key path in a complex network. However, the existing studies using MPA do not use important textual information of patents, except for citation data. In this paper, we suggest a new MPA based on patent documents to identify the main path of technological evolution. For this purpose, first, we used the subject-action-object structure to derive core keywords based on causal relationships in patent claims. Second, the DEcision-MAking Trial and Evaluation Laboratory (DEMATEL) technique was applied to draw link weights between patents where causal relationships of keywords were reflected. Finally, a main path in a patent network was identified using the global main path and key-route main path analysis methods. In this paper, we collected and analyzed patent data related to self-driving car technologies, and we verified the technical changes in the main path obtained based on the proposed approach. We found that the generic technologies of the self-driving operation had the strongest influence on the other self-driving car technologies in the sensing-planning-acting steps.