Comparing technology convergence of artificial intelligence on the industrial sectors: two-way approaches on network analysis and clustering analysis

This study investigates technology convergence of AI considering both industrial sectors and technological characteristics with patent data in terms of two-way approaches: IPC-based network analyses and text-based clustering analysis. The IPC-based network analyses, which indicate a top-down approach in this study, focuses on influential technology area with hub nodes and their tie nodes in an IPC-based convergence network. A network centrality analysis is applied to determine the hub nodes which identify notable industrial sectors and influential technology. In addition, an ego-network analysis is conducted to examine the strongly related technology on the hub nodes. Meanwhile, from a bottom-up approach, a text-based clustering analysis is performed and the result shows an applied target of the technology and an integrated form of various technology which are not found from the top-down approach. Consequently, this study suggests new research framework to understand technology convergence based on the industrial sector, influential technology category, and technology application aspects. In line with the findings, this study analyzes technology convergence of AI by the notable industrial sectors: finance/management, medical, transport, semiconductor, game, and biotechnology sector. The results of this study suggest practical implications for AI technology and related industries.