스마트팩토리 특허 데이터를 활용한 기술주제 모델링 및 국가별 기술 네트워크 분석
Topic Modeling and Cross-National Analysis of Technology Network Using Smart Factory Patent Data
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Smart factory technology, a core component of the Fourth Industrial Revolution, demonstrates significant disparities in techno- logical development across countries. To quantitatively assess these international technology gaps, this study proposes an integrated analytical framework that combines text mining-based topic modeling and social network analysis (SNA), using global smart factory-related patent data from 2017 to 2023. Approximately 4,300 patent documents (titles and abstracts) were collected through the GPASS system and preprocessed. Through Latent Dirichlet Allocation (LDA) modeling with optimized hyperparameters, major technology topics were identified. Semantic interpretation using ChatGPT and expert review enabled the assignment of precise topic labels, which were further mapped to CPC (Cooperative Patent Classification) codes to construct a standardized technology taxonomy. Subsequently, the network structures of topic and classification nodes were analyzed by country (China, the United States, and South Korea), and the relative importance of key technology areas was evaluated using centrality metrics such as degree, closeness, betweenness, and eigenvector centrality. The analysis revealed that, globally, the most central technology areas include manufacturing process management and control, IoT and data-driven decision making, and facility-based process optimization. At the national level, China showed a strategic focus on technologies related to product quality improvement and cost reduction, South Korea emphasized IoT-enabled technologies and equipment-level optimization, while the United States priori- tized control systems and data-driven project management. By utilizing patent-based textual data, this study offers a novel method- ology for quantitatively diagnosing structural differences in national technological capabilities. The proposed framework provides valuable insights for country-specific R&D planning and strategic decision-making in the field of smart manufacturing.
Abstract
Smart factory technology, a core component of the Fourth Industrial Revolution, demonstrates significant disparities in techno- logical development across countries. To quantitatively assess these international technology gaps, this study proposes an integrated analytical framework that combines text mining-based topic modeling and social network analysis (SNA), using global smart factory-related patent data from 2017 to 2023. Approximately 4,300 patent documents (titles and abstracts) were collected through the GPASS system and preprocessed. Through Latent Dirichlet Allocation (LDA) modeling with optimized hyperparameters, major technology topics were identified. Semantic interpretation using ChatGPT and expert review enabled the assignment of precise topic labels, which were further mapped to CPC (Cooperative Patent Classification) codes to construct a standardized technology taxonomy. Subsequently, the network structures of topic and classification nodes were analyzed by country (China, the United States, and South Korea), and the relative importance of key technology areas was evaluated using centrality metrics such as degree, closeness, betweenness, and eigenvector centrality. The analysis revealed that, globally, the most central technology areas include manufacturing process management and control, IoT and data-driven decision making, and facility-based process optimization. At the national level, China showed a strategic focus on technologies related to product quality improvement and cost reduction, South Korea emphasized IoT-enabled technologies and equipment-level optimization, while the United States priori- tized control systems and data-driven project management. By utilizing patent-based textual data, this study offers a novel method- ology for quantitatively diagnosing structural differences in national technological capabilities. The proposed framework provides valuable insights for country-specific R&D planning and strategic decision-making in the field of smart manufacturing.