Identifying the Technology Opportunities and the Technology Taxonomy for Railway Static Inverters With Patent Data Analytics

Abstract

In this study, we identify and forecast promising railway static inverter technologies and the technology taxonomy using patent data analytics. To this end, we identify technology topics through LDA topic modeling and interpret them through N-gram analysis. We then derive detailed promising technology topics through time series analysis and technology mapping. Finally, we identify leading companies and research institutes in the field of promising technology topics through bibliography and social network analysis of patent applicants. Our study identified six technology topics, and fields related to converter technology were identified as detailed promising technologies. Japanese companies represented by Mitsubishi, Hitachi, and Toshiba are leading R&D in this field, while Chinese companies represented by CRRC have also secured technological competitiveness. Additionally, France’s Alstom is technically competitive when considering only the centrality index. Our study will contribute to its use as a meaningful reference for the establishment of railway R&D and the development of promising railway static inverter technologies.

Keywords

• Static inverter
• patent analytics
• semantic analytics
• social network analysis
• technological forecasting
• technology mapping