Patent Data Analytics for Technology Forecasting of the Railway Main Transformer

Abstract

The railway main transformer is considered one of the most important electrical equipment for trains. Companies and research institutes around the world are striving to develop high-performance railway main transformers. In order to be the first mover for railway main transformer technology, companies and research institutes should predict vacant technology based on the analysis of promising detailed technology areas. Therefore, in this study, a patent analysis to predict vacant technologies based on identified promising IPC technology areas is provided. In order to identify promising detailed IPC technology areas, the technology mapping analysis, the time series analysis, and the social network analysis are conducted based on the patent-IPC matrix, extracted from the data information of 707 patents from the patent database of Korea, China, Japan, United States, Canada, and Europe. Then, through the GTM analysis based on promising detailed IPC technology areas, one vacant technology node and three analysis target nodes surrounding the vacant technology node are obtained to predict vacant technologies. From the analysis, we predict the following three groups of vacant technologies: (1) blowerless technology, (2) oil-free technology, and (3) solid-state technology. This study provides insights on the technology trend in railway main transformers, as well as the analysis framework for the development of R&D strategies based on the patent data.

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

“…Son and Cho [14] analyze the technology fusion phenomena and its characteristics, focusing on the solar photovoltaic (PV) industry in South Korea. Lee et al [15] conducted patent data analysis for vacant technology prediction based on promising IPC technology fields. Kwon et al [16] aim to propose a new framework for the extension to the stage for establishing a technology roadmap based on patent analysis.…”

“…We can build a variety of matrices depending on documents and technical keywords. Document-Term Matrix can be constructed using the frequency of appearance of technical keywords corresponding to each document [15], [34]. The building of a Document-Term Matrix (DTM) is important because it is used in LDA topical modeling, technology mapping analysis, and social network analysis, which are analysis methodologies used in this study [35][36][37].…”

“…In this study, we converted the annual appearance frequency of patents for each technology topic into time series data and predicted the trend of future applications for each field through time series analysis using the ARIMA model [71], [72]. Figure 4 above is an example of the results of predicting fields with Hot, Active, and Cold according to future trends derived through ARIMA analysis for each technology topic [15].…”

“…To overcome this, this study aims to supplement it by utilizing the PFS (Patent Family Size) index. The PFS index is an indicator calculated by considering the number of the same patents filed in each country for one patent and is used to measure the marketability of a patent [15], [76]. The PFS index is calculated using the following Equation 5.…”