An electric motor is a device that changes electrical energy into mechanical energy for railway vehicles. When developing the electric motor, it used to be developed simply for structures or control methods of the motor itself without considering convergence with other devices or technologies. However, as the railway vehicles become more advanced, technology development through convergence with other devices or technologies is spreading. Therefore, based on patent data related to the electric motors applied to the railway vehicles, this research aims to carry out technical forecasting for establishing research and development (R and D) direction for new technologies by predicting vacant technologies from the point of view of technology convergence. In other words, we studied how to find the vacant technologies in a field of convergence technology for the electric motor of the railway vehicles by analyzing the patent data. More specifically, we search the patents data associated with the electric motor of the railway vehicle that contain multiple IPC codes, and use multiple IPC codes to determine the field of convergence technology. In addition, we extract keywords from the patents data related to each of the determined convergence technologies and define the vacant technologies by interpreting the field of convergence technology and the extracted keywords.

In this study, we identify promising, currently vacant technologies for a Truck on Flatcar or Truck on Freight Train (TFTFT) system by analyzing the relevant patent information. We then apply network analysis from macro- and microperspectives to establish technology development strategies. We first researched the patent database from the United States Patent and Trademark Office (USPTO) by extracting relevant keywords for the TFTFT system. We then preprocessed the patent data to develop a patent-International Patent Classification (IPC) matrix and a patent-keyword matrix. Next, we developed a generative topographic mapping (GTM)-based patent map using the patent-IPC matrix and detected any patent vacuums. Then, in order to confirm the promising patent vacuums, we technically examined criticality and trend analyses. Finally, we designed an IPC-based network and a keyword network with promising patent vacuums to derive a technology development strategy from a macro- and microperspective for the TFTFT system. As a result, we confirmed two promising patent vacuums. The patent vacuums found were defined as the technical field of rail vehicles suitable for TFTFT systems and the technical field of equipment and systems for freight transfer to rail vehicles. The proposed procedure and analysis method provide useful insights for developing a research and development (R&D) strategy and technology development strategy for a TFTFT system.