Deep Technology Tracing for HighTech Companies Technological change and innovation are vitally important, especially for hightech companies. However, factors influencing their future research and development (Ru0026D) trends are both complicated and various, leading it a quite difficult task to make technology tracing for hightech companies. To this end, in this paper, we develop a novel datadriven solution, i.e., Deep Technology Forecasting (DTF) framework, to automatically find the most possible technology directions customized to each hightech company. Specially, DTF consists of three components: Potential Competitor Recognition (PCR), Collaborative Technology Recognition (CTR), and Deep Technology Tracing (DTT) neural network. For one thing, PCR and CTR aim to capture competitive relations among enterprises and collaborative relations among technologies, respectively. For another, DTT is designed for modeling dynamic interactions between companies and technologies with the above relations involved. Finally, we evaluate our DTF framework on realworld patent data, and the experimental results clearly prove that DTF can precisely help to prospect future technology emphasis of companies by exploiting hybrid factors.

Investigating Problems of Research and Development of Artificial Intelligence Technology in Japan Japan is behind the United States in industrialization of Artificial Intelligence (AI). Thus, we must clarify the problems with AI research and development (Ru0026D) in Japan. In the field of technology management, many studies have examined Ru0026D activities using scientific papers or patent data, which represent the output of basic research and applied research, respectively. We attempt to reveal problem with the Ru0026D of AI technology in Japan by analyzing data pertaining to patents and scientific papers, focusing on the linkage between basic and applied research. We demonstrate that Japan is weak in terms of patent clusters with no related to paper clusters. In addition, regarding patent clusters that are related to one or more paper clusters, we demonstrate that Japan has both strengths and weaknesses in these fields. Our findings could provide suggestions for Ru0026D strategy and policy related to AI.