While the state of technology at country level has been compared by using patent information, the difference of innovation process, how the inventions are converted into actual products and services, are under investigated. Historically, the relationship between technology and the market has been analyzed by using technology-industry concordance matrix, but the granularity of market information is fined by industrial classification system. In this study, we use both patent and web contents information to estimate key word level detail innovation conversion model, and compare those across three countries, China, Japan and the United States. First, we apply dual attention model to extract product/service information out of web page information (Motohashi and Zhu, 2023). Then, using the textual information of both patent abstracts and product/service keywords, we develop the machine learning model to predict product/service from some particular type of technology. Finally, we compare the actual product/service vector and predicted product/service vector by cosine similarity to see the innovation transformation process is different by countries.
Ansprechpartner: Daehyun Kim
Eintragung in den Einladungsverteiler und mehr Informationen auf der Seminarseite.