In recent years, assessing patent risks has attracted fast-growing attention from both researchers and practitioners in studies of technological innovation. Following the existing literature on risks and intellectual property (IP) risks, we define patent risks as the lack of understanding of the distribution of patents that lead to losing a key patent, increased research and development costs, and, potentially, infringement litigation. This paper aims to propose an explorative approach to investigating patent risks in the target technology field by integrating social network analysis and patent analysis. Compared to previous research, this study makes an important contribution toward identifying patent risks in the overall technological field by employing a patent-based multi-level network model that has not appeared in existing methodologies of patent risks. In order to verify the effectiveness of this approach, we take artificial intelligence (AI) as an example. Data collected from the Derwent Innovation Index (DII) database were used to build the patent-based multi-level network on patent risks from market, technology, and assignee perspectives. The results indicate that the lack of international collaborations among assignees and industry–university–research collaboration may lead to patent collaboration risks. Regarding patent market risks, the lack of overseas patent applications, especially the lack of distribution in the main competitive markets, is a key factor. As for patent technology risks, most of the leading assignees lack awareness of the distribution in the following technological fields: industrial electric equipment, engineering instrumentation, and automotive electrics. In summary, assignees from the U.S. with first mover advantages are still powerful leaders in the AI technology field. Although China is catching up very rapidly in the total number of AI patents, the apparent patent risks under the perspectives of collaboration, market, and technology will obviously hamper the catch-up efforts of China’s AI industry. We conclude that, in practice, the proposed patent-based multi-level network model not only plays an important role in helping stakeholders in the AI technological field to prevent patent risks, find new technology opportunities, and obtain sustainable development, but also has significance for guiding the industrial development of various emerging technology fields.
Sustainable Development Utilizing Local Agricultural Resources: A Network Analysis of Interorganizational Collaborations in Tsuruoka, Noto, and Aso in Japan
