Exploring Potential R&D Collaboration Partners Using Embedding of Patent Graph

Rapid market change is one of the reasons for accelerating a technology lifecycle. Enterprises have socialized, externalized, combined, and internalized knowledge for their survival. However, the current era requires ambidextrous innovation through the diffusion of knowledge from enterprises. Accordingly, enterprises have discovered sustainable resources and increased market value through collaborations with research institutions and universities. Such collaborative activities effectively improve enterprise innovation, economic growth, and national competence. However, as such collaborations are conducted continuously and iteratively, their effect has gradually weakened. Therefore, we focus on exploring potential R&D collaboration partners through patents co-owned by enterprises, research institutions, and universities. The business pattern of co-applicants is extracted through a patent graph, and potential R&D collaboration partners are unearthed. In this paper, we propose a method of converting a co-applicant-based graph into a vector using representation learning. Our purpose is to explore potential R&D collaboration partners from the similarity between vectors. Compared to other methods, the proposed method contributes to discovering potential R&D collaboration partners based on organizational features. The following questions are considered in order to discover potential R&D partners in collaborative activities: Can information about co-applicants of patents satisfactorily explain R&D collaboration? Conversely, can potential R&D collaboration partners be discovered from co-applicants? To answer these questions, we conducted experiments using autonomous-driving-related patents. We verified that our proposed method can explore potential R&D collaboration partners with high accuracy through experiments.