An improved solution for partner selection of industry-university cooperation

Abstract

Industry-University Cooperation (IUC) is recognized as an effective model for technological innovation, helping small and medium-sized enterprises (SMEs) to seek opportunities from technological development and to achieve sustainable competitiveness. The study proposes an improved solution to resolve the 4W questions commonly faced by SMEs, including identifying cooperative topics, comparing technology competitors, evaluating cooperative universities, and selecting research teams. Firstly, the Latent Dirichlet Allocation model is applied to extract subtechnology topics in a specific technology domain, and the patent clustering is analyzed via Kmeans algorithm. Secondly, competitive and uncompetitive sub-technologies of the enterprise, as well as the geographical distribution of competitors in sub-technologies are analyzed. Thirdly, the study proposes a new topic matching index (TMI) to help determine the best cooperative universities. Finally, the cooperation network is visualized to help identify the specific core research teams for each specific technology. The research team conducted an experimental study using real patent data on a “Blockchain” technology enterprise to verify the proposed solution.