Structural Analysis of CPC Co-classification Networks and Forecasting of Convergence Technologies in Dental Medical Device Patents: Application of a Node2Vec-based Link Prediction

Key Points

• This study aims to elucidate the structure of convergence technologies in dental medical devices and quantitatively predict potential fusion areas.
• Collected and refined dental medical device patents from the US, Europe, and Korea from 2010 to 2024.
• Constructed CPC co-classification networks using 4-digit CPC classification data and applied Louvain clustering algorithm.
• Applied a Node2Vec-based link prediction model to estimate the likelihood of unseen CPC combinations during the validation period (2020–2024).
• Identified five key fusion technology groups: digital imaging and AI diagnostics, 3D printing and CAD/CAM, biomaterials and implants, smart sensors and IoT, traditional dental equipment and materials.
• Showed strong evidence of the predictive model's performance using time-series validation with emerging co-classification relationships during the validation period.