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

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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.

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Identifies potential convergence areas in dental medical device patents, suggesting new innovation avenues.

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Yoo et al. (2026) studied this question.

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