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| Technical Clustering Based on Patent Synthetic Citations: Case Study in Artificial Intelligence |
| Gan Jingxian1, Tian Chen2, Wang Yu3 |
1.School of Intellectual Property, Dalian University of Technology, Dalian 116024
2.School of Management, Dalian Polytechnic University, Dalian 116034
3.School of Business, Dalian University of Technology, Panjin 124221 |
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Abstract To address the limitations inherent in actual citation data for patent technology topic clustering, specifically data sparsity and inadequate representation of technical relevance, a “Synthetic Citation” construction algorithm based on full-text similarity was designed in this study. Furthermore, by integrating the logic of bibliographic coupling from bibliometrics, “Synthetic Citation Bibliographic Coupling” clustering model was developed. This approach was aimed at reconstructing the technical citation network of patents in a virtual semantic space by mining latent semantic associations. The findings revealed that, compared to actual citation networks, the synthetic citation network effectively captured latent technical associations overlooked by conventional methods, and also significantly enhanced the measurement precision of technical relevance between patents. The empirical results demonstrated that the clustering outcomes obtained using this method can achieve substantial improvements in both the integrity of topic identification and classification accuracy.
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Received: 25 September 2025
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