Research on Similar Patent Identification Based on Multimodal Feature Fusion
[Purpose/Significance]The burgeoning number of patents poses significant challenges to patent retrieval,highlighting the urgent need for advanced computational techniques to identify similar patents.[Method/Process]This paper proposed a multimodal feature fusion method for similar patent identification.It utilized the BERT-wwm model and the ResNet-50 model to extract textual and image features of patents,respectively.By inte-grating self-attention and cross-attention mechanisms,the method effectively harnessed intra-modal feature infor-mation and inter-modal interaction information.Based on these,the model was trained and optimized for the similar patent identification.[Result/Conclusion]Empirical tests using IPC category"C08F10/00"data demonstrate that the model achieves an accuracy of 80.03％and a recall rate of 82.01％,outperforming baseline models.In simula-tions of similar patent identification,the model reaches a recall rate of 88.89％,indicating superior practical perfor-mance.The fusion of textual and image modal features significantly enhances the accuracy and efficiency of similar patent identification.This approach facilitates improved patent retrieval efficiency,accelerates the patent examina-tion process,aids in patent alert analysis,and strengthens intellectual property protection.
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