A Multi-View Attention–Similarity Decision-Support Framework for Core Feature Recognition in Design Patent Infringement Assessment

Identifying core features is a crucial step in design patent infringement assessments (DPIAs), as the identification results serve as a vital basis for subsequent qualitative and quantitative assessments. Traditional DPIAs typically rely on the experience and knowledge of experts and the judge’s discretion, resulting in significant uncertainty and subjectivity. This can easily lead to inconsistent judgments in similar cases, hindering technological innovation and potentially triggering social conflicts. To address this, this paper proposes an intelligent identification framework for core features of patents that integrates multi-angle attention recognition and novelty calculation. First, design patents are downloaded, and views are extracted to construct a database. Simultaneously, the Canny operator is used to extract the product’s contour features. Second, a CBAM-ResNet50 model is constructed, trained and optimized using transfer learning to achieve quantitative identification of salient features. Subsequently, the VGG16 model combined with cosine similarity is used to calculate product feature similarity, and the results from both methods are combined to comprehensively determine the core features. Experiments are conducted using a showerhead as an example, with 400 patents randomly selected as the development set. The results show that the proposed CBAM-ResNet50 model significantly outperforms other comparative models in terms of Intersection over Union (IOU) and Dice Similarity Coefficient (DSC), and the VGG16 model combined with the cosine similarity algorithm achieves the highest discriminative power for product front view contour. Combining the two methods yields a validation set of core features, which are then evaluated by an expert panel. The expert endorsement rate (EEP) on the final test set is 86.5% (173/200; the Wilson confidence interval is 81.1% to 90.6%). This research not only provides a quantifiable and interpretable decision-support tool for DPIA but also offers feasible technical support for intelligent intellectual property examination and even product innovation design.