A Patent Infringement Analysis Approach Based on Patent Knowledge Graph Driven Fusion of Graph and Image Similarity

Patent infringement analysis (PIA) represents a critical task in patent circumvention design. It aims to identify the likelihood of infringement for target technologies to enhance product innovation, serving as a crucial measure for technical protection. Traditional PIA processes rely on examiners’ subjective judgments on the patent’s technical advantages and textual similarity, leading to the infringement results heavily dependent on personal experience. Pervious similarity calculation models based on keywords or textual content lack the utilization of knowledge of functions, structures, and features within patents and their interrelations, resulting in inaccurate assessments of technological infringement for similar patents. Furthermore, the potential of patent images in infringement analysis remains underutilized. To overcome the issues, a patent knowledge graph (PKG) driven patent similarity calculation model fusing graph similarity (GS) and image similarity (IS) is proposed to achieve the prediction of the probability of patent infringement using patent text and structure images as multimodal data. First, an ontology model based on requirement-function-structure-location features (RFSL) is constructed. Four entity types are extracted from patent texts and images using a fine-tuned named entity recognition (NER) model combined with semantic relation analysis. Second, syntactic matching rules for eight types of entity relationships are constructed to extract triples, mapping patent texts into graph networks via the PKG. Finally, the integrated Graphical Neural Network (GNN) and Convolutional Neural Network (CNN) are integrated to calculate the overall similarity between the newly filed patent and the comparison patent to output the infringement probability. A case study of steel pipe welding device design is used to validate the proposed approach, then the comparison results confirm the potential of fusion GS and IS in the application in PIA.