Optimization of Deep Learning-Based Patent Infringement Text Comparison and Retrieval Algorithms

INTRODUCTION: Patent texts may present challenges in accurately identifying potential patent infringement risks due to subtle differences in technical features. OBJECTIVES: Deep learning effectively models the complex semantic structures and local feature correlations within patent texts by integrating deep semantic representations and deep matching mechanisms, thereby enhancing sensitivity to minute technical variations and improving decision support for infringement risk assessment. OBJECTIVES: this study explores deep learning-based optimization methods for patent infringement risk text comparison and retrieval algorithms. A macro-level patent text comparison and retrieval layer is constructed by integrating Word2vec and LDA topic models. This layer models patent text semantics, generates word-level semantic vectors for patents, and enables rapid comparison and retrieval of candidate patent sets related to the target patent at the technical feature semantic level. A COV-BiGRU twin neural network is introduced as the fine-grained comparison and retrieval layer. This layer optimizes the macro-level retrieval algorithm by performing granular semantic matching and Manhattan distance calculations between candidate patent texts and the target patent text. Based on these results, patent infringement risk retrieval are retrieved. RESULTS: Results demonstrate that this method reduces the candidate patent text set size to 0.457% of the full database during macro-level retrieval. CONCLUSION: In the fine-grained retrieval optimization phase, it achieves 100% recall of known high-risk patents while effectively excluding non-infringing patents that share similar technical themes but differ in specific technical features. This validates the preliminary effectiveness of the optimized dual-layer retrieval algorithm for texts with fine-grained differences on the tested patent corpus. This validates the preliminary effectiveness of the optimized dual-layer retrieval algorithm for texts with fine-grained differences on the tested patent corpus.