Research on Key Technological Breakthroughs of Natural Language Processing Technology in Intelligent Review of Intellectual Property Texts

Abstract

Intelligent review of intellectual property texts is the core path to break through the efficiency bottleneck of traditional review models and improve review quality. As the core support for text semantic parsing and knowledge mining, the application depth of natural language processing (NLP) technology directly determines the level of intelligent review. This paper systematically analyzes the scene characteristics and technical requirements of intellectual property text review, with a focus on the key breakthrough directions of NLP technology in this field: from the precision of structured extraction of technical features, the depth of cross-modal semantic association, to the systematization of the integration of legal and technical knowledge, and then to the optimization of the interpretability of review decisions. Based on typical practical cases such as "Copyright AI Intelligent Review" in Keqiao and incoPat AI Retrieval, a technical framework of "text parsing - knowledge modeling - intelligent reasoning - decision output" is constructed to reveal the breakthrough paths of technical bottlenecks in each link and look forward to the technological development trends in the era of large models. Provide theoretical support and practical reference for the research and development and optimization of the intelligent intellectual property review system.