摘要
The substantial increase in patent applications has created notable challenges in retrieving, analyzing, and managing patent data. According to the World Intellectual Property Indicators (WIPI) report published by the World Intellectual Property Organization (WIPO) in 2024, the total number of patent applications worldwide surpassed 3.55 million in 2023. Traditional manual methods for extracting and interpreting key patented knowledge are usually time-consuming, expensive, subjective, and lack validation. To address the rise in patent filings and the growing need for effective patent knowledge management, we have developed an intelligent patent summarization system that utilizes large language model (LLM) technology to enhance the understanding and usability of patent documents. This research uses patents related to advanced vehicle-to-everything (V2X) technologies as case studies. Through empirical analysis, we show that the system can automatically condense large amounts of patent documents into concise and meaningful summaries. This intelligent patent summarization system runs efficiently on consumer-grade computers. Experimental results indicate that its semantic structure achieves nearly 90 % similarity compared to patents written by domain experts. This research aims to enhance the efficiency, accuracy, and accessibility of patent document processing, thereby significantly advancing the practical application of this technology.