用大模型比对专利权要求与现有技术，评估新颖性
Can AI Examine Novelty of Patents?: Novelty Evaluation Based on the Correspondence between Patent Claim and Prior Art
- 让大模型模仿审查员，对比专利权利要求与引用文献
- 生成式模型准确率尚可，解释内容能说明技术关联
- 首个专门用于新颖性评估的真实案例数据集
评估专利权利要求的新颖性是一项关键但极具挑战的任务，传统上由专利审查员完成。尽管自然语言处理技术在诸多专利相关任务中取得进展，但新颖性评估仍属空白。本文提出一项新挑战：通过将专利权利要求与引用的现有技术文档进行对比，评估大语言模型（LLMs）在类似审查员流程下的新颖性判断能力。我们构建了首个专为新颖性评估设计的数据集，基于真实专利审查案例，并分析了各类模型在此任务上的表现。研究发现，分类模型难以有效评估新颖性，而生成式模型则能做出合理准确的判断，其生成的解释也足够清晰，能够理解目标专利与现有技术之间的关系。这些结果表明，大语言模型有潜力辅助专利审查，减轻审查员与申请人的工作负担。本研究揭示了当前模型的局限性，为未来通过先进模型与优化数据集提升AI驱动的专利分析奠定了基础。
原文摘要 · Abstract (English)
Assessing the novelty of patent claims is a critical yet challenging task traditionally performed by patent examiners. While advancements in NLP have enabled progress in various patent-related tasks, novelty assessment remains unexplored. This paper introduces a novel challenge by evaluating the ability of large language models (LLMs) to assess patent novelty by comparing claims with cited prior art documents, following the process similar to that of patent examiners done. We present the first dataset specifically designed for novelty evaluation, derived from real patent examination cases, and analyze the capabilities of LLMs to address this task. Our study reveals that while classification models struggle to effectively assess novelty, generative models make predictions with a reasonable level of accuracy, and their explanations are accurate enough to understand the relationship between the target patent and prior art. These findings demonstrate the potential of LLMs to assist in patent evaluation, reducing the workload for both examiners and applicants. Our contributions highlight the limitations of current models and provide a foundation for improving AI-driven patent analysis through advanced models and refined datasets.
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