构建可复现的专利新颖性检索评估框架，提升系统优化依据
Research on Evaluation Methods for Patent Novelty Search Systems and Empirical Analysis
- 基于审查员引用和同族专利引用构建高质量评估数据集
- 多维度分析显示系统在不同语言、技术领域表现差异显著
- 提供可扩展的评估方案，适合专利系统开发者与研究者使用
专利新颖性检索系统对知识产权保护与创新评估至关重要，其检索准确率直接影响专利质量。本文提出一种综合性评估方法，通过提取技术一致的同族专利中的审查员引用与X类引用，构建高质量、可复现的评估数据集，并以发明描述为输入评估系统性能。采用Top-k检测率与召回率作为核心指标，进一步按语言、技术领域（IPC）和申请国别进行多维分析。实验表明该方法能有效揭示系统在不同场景下的性能差异，为系统改进提供可行动证据。该框架具备可扩展性与实用性，为专利新颖性检索系统的研发与优化提供重要参考。
原文摘要 · Abstract (English)
Patent novelty search systems are critical to IP protection and innovation assessment; their retrieval accuracy directly impacts patent quality. We propose a comprehensive evaluation methodology that builds high-quality, reproducible datasets from examiner citations and X-type citations extracted from technically consistent family patents, and evaluates systems using invention descriptions as inputs. Using Top-k Detection Rate and Recall as core metrics, we further conduct multi-dimensional analyses by language, technical field (IPC), and filing jurisdiction. Experiments show the method effectively exposes performance differences across scenarios and offers actionable evidence for system improvement. The framework is scalable and practical, providing a useful reference for development and optimization of patent novelty search systems
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