用大模型自动优化专利权利要求，提升审查通过率
ClaimBrush: A Novel Framework for Automated Patent Claim Refinement Based on Large Language Models
- 基于真实审查案例构建专利权利要求重写数据集，微调大模型实现自动化重写
- 相比现有方法，模型在重写准确率上显著提升，且经审查员偏好优化后效果更优
- 适合知识产权团队、专利代理人用于高效撰写高质量专利文件
从知识产权战略角度出发，专利申请中权利要求的自动化优化至关重要。本文提出ClaimBrush框架，包含一个数据集和一个重写模型。通过收集大量实际专利审查过程中的权利要求修改案例，构建了用于训练和评估专利权利要求重写模型的数据集。基于该数据集，我们微调大语言模型，构建了自动专利权利要求重写模型。此外，通过引入基于专利审查员意见预测模型的偏好优化策略，进一步提升了模型性能。实验结果表明，所提重写模型在准确率上优于启发式基线和零样本大语言模型；基于审查员偏好的优化显著增强了权利要求优化效果。
原文摘要 · Abstract (English)
Automatic refinement of patent claims in patent applications is crucial from the perspective of intellectual property strategy. In this paper, we propose ClaimBrush, a novel framework for automated patent claim refinement that includes a dataset and a rewriting model. We constructed a dataset for training and evaluating patent claim rewriting models by collecting a large number of actual patent claim rewriting cases from the patent examination process. Using the constructed dataset, we built an automatic patent claim rewriting model by fine-tuning a large language model. Furthermore, we enhanced the performance of the automatic patent claim rewriting model by applying preference optimization based on a prediction model of patent examiners' Office Actions. The experimental results showed that our proposed rewriting model outperformed heuristic baselines and zero-shot learning in state-of-the-art large language models. Moreover, preference optimization based on patent examiners' preferences boosted the performance of patent claim refinement.
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