Current patent claim generation systems face three fundamental limitations: poor cross-jurisdictional generalization, inadequate semantic relationship modeling between claims and prior art, and unreliable quality assessment. We introduce a novel three-stage framework that addresses these challenges through relationship-aware similarity analysis, domain-adaptive claim generation, and unified quality assessment. Our approach employs multi-head attention with eight specialized heads for explicit relationship modeling, integrates curriculum learning with dynamic LoRA adapter selection across five patent domains, and implements cross-attention mechanisms between evaluation aspects for comprehensive quality assessment. Extensive experiments on USPTO HUPD dataset, EPO patent collections, and Patent-CE benchmark demonstrate substantial improvements: 7.6-point ROUGE-L gain over GPT-4o, 8.3\% BERTScore enhancement over Llama-3.1-8B, and 0.847 correlation with human experts compared to 0.623 for separate evaluation models. Our method maintains 89.4\% cross-jurisdictional performance retention versus 76.2\% for baselines, establishing a comprehensive solution for automated patent prosecution workflows.

翻译：当前专利权利要求生成系统面临三个基本限制：跨司法管辖区泛化能力差、权利要求与现有技术之间语义关系建模不足，以及质量评估不可靠。我们提出了一种新颖的三阶段框架，通过关系感知相似性分析、领域自适应权利要求生成和统一质量评估来解决这些挑战。我们的方法采用具有八个专用头的多头注意力机制进行显式关系建模，在五个专利领域中结合课程学习与动态LoRA适配器选择，并实现评估维度间的交叉注意力机制以进行综合质量评估。在USPTO HUPD数据集、EPO专利集合和Patent-CE基准上的大量实验表明显著改进：相比GPT-4o获得7.6点的ROUGE-L提升，相比Llama-3.1-8B实现8.3%的BERTScore增强，与人类专家的相关性达到0.847（而独立评估模型为0.623）。我们的方法保持了89.4%的跨司法管辖区性能保留率（基线为76.2%），为自动化专利审查流程建立了全面解决方案。