用图注意力网络分析专利条款依赖关系，提升诉讼风险预测准确率。
Modeling Claim Dependency Structure for Patent Litigation Prediction with Graph Attention Networks
- 独立编码每条专利条款，构建有向依赖图，用图注意力网络建模结构关系。
- 预测AUC达0.818，前10%召回提升4.89倍，仅需专利授权时信息。
- 可解释高风险专利的结构选择与内容敏感性分化，适合专利风控与法律科技应用。
专利诉讼给企业带来巨大成本并扭曲研发激励，早期识别风险具有实际意义。以往基于BERT的方法存在两大局限：平坦序列编码丢失独立与从属条款间的法律依赖结构，且将全部条款输入单一编码器会忽略关键文本。对134万件美国实用专利的六模型消融实验表明，逐条款编码、图连通性、注意力机制和注意力聚合各自提供独立且可叠加的预测价值。本文提出ClaimGAT，一种图注意力网络，独立编码每条条款，构建有向条款依赖图，通过GATConv层处理，并以注意力聚合生成诉讼风险评分及条款级门控权重，支持事后结构分析。ClaimGAT在仅使用专利授权时可见信息的前提下，实现0.818的AUC-ROC和前10%处4.89倍的提升。结果揭示高风险专利普遍存在结构选择与内容敏感性分离的现象，符合防御性撰写特征。
原文摘要 · Abstract (English)
Patent litigation imposes substantial costs on firms and distorts R&D incentives, making early risk identification a practically important task. While prior work has applied BERT-based models to patent claim text, two fundamental limitations remain: flat sequence encoding loses the dependency structure between independent and dependent claims that legally determines patent scope, and feeding the entire claim set to a single encoder discards legally critical text. A six-model ablation on 1.34 million USPTO utility patents confirms that per-claim encoding, graph connectivity, attention, and Attentional Aggregation each provide independent, additive predictive value. We propose ClaimGAT, a Graph Attention Network that encodes each claim independently, constructs a directed claim dependency graph, processes it with GATConv layers, and aggregates independent claims via Attentional Aggregation to yield both a litigation risk score and claim-level gate weights that enable post-hoc structural analysis. ClaimGAT achieves an AUC-ROC of 0.818 and a lift of 4.89x at the top 10%, using only information observable at the time of patent grant. It reveals a tendency in high-risk patents for structural selection and content sensitivity to diverge, a pattern consistent with defensive claim drafting.
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