精准识别中美AI专利，揭示创新模式差异与技术依赖关系
AI Patents in the United States and China: Measurement, Organization, and Knowledge Flows
- 用微调后的PatentSBERTa模型高精度识别中美专利中的AI技术
- 中美AI专利数量快速上升，近年中国年申请量超美国，但美国仍主导前沿知识
- 中国创新更分散，高校和国企作用大；中美企业均获显著专利溢价
我们通过在美国专利商标局（USPTO）的AI专利数据集上微调PatentSBERTa，构建了一个高精度的AI专利分类器。该模型在测试中达到97.0%精确率、91.3%召回率和94.0% F1分数，且在中文专利中表现出良好的泛化能力。基于此，我们分析了1976-2023年美国已授权专利及2010-2023年中国专利数据，发现两国AI专利数量均快速增长，子领域结构趋于趋同，尽管近年来中国年度专利申请量已超过美国。然而，创新组织形态差异明显：美国以大型私营企业和成熟创新中心为主，而中国则呈现更广域的地理分布和更多元的机构参与，高校与国有企业的角色更为突出。对于上市公司而言，两国均存在显著的AI专利市场价值溢价。跨国家引用显示技术持续相互依赖，而非脱钩，且中国对美国前沿知识的依赖程度高于美国对中国。
原文摘要 · Abstract (English)
We develop a high-precision classifier to measure artificial intelligence (AI) patents by fine-tuning PatentSBERTa on manually labeled data from the USPTO's AI Patent Dataset. Our classifier substantially improves the existing USPTO approach, achieving 97.0% precision, 91.3% recall, and a 94.0% F1 score, and it generalizes well to Chinese patents based on citation and lexical validation. Applying it to granted U.S. patents (1976-2023) and Chinese patents (2010-2023), we document rapid growth in AI patenting in both countries and broad convergence in AI patenting intensity and subfield composition, even as China surpasses the United States in recent annual patent counts. The organization of AI innovation nevertheless differs sharply: U.S. AI patenting is concentrated among large private incumbents and established hubs, whereas Chinese AI patenting is more geographically diffuse and institutionally diverse, with larger roles for universities and state-owned enterprises. For listed firms, AI patents command a robust market-value premium in both countries. Cross-border citations show continued technological interdependence rather than decoupling, with Chinese AI inventors relying more heavily on U.S. frontier knowledge than vice versa.
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