Abstract
Classifying patents by their relevance to the UN Sustainable Development Goals (SDGs) is crucial for tracking how innovation addresses global challenges. However, the absence of a large, labeled dataset limits the use of supervised learning. Existing methods, such as keyword searches, transfer learning, and citation-based heuristics, lack scalability and generalizability. This paper frames patent-to-SDG classification as a weak supervision problem, using citations from patents to SDG-tagged scientific publications (NPL citations) as a noisy initial signal. To address its sparsity and noise, we develop a composite labeling function (LF) that uses large language models (LLMs) to extract structured concepts, namely functions, solutions, and applications, from patents and SDG papers based on a patent ontology. Cross-domain similarity scores are computed and combined using a rank-based retrieval approach. The LF is calibrated via a custom positive-only loss that aligns with known NPL-SDG links without penalizing discovery of new SDG associations. The result is a silver-standard, soft multi-label dataset mapping patents to SDGs, enabling the training of effective multi-label regression models. We validate our approach through two complementary strategies: (1) internal validation against held-out NPL-based labels, where our method outperforms several baselines including transformer-based models, and zero-shot LLM; and (2) external validation using network modularity in patent citation, co-inventor, and co-applicant graphs, where our labels reveal greater thematic, cognitive, and organizational coherence than traditional technological classifications. These results show that weak supervision and semantic alignment can enhance SDG classification at scale.
Abstract (translated)
根据联合国可持续发展目标（SDGs）的相关性对专利进行分类对于跟踪创新如何应对全球挑战至关重要。然而，缺乏大规模、标注的数据集限制了监督学习的应用。现有的方法，如关键词搜索、迁移学习和基于引用的启发式算法，在可扩展性和泛化能力方面存在不足。本文将专利到SDG的分类视为一个弱监督问题，并利用专利对带有SDG标签的科学出版物（非专利文献或NPL）的引用作为初始信号，尽管这些信号可能有噪声。 为了应对其稀疏性和噪音，我们开发了一个复合标记函数（LF），该函数使用大型语言模型（LLMs）根据专利语义学从专利和SDG论文中提取结构化的概念，例如功能、解决方案和应用。通过基于排名的检索方法计算跨域相似度分数，并将其组合起来。我们的标记函数通过一个定制的仅正标签损失进行校准，该损失与已知的NPL-SDG链接保持一致，同时不会惩罚新SDG关联的发现。 结果是一个银标准、软多标签数据集，将专利映射到SDGs，使有效的多标签回归模型得以训练。我们通过两种互补的方法验证了我们的方法：（1）内部验证，即与保留的NPL基线标签进行比较，在这种方法中，我们的方法优于包括基于变压器的模型和零样本LLM在内的多种基准；以及（2）外部验证使用专利引用、共同发明人和联合申请人网络中的模块性，其中我们的标签显示了比传统技术分类更高的主题、认知和组织一致性。这些结果表明，弱监督和语义对齐可以在大规模上增强SDG分类的准确性与有效性。
URL
https://arxiv.org/abs/2509.09303
