PatentMatch：用于匹配专利权利要求和现有技术的数据集
PatentMatch: A Dataset for Matching Patent Claims & Prior Art
摘要：
专利审查员在评估专利申请中权利要求的新颖性和创造性时，需要解决复杂的信息检索任务。对于一项权利要求，他们会搜索现有技术，其中包括所有相关的公开信息。这项耗时的任务需要深入了解各自的技术领域和专利领域特定的语言。出于这些原因，我们通过创建用于监督机器学习的训练数据集（称为PatentMatch）来解决现有技术的计算机辅助搜索。它包含来自专利申请的成对权利要求和来自引用的专利文件的不同程度的语义对应的文本段落。每一对都由欧洲专利局技术熟练的专利审查员贴上标签。因此，标签指示语义对应（匹配）的程度，即，该文本段落是否有损于要求保护的发明的新颖性。使用基线系统的初步实验表明，PatentMatch确实可以用于在这个具有挑战性的信息检索任务上训练二进制文本对分类器。该数据集可在线获取：https://hpi.de/naumann/s/patentmatch。
英文摘要：
Patent examiners need to solve a complex information retrieval task when they assess the novelty and inventive step of claims made in a patent application. Given a claim, they search for prior art, which comprises all relevant publicly available information. This time-consuming task requires a deep understanding of the respective technical domain and the patent-domain-specific language. For these reasons, we address the computer-assisted search for prior art by creating a training dataset for supervised machine learning called PatentMatch. It contains pairs of claims from patent applications and semantically corresponding text passages of different degrees from cited patent documents. Each pair has been labeled by technically-skilled patent examiners from the European Patent Office. Accordingly, the label indicates the degree of semantic correspondence (matching), i.e., whether the text passage is prejudicial to the novelty of the claimed invention or not. Preliminary experiments using a baseline system show that PatentMatch can indeed be used for training a binary text pair classifier on this challenging information retrieval task. The dataset is available online: https://hpi.de/naumann/s/patentmatch.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
