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: this https URL

本文介绍了一个名为 PatentMatch 的机器学习训练数据集，用于协助专利审查员快速检索相关先前公开信息，其数据集包含有标签的专利申请索赔对和语义上对应的先前公开信息，可用于解决语言障碍和技术域障碍，初步实验结果显示该数据集可用于解决这一复杂的信息检索任务。