  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.
