# PatentMatch: A Dataset for Matching Patent Claims & Prior Art

arXiv: https://arxiv.org/abs/2012.13919 · HTML: https://mlsanity.com/paper/2012.13919/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Dec 2020.

## Built on by (most cited first, 15 of 15)

- [Natural Language Processing in the Patent Domain: A Survey](https://mlsanity.com/paper/2403.04105/) (2024)
- [A Survey on Patent Analysis: From NLP to Multimodal AI](https://mlsanity.com/paper/2404.08668/) (2024)
- [Linguistically Informed Masking for Representation Learning in the Patent Domain](https://mlsanity.com/paper/2106.05768/) (2021)
- [Patent Data for Engineering Design: A Critical Review and Future Directions](https://mlsanity.com/paper/2111.08500/) (2021)
- [Enriching Patent Claim Generation with European Patent Dataset](https://mlsanity.com/paper/2505.12568/) (2025)
- [Self-Filtered Distillation with LLMs-generated Trust Indicators for Reliable Patent Classification](https://mlsanity.com/paper/2510.05431/) (2025)
- [PatentEval: Understanding Errors in Patent Generation](https://mlsanity.com/paper/2406.06589/) (2024)
- [Can AI Examine Novelty of Patents?: Novelty Evaluation Based on the Correspondence between Patent Claim and Prior Art](https://mlsanity.com/paper/2502.06316/) (2025)
- [PEDANTIC: A Dataset for the Automatic Examination of Definiteness in Patent Claims](https://mlsanity.com/paper/2505.21342/) (2025)
- [PATENTWRITER: A Benchmarking Study for Patent Drafting with LLMs](https://mlsanity.com/paper/2507.22387/) (2025)
- [Comparing Complex Concepts with Transformers: Matching Patent Claims Against Natural Language Text](https://mlsanity.com/paper/2407.10351/) (2024)
- [Formally Verified Patent Analysis via Dependent Type Theory: Machine-Checkable Certificates from a Hybrid AI + Lean 4 Pipeline](https://mlsanity.com/paper/2604.18882/) (2026)
- [Citation-Driven Multi-View Training for Patent Embeddings: QaECTER and Sophia-Bench](https://mlsanity.com/paper/2604.22897/) (2026)
- [QBD-RankedDataGen: Generating Custom Ranked Datasets for Improving Query-By-Document Search Using LLM-Reranking with Reduced Human Effort](https://mlsanity.com/paper/2505.04732/) (2025)
- [Self-Knowledge Retrieval Augmented Generation Framework for Patent Matching](https://mlsanity.com/paper/2608.11030/) (2026)

## Abstract (arXiv)

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.