# Comparing Complex Concepts with Transformers: Matching Patent Claims Against Natural Language Text

arXiv: https://arxiv.org/abs/2407.10351 · HTML: https://mlsanity.com/paper/2407.10351/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 6 references that are in the map.
- First posted on arXiv 14 Jul 2024.

## Builds on (most cited first, 4 of 4)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Large Language Models are Effective Text Rankers with Pairwise Ranking Prompting](https://mlsanity.com/paper/2306.17563/) (2023)
- [PatentMatch: A Dataset for Matching Patent Claims & Prior Art](https://mlsanity.com/paper/2012.13919/) (2020)
- [A Text-Embedding-based Approach to Measure Patent-to-Patent Technological Similarity -- Workflow, Code, and Applications](https://mlsanity.com/paper/2003.12303/) (2020)

## Built on by (most cited first, 1 of 1)

- [PEDANTIC: A Dataset for the Automatic Examination of Definiteness in Patent Claims](https://mlsanity.com/paper/2505.21342/) (2025)

## Abstract (arXiv)

A key capability in managing patent applications or a patent portfolio is comparing claims to other text, e.g. a patent specification. Because the language of claims is different from language used elsewhere in the patent application or in non-patent text, this has been challenging for computer based natural language processing. We test two new LLM-based approaches and find that both provide substantially better performance than previously published values. The ability to match dense information from one domain against much more distributed information expressed in a different vocabulary may also be useful beyond the intellectual property space.