# Automating the search for a patent's prior art with a full text similarity search

arXiv: https://arxiv.org/abs/1901.03136 · HTML: https://mlsanity.com/paper/1901.03136/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jan 2019.

## Built on by (most cited first, 7 of 7)

- [Natural Language Processing in the Patent Domain: A Survey](https://mlsanity.com/paper/2403.04105/) (2024)
- [PatentMind: A Multi-Aspect Reasoning Graph for Patent Similarity Evaluation](https://mlsanity.com/paper/2505.19347/) (2025)
- [A Survey on Sentence Embedding Models Performance for Patent Analysis](https://mlsanity.com/paper/2206.02690/) (2022)
- [From PARIS to LE-PARIS: Toward Patent Response Automation with Recommender Systems and Collaborative Large Language Models](https://mlsanity.com/paper/2402.00421/) (2024)
- [Beyond Scaling: Predicting Patent Approval with Domain-specific Fine-grained Claim Dependency Graph](https://mlsanity.com/paper/2404.14372/) (2024)
- [Introduction of a novel word embedding approach based on technology labels extracted from patent data](https://mlsanity.com/paper/2102.00425/) (2021)
- [AI Prior Art Search: Semantic Clusters and Evaluation Infrastructure](https://mlsanity.com/paper/2512.18384/) (2025)

## Abstract (arXiv)

More than ever, technical inventions are the symbol of our society's advance. Patents guarantee their creators protection against infringement. For an invention being patentable, its novelty and inventiveness have to be assessed. Therefore, a search for published work that describes similar inventions to a given patent application needs to be performed. Currently, this so-called search for prior art is executed with semi-automatically composed keyword queries, which is not only time consuming, but also prone to errors. In particular, errors may systematically arise by the fact that different keywords for the same technical concepts may exist across disciplines. In this paper, a novel approach is proposed, where the full text of a given patent application is compared to existing patents using machine learning and natural language processing techniques to automatically detect inventions that are similar to the one described in the submitted document. Various state-of-the-art approaches for feature extraction and document comparison are evaluated. In addition to that, the quality of the current search process is assessed based on ratings of a domain expert. The evaluation results show that our automated approach, besides accelerating the search process, also improves the search results for prior art with respect to their quality.