# BERT based freedom to operate patent analysis

arXiv: https://arxiv.org/abs/2105.00817 · HTML: https://mlsanity.com/paper/2105.00817/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 16 references that are in the map.
- First posted on arXiv 12 Apr 2021.

## Builds on (most cited first, 6 of 6)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [How to Fine-Tune BERT for Text Classification?](https://mlsanity.com/paper/1905.05583/) (2019)
- [Prior Art Search and Reranking for Generated Patent Text](https://mlsanity.com/paper/2009.09132/) (2020)
- [Deep Patent Landscaping Model Using Transformer and Graph Embedding](https://mlsanity.com/paper/1903.05823/) (2019)
- [BERT based patent novelty search by training claims to their own description](https://mlsanity.com/paper/2103.01126/) (2021)

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

- [Natural Language Processing in the Patent Domain: A Survey](https://mlsanity.com/paper/2403.04105/) (2024)

## Abstract (arXiv)

In this paper we present a method to apply BERT to freedom to operate patent analysis and patent searches. According to the method, BERT is fine-tuned by training patent descriptions to the independent claims. Each description represents an invention which is protected by the corresponding claims. Such a trained BERT could be able to identify or order freedom to operate relevant patents based on a short description of an invention or product. We tested the method by training BERT on the patent class G06T1/00 and applied the trained BERT on five inventions classified in G06T1/60, described via DOCDB abstracts. The DOCDB abstract are available on ESPACENET of the European Patent Office.