arXiv 12 Apr 2021 · cs.CL
arXiv:2105.00817 · PDF · DOI · OpenAlex · Extracted main text
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.
appendix boundary found by appendix_command · 30% of the source is main text. Read the extracted text to check this.
The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Freunek \ Bodmer (2021) `BERT based patent novelty search by training claims to their own description', arXiv preprint:2103.01126 | 1.000 | 7 | 3 | 100% |
| 2 | Aristodemou \ Tietze (2018) `The state-of-the-art on intellectual property analytics (ipa): A literature review on artificial intelligence, machine learning… | 0.405 | 1 | 1 | 100% |
| 3 | Chikkamath, Endres, Bayyapu \ Hewel (2020) `An empirical study on patent novelty detection: A novel approach using machine learning and natural language processing', 2020… | 0.405 | 1 | 1 | 100% |
| 4 | Choi, Lee, Park \ Choi (2019) `Deep patent landscaping model using transformer and graph embedding', arXiv preprint:1903.05823 | 0.405 | 1 | 1 | 100% |
| 5 | Devlin, Chang, Lee \ Toutanova (2018) `Bert: Pre-training of deep bidirectional transformers for language understanding', arXiv preprint:1810.04805 | 0.405 | 1 | 1 | 100% |
| 6 | Krestel, Chikkamath, Hewel \ Risch (2021) `A survey on deep learning for patent analysis', World Patent Information 65, 102035 | 0.405 | 1 | 1 | 100% |
| 7 | Lee \ Hsiang (2020) `Patent classification by fine-tuning BERT language model', World Patent Information 61, 4 | 0.405 | 1 | 1 | 100% |
| 8 | Lee \ Hsiang (2020) `Prior art search and reranking for generated patent text', arXiv preprint:2009.09132 | 0.405 | 1 | 1 | 100% |
| 9 | Risch, Alder, Hewel \ Krestel (2020) `PatentMatch: data for matching patent claims & prior art', arXiv preprint:2012.13919 | 0.405 | 1 | 1 | 100% |
| 10 | Setchi, Spasić, Morgan, Harrison \ Corken (2021) `Artificial intelligence for patent prior art searching', World Patent Information 64, 102021 | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 12 scored citations.