arXiv 1 Mar 2021 · Statistics — Machine Learning · 3 citations (OpenAlex)
arXiv:2103.01126 · PDF · DOI · OpenAlex · Extracted main text
In this paper we present a method to concatenate patent claims to their own description. By applying this method, BERT trains suitable descriptions for claims. Such a trained BERT (claim-to-description- BERT) could be able to identify novelty relevant descriptions for patents. In addition, we introduce a new scoring scheme, relevance scoring or novelty scoring, to process the output of BERT in a meaningful way. We tested the method on patent applications by training BERT on the first claims of patents and corresponding descriptions. BERT's output has been processed according to the relevance score and the results compared with the cited X documents in the search reports. The test showed that BERT has scored some of the cited X documents as highly relevant.
appendix boundary found by none_found · 100% 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 | Risch, Alder, Hewel \ Krestel (2020) `PatentMatch: data for matching patent claims & prior art', arXiv preprint arXiv:2012.13919 | 0.737 | 3 | 2 | 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 | Choi, Lee, Park \ Choi (2019) `Deep patent landscaping model using transformer and graph embedding', arXiv preprint arXiv:1903.05823 | 0.405 | 1 | 1 | 100% |
| 4 | Demey \ Golzio (2020) `Search strategies at the european patent office', World Patent Information 63, 101989 | 0.405 | 1 | 1 | 100% |
| 5 | Devlin, Chang, Lee \ Toutanova (2018) `Bert: Pre-training of deep bidirectional transformers for language understanding', arXiv preprint arXiv:1810.04805 | 0.405 | 1 | 1 | 100% |
| 6 | Lee \ Hsiang (2020) `Patent classification by fine-tuning BERT language model', World Patent Information 61, 4 | 0.405 | 1 | 1 | 100% |
| 7 | Lee \ Hsiang (2020) `Prior art search and reranking for generated patent text', arXiv preprint arXiv:2009.09132 | 0.405 | 1 | 1 | 100% |
| 8 | Setchi, Spasić, Morgan, Harrison \ Corken (2021) `Artificial intelligence for patent prior art searching', World Patent Information 64, 102021 | 0.405 | 1 | 1 | 100% |
| 9 | Srebrovic \ Yonamine (2020) `Leveraging the bert algorithm forpatents with tensorflow and bigquery' | 0.405 | 1 | 1 | 100% |
| 10 | Sun, Qiu, Xu \ Huang (2019) How to fine-tune bert for text classification?, in `China National Conference on Chinese Computational Linguistics', Springer, p… | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 11 scored citations.