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PatentBERT: Patent Classification with Fine-Tuning a pre-trained BERT Model
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In this work we focus on fine-tuning a pre-trained BERT model and applying it to patent classification. When applied to large datasets of over two millions patents, our approach outperforms the state of the art by an approach using CNN with word embeddings. In addition, we focus on patent claims without other parts in patent documents. Our contributions include: (1) a new state-of-the-art method based on pre-trained BERT model and fine-tuning for patent classification, (2) a large dataset USPTO-3M at the CPC subclass level with SQL statements that can be used by future researchers, (3) showing that patent claims alone are sufficient for classification task, in contrast to conventional wisdom.
Forward citations
Cited by 2 Pith papers
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From scratch to silver: Creating trustworthy training data for patent-SDG classification using Large Language Models
A weak-supervision pipeline using LLM-extracted concepts and rank fusion creates silver-standard patent-to-SDG labels that recover known citation-derived associations and show high network modularity.
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Can AI Examine Novelty of Patents?: Novelty Evaluation Based on the Correspondence between Patent Claim and Prior Art
A new patent novelty benchmark from real examiner rejections shows large language models can classify novelty at about 62% accuracy, while smaller classification models perform at chance.
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