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Found 8 papers, 1 papers with code
From PARIS to LE-PARIS: Toward Patent Response Automation with Recommender Systems and Collaborative Large Language Models
These systems are designed to enhance the efficiency of patent attorneys in handling OA responses through collaboration with AI.
Prior Art Search and Reranking for Generated Patent Text
The steps of reranking are: (1) search the most similar text in the training data of GPT-2 by taking a bag-of-word ranking approach (BM25), (2) convert the search results in text format to BERT embeddings, and (3) provide the final result by ranking the BERT embeddings based on their similarities with the patent text generated by GPT-2.
Measuring Patent Claim Generation by Span Relevancy
Specifically, we fine-tune a pre-trained Google BERT model to measure the patent claim spans generated by a fine-tuned OpenAI GPT-2 model.
Patent Claim Generation by Fine-Tuning OpenAI GPT-2
Our contributions include: (1) being the first to generate patent claims by machines and being the first to apply GPT-2 to patent claim generation, (2) providing various experiment results for qualitative analysis and future research, (3) proposing a new sampling approach for text generation, and (4) building an e-mail bot for future researchers to explore the fine-tuned GPT-2 model further.
PatentBERT: Patent Classification with Fine-Tuning a pre-trained BERT Model
In this work we focus on fine-tuning a pre-trained BERT model and applying it to patent classification.
Ranked #1 on
Multi-Label Text Classification
on USPTO-3M
