ClaimBrush: A Novel Framework for Automated Patent Claim Refinement Based on Large Language Models
<|reference_start|>ClaimBrush: A Novel Framework for Automated Patent Claim Refinement Based on Large Language Models: Automatic refinement of patent claims in patent applications is crucial from the perspective of intellectual property strategy. In this paper, we propose ClaimBrush, a novel framework for automated patent claim refinement that includes a dataset and a rewriting model. We constructed a dataset for training and evaluating patent claim rewriting models by collecting a large number of actual patent claim rewriting cases from the patent examination process. Using the constructed dataset, we built an automatic patent claim rewriting model by fine-tuning a large language model. Furthermore, we enhanced the performance of the automatic patent claim rewriting model by applying preference optimization based on a prediction model of patent examiners' Office Actions. The experimental results showed that our proposed rewriting model outperformed heuristic baselines and zero-shot learning in state-of-the-art large language models. Moreover, preference optimization based on patent examiners' preferences boosted the performance of patent claim refinement.<|reference_end|>
@article{kawano2024claimbrush:,
    title={ClaimBrush: A Novel Framework for Automated Patent Claim Refinement
  Based on Large Language Models},
    author={Seiya Kawano, Hirofumi Nonaka, Koichiro Yoshino},
    journal={arXiv preprint arXiv:2410.05575},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.05575},
    primaryClass={cs.CL cs.AI}
}