AIDB Daily Papers
特許審査における全段階の審査官通知・反論生成ベンチマーク「PatRe」
※ 日本語タイトル・ポイントはAIによる自動生成です。正確な内容は原論文をご確認ください。
ポイント
- 特許審査の全プロセスをモデル化する初のベンチマーク「PatRe」を提案しました。
- 従来の静的な分類タスクと異なり、動的な多段階の対話プロセスとして特許審査を捉える点が重要です。
- LLMを用いた実験により、モデルの性能差や審査官分析と出願人側の反論生成におけるタスク非対称性が明らかになりました。
Abstract
Patent examination is a complex, multi-stage process requiring both technical expertise and legal reasoning, increasingly challenged by rising application volumes. Prior benchmarks predominantly view patent examination as discriminative classification or static extraction, failing to capture its inherently interactive and iterative nature, similar to the peer review and rebuttal process in academic publishing. In this paper, we introduce PatRe, the first benchmark that models the full patent examination lifecycle, including Office Action generation and applicant rebuttal. PatRe comprises 480 real-world cases and supports both oracle and retrieval-simulated evaluation settings. Our benchmark reframes patent examination as a dynamic, multi-turn process of justification and response. Extensive experiments across various LLMs reveal critical insights into model performance, including differences between proprietary and open-source models, as well as task asymmetries between examiner analysis and applicant-side rebuttal. These findings highlight both the potential and current limitations of LLMs in modeling complex, real-world legal reasoning and technical novelty judgment in patent examination. We release our code and dataset to facilitate future research on patent examination modeling.
Paper AI Chat
この論文のPDF全文を対象にAIに質問できます。
質問の例: