PatRe: A Full-Stage Office Action and Rebuttal Generation Benchmark for Patent Examination
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arXiv
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| Format: | Preprint |
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2026
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| author | Wang, Qiyao Chen, Xinyi Chen, Longze Wang, Hongbo Alinejad-Rokny, Hamid Lin, Yuan Yang, Min |
| author_facet | Wang, Qiyao Chen, Xinyi Chen, Longze Wang, Hongbo Alinejad-Rokny, Hamid Lin, Yuan Yang, Min |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_03571 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | PatRe: A Full-Stage Office Action and Rebuttal Generation Benchmark for Patent Examination Wang, Qiyao Chen, Xinyi Chen, Longze Wang, Hongbo Alinejad-Rokny, Hamid Lin, Yuan Yang, Min Computation and Language Artificial Intelligence 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. |
| title | PatRe: A Full-Stage Office Action and Rebuttal Generation Benchmark for Patent Examination |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2605.03571 |