mlsanityData to 14 Oct 2026

PatRe: A Full-Stage Office Action and Rebuttal Generation Benchmark for Patent Examination

arXiv 2605.03571 ↗First posted 5 May 2026cs.CL cs.AIcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 19 of its 25 references that are in the map. First posted on arXiv 5 May 2026.

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19 of 25 references.

References · 19

  1. 2023Efficient Memory Management for Large Language Model Serving with PagedAttentioncited by 5,944
  2. 2024GPT-4o System Cardcited by 4,588
  3. 2025DeepSeek-V3.2: Pushing the Frontier of Open Large Language Modelscited by 815
  4. 2024AgentReview: Exploring Peer Review Dynamics with LLM Agentscited by 93
  5. 2022The Harvard USPTO Patent Dataset: A Large-Scale, Well-Structured, and Multi-Purpose Corpus of Patent Applicationscited by 33
  6. 2025Re2: A Consistency-ensured Dataset for Full-stage Peer Review and Multi-turn Rebuttal Discussionscited by 23
  7. 2024AutoPatent: A Multi-Agent Framework for Automatic Patent Generationcited by 16
  8. 2026Paper2Rebuttal: A Multi-Agent Framework for Transparent Author Response Assistancecited by 9
  9. 2024Pap2Pat: Benchmarking Outline-Guided Long-Text Patent Generation with Patent-Paper Pairscited by 8
  10. 2024MoZIP: A Multilingual Benchmark to Evaluate Large Language Models in Intellectual Propertycited by 7
  11. 2026RebuttalAgent: Strategic Persuasion in Academic Rebuttal via Theory of Mindcited by 6
  12. 2025PANORAMA: A Dataset and Benchmarks Capturing Decision Trails and Rationales in Patent Examinationcited by 5
7 more
  1. 2025Automatic Paper Reviewing with Heterogeneous Graph Reasoning over LLM-Simulated Reviewer-Author Debatescited by 5
  2. 2024Patent-CR: A Dataset for Patent Claim Revisioncited by 4
  3. 2026PILOT-Bench: A Benchmark for Legal Reasoning in the Patent Domain with IRAC-Aligned Classification Taskscited by 4
  4. 2024IPEval: A Bilingual Intellectual Property Agency Consultation Evaluation Benchmark for Large Language Modelscited by 3
  5. 2024PatentEdits: Framing Patent Novelty as Textual Entailmentcited by 3
  6. 2025IPBench: Benchmarking the Knowledge of Large Language Models in Intellectual Propertycited by 3
  7. 2025PEDANTIC: A Dataset for the Automatic Examination of Definiteness in Patent Claimscited by 3

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