In patent prosecution, timely and effective responses to Office Actions (OAs) are crucial for acquiring patents, yet past automation and AI research have scarcely addressed this aspect. To address this gap, our study introduces the Patent Office Action Response Intelligence System (PARIS) and its advanced version, the Large Language Model Enhanced PARIS (LE-PARIS). These systems are designed to expedite the efficiency of patent attorneys in collaboratively handling OA responses. The systems' key features include the construction of an OA Topics Database, development of Response Templates, and implementation of Recommender Systems and LLM-based Response Generation. Our validation involves a multi-paradigmatic analysis using the USPTO Office Action database and longitudinal data of attorney interactions with our systems over six years. Through five studies, we examine the constructiveness of OA topics (studies 1 and 2) using topic modeling and the proposed Delphi process, the efficacy of our proposed hybrid recommender system tailored for OA (both LLM-based and non-LLM-based) (study 3), the quality of response generation (study 4), and the practical value of the systems in real-world scenarios via user studies (study 5). Results demonstrate that both PARIS and LE-PARIS significantly meet key metrics and positively impact attorney performance.

翻译：在专利审查过程中，及时有效地回应审查意见通知书（Office Action, OA）对专利授权至关重要，然而以往的自动化与人工智能研究极少涉及这一环节。为填补此空白，本研究提出了专利审查意见答复智能系统（PARIS）及其增强版本——大语言模型增强的PARIS（LE-PARIS）。两系统旨在提升专利代理人协同处理OA答复的效率。其核心功能包括构建OA主题数据库、开发答复模板，以及部署推荐系统和基于LLM的答复生成模块。验证工作采用多范式分析，基于美国专利商标局（USPTO）OA数据库及代理人六年间与系统交互的纵向数据。通过五项研究，我们分别运用主题建模与所提德尔菲过程考察了OA主题的建设性（研究1与2），检验了针对OA设计的混合推荐系统（含基于LLM与非基于LLM两种）的有效性（研究3），评估了答复生成质量（研究4），并通过用户研究验证了系统在真实场景中的实用价值（研究5）。结果表明，PARIS与LE-PARIS均显著满足关键指标，并对代理人绩效产生积极影响。