In patent prosecution, timely and effective responses to Office Actions (OAs) are crucial for securing patents. However, past automation and artificial intelligence research have largely overlooked this aspect. To bridge this gap, our study introduces the Patent Office Action Response Intelligence System (PARIS) and its advanced version, the Large Language Model (LLM) Enhanced PARIS (LE-PARIS). These systems are designed to enhance the efficiency of patent attorneys in handling OA responses through collaboration with AI. 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. To validate the effectiveness of the systems, we have employed a multi-paradigm analysis using the USPTO Office Action database and longitudinal data based on attorney interactions with our systems over six years. Through five studies, we have examined the constructiveness of OA topics (studies 1 and 2) using topic modeling and our proposed Delphi process, the efficacy of our proposed hybrid LLM-based recommender system tailored for OA responses (study 3), the quality of generated responses (study 4), and the systems' practical value in real-world scenarios through user studies (study 5). The results indicate that both PARIS and LE-PARIS significantly achieve key metrics and have a positive impact on attorney performance.

翻译：在专利审查过程中，及时有效地回应审查意见（OA）是确保专利授权的关键。然而，以往的自动化与人工智能研究在很大程度上忽视了这一环节。为弥补这一空白，本研究提出了专利审查意见答复智能系统（PARIS）及其进阶版本——大语言模型增强型PARIS（LE-PARIS）。这些系统旨在通过人类与人工智能的协作，提升专利律师处理OA答复的效率。系统的核心功能包括构建OA主题数据库、开发答复模板，以及实施推荐系统与基于大语言模型的答复生成。为验证系统有效性，我们采用多范式分析，利用USPTO审查意见数据库及基于律师与系统交互六年的纵向数据开展研究。通过五项实验，我们借助主题建模及提出的德尔菲流程检验了OA主题的建设性（研究1与2），验证了为OA答复定制的混合型大语言模型推荐系统的效能（研究3），评估了生成答复的质量（研究4），并通过用户研究考察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均能显著达成关键指标，并对律师绩效产生积极影响。