Summary of From Paris to Le-paris: Toward Patent Response Automation with Recommender Systems and Collaborative Large Language Models, by Jung-mei Chu et al.
From PARIS to LE-PARIS: Toward Patent Response Automation with Recommender Systems and Collaborative Large Language Models
by Jung-Mei Chu, Hao-Cheng Lo, Jieh Hsiang, Chun-Chieh Cho
First submitted to arxiv on: 1 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Human-Computer Interaction (cs.HC); Information Retrieval (cs.IR); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces the Patent Office Action Response Intelligence System (PARIS) and its advanced version, LE-PARIS, designed to enhance patent attorneys’ efficiency in responding to Office Actions. The systems leverage AI collaboration through features like an OA Topics Database, Response Templates, Recommender Systems, and LLM-based Response Generation. By employing a multi-paradigm analysis using the USPTO Office Action database and longitudinal data, the study validates the effectiveness of PARIS and LE-PARIS in enhancing attorney performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this research, scientists created special systems called PARIS and LE-PARIS to help patent lawyers respond quickly and well to important documents from the government. These systems use artificial intelligence to make suggestions and even write some responses for the lawyers. The study tested these systems with real data and showed that they can really help lawyers do their jobs better. |
