IP Intelligence: Research on AI for IP Service and IP of AI

IPBench: Benchmarking the Knowledge of Large Language Models in Intellectual Property

Qiyao Wang, Guhong Chen, Hongbo Wang, Huaren Liu, Minghui Zhu, Zhifei Qin, Linwei Li, Yilin Yue, Shiqiang Wang, Jiayan Li, Yihang Wu, Ziqiang Liu, Longze Chen, Run Luo, Liyang Fan, Jiaming Li, Lei Zhang, Kan Xu, Hongfei Lin, Hamid Alinejad-Rokny, Shiwen Ni^†, Yuan Lin^†, Min Yang^†

Website | Paper | Github

AutoPatent: A Multi-Agent Framework for Automatic Patent Generation

Qiyao Wang^*, Shiwen Ni^*, Huaren Liu, Shule Lu, Guhong Chen, Xi Feng, Chi Wei, Qiang Qu, Hamid Alinejad-Rokny, Yuan Lin^†, Min Yang^†

TL;DR: We introduce Draft2Patent, a novel task for generating full-length patents (~17K tokens) from drafts, along with the D2P benchmark. Our AutoPatent framework, leveraging a multi-agent system, excels in patent generation, with Qwen2.5-7B outperforming larger models like GPT-4o and Qwen2.5-72B in metrics and human evaluations.

Website | Paper | Github

IPEval: A Bilingual Intellectual Property Agency Consultation Evaluation Benchmark for Large Language Models

Qiyao Wang, Jianguo Huang, Shule Lu, Yuan Lin^†, Kan Xu, Liang Yang, Hongfei Lin

TL;DR: IPEval introduces a benchmark for assessing Large Language Models' (LLMs) performance in intellectual property (IP) law with 2,657 questions. It evaluates LLMs across key IP areas using zero-shot, 5-few-shot, and Chain of Thought (CoT) approaches. Findings highlight the need for specialized IP LLMs due to language proficiency bias. The benchmark is crucial for developing LLMs with deeper IP knowledge.

Website | Paper | Github | HuggingFace