Benchmarking Patent Drafting from Inventor-Style Disclosures

2026-08-21

AI Summary

本文针对从非正式发明材料直接生成完整且合法的专利申请的问题，提出Dis2Pat数据集和Patent-MAF框架作为解决方案。

Abstract

While recent large language models (LLMs) have achieved promising results on individual patent drafting tasks, they fundamentally fail to investigate the core challenge of real-world patent drafting: generating a complete and legally coherent patent application directly from early-stage invention materials. Prior work predominantly assumes later-stage, highly structured, or already legalistic inputs. However, real patenting workflows begin with informal, de-legalized disclosures authored by inventors. To bridge the gap, we introduce Dis2Pat, a disclosure-to-patent dataset that reflects realistic patenting workflows by requiring the generation of complete patent applications directly from inventor-style, de-legalized disclosures. Given the inherent difficulty of long-form, legally constrained patent drafting and the strong privacy requirements, we further propose a strong baseline named Patent-MAF. It is a multi-agent framework for locally deployable patent drafting. Benchmark results reveal that current LLMs exhibit limitations in patent drafting, while Patent-MAF provides a strong baseline that consistently outperforms evaluated open-source models and remains competitive with large closed-source models.

Problem

• large language models
• patent drafting
• inventor-style disclosures
• legal coherence
• realistic patenting workflows

Innovation

• Dis2Pat
• inventor-style disclosures
• Patent-MAF
• multi-agent framework