In traditional innovation practices, concept and IP generation are often iteratively integrated. Both processes demand an intricate understanding of advanced technical domain knowledge. Existing large language models (LLMs), while possessing massive pre-trained knowledge, often fall short in the innovative concept generation due to a lack of specialized knowledge necessary for the generation. To bridge this critical gap, we propose a novel knowledge finetuning (KFT) framework to endow LLM-based AI with the ability to autonomously mine, understand, and apply domain-specific knowledge and concepts for invention generation, i.e., concept and patent generation together. Our proposed PatentGPT integrates knowledge injection pre-training (KPT), domain-specific supervised finetuning (SFT), and reinforcement learning from human feedback (RLHF). Extensive evaluation shows that PatentGPT significantly outperforms the state-of-the-art models on patent-related benchmark tests. Our method not only provides new insights into data-driven innovation but also paves a new path to fine-tune LLMs for applications in the context of technology. We also discuss the managerial and policy implications of AI-generating inventions in the future.

翻译：在传统创新实践中，概念与知识产权生成常以迭代方式相互融合。这两个过程均要求对前沿技术领域知识具有深刻理解。现有的大型语言模型（LLMs）虽具备海量预训练知识，但由于缺乏生成所需的专业知识，在创新概念生成方面往往表现不足。为弥合这一关键差距，我们提出一种新颖的知识微调（KFT）框架，使基于LLM的人工智能具备自主挖掘、理解并应用领域特定知识与概念以进行发明生成（即概念与专利协同生成）的能力。我们提出的PatentGPT整合了知识注入预训练（KPT）、领域特定监督微调（SFT）以及基于人类反馈的强化学习（RLHF）。大量评估表明，PatentGPT在专利相关基准测试中显著优于现有最先进模型。我们的方法不仅为数据驱动创新提供了新视角，也为在技术应用背景下微调LLMs开辟了新路径。同时，我们还探讨了未来人工智能生成发明所蕴含的管理与政策启示。