Abstract
Over 3.5 million patents are filed annually, with drafting patent descriptions requiring deep technical and legal expertise. Transforming scientific papers into patent descriptions is particularly challenging due to their differing rhetorical styles and stringent legal requirements. Unlike black-box text-to-text approaches that struggle to model structural reasoning and legal constraints, we propose FlowPlan-G2P, a novel framework that mirrors the cognitive workflow of expert drafters by reformulating this task into three stages: (1) Concept Graph Induction, extracting technical entities and relationships into a directed graph via expert-like reasoning; (2) Paragraph and Section Planning, reorganizing the graph into coherent clusters aligned with canonical patent sections; and (3) Graph-Conditioned Generation, producing legally compliant paragraphs using section-specific subgraphs and tailored prompts. Experiments demonstrate that FlowPlan-G2P significantly improves logical coherence and legal compliance over end-to-end LLM baselines. Our framework establishes a new paradigm for paper-to-patent generation and advances structured text generation for specialized domains.
Abstract (translated)
每年有超过350万项专利申请，撰写专利描述需要深厚的技术和法律专业知识。将科技论文转化为专利说明书尤其具有挑战性，这主要是因为它们在修辞风格上存在差异，并且要满足严格的法律规定。不同于那些难以建模结构化推理及法律约束的黑箱文本到文本的方法，我们提出了FlowPlan-G2P这一全新框架，它通过模仿专家撰稿人的认知工作流程，将任务分解为三个阶段来处理： 1. **概念图诱导**：利用类似专家的推理方式，从科技论文中提取技术实体和关系，并将其组织成一个有向图。 2. **段落与章节规划**：重新排列这些节点和边以形成一系列相互关联且连贯的小簇，并将它们对齐到标准专利章节之下。 3. **条件图生成**：使用特定于每个章节的子图以及定制提示，生成符合法律规定的段落。 实验表明，FlowPlan-G2P在逻辑一致性和法律合规性方面相比端到端LLM基准有了显著提高。我们所提出的框架为论文转专利文本生成设立了新的范式，并推动了专门领域内的结构化文本生成技术的发展。
URL
https://arxiv.org/abs/2601.02589
