A Structured Generation Framework for Transforming Scientific Papers into Patent
Generating patent descriptions from scientific papers is challenging due to fundamental rhetorical and structural disparities between the two genres. Existing approaches treat this as surface-level rewriting, failing to capture the hierarchical reasoning and statutory constraints inherent in patent drafting. We propose FlowPlan-G2P, a graph-mediated generation framework that decomposes this transformation into three stages: (1) Concept Graph Induction, extracting technical entities and functional dependencies into a directed graph; (2) Section-level Planning, partitioning the graph into coherent subgraphs aligned with canonical patent sections; and (3) Graph-Conditioned Generation, synthesizing legally compliant paragraphs conditioned on section-specific subgraphs. Experiments on expert-validated benchmarks reveal that standard NLG metrics systematically favor legally non-compliant outputs over valid patent descriptions, motivating our domain-specific evaluation. Under this evaluation, FlowPlan-G2P with an open-weight backbone consistently outperforms vanilla proprietary models, demonstrating that structured decomposition is a stronger determinant of quality than model scale.
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Computer Science > Computation and Language arXiv:2601.02589 (cs) [Submitted on 5 Jan 2026 (v1), last revised 23 May 2026 (this version, v4)] Title:FlowPlan-G2P: A Structured Generation Framework for Transforming Scientific Papers into Patent Descriptions Authors:Kris W Pan, Yongmin Yoo View a PDF of the paper titled FlowPlan-G2P: A Structured Generation Framework for Transforming Scientific Papers into Patent Descriptions, by Kris W Pan and 1 other authors View PDF HTML (experimental) Abstract:Generating patent descriptions from scientific papers is challenging due to fundamental rhetorical and structural disparities between the two genres.
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