# A Structured Generation Framework for Transforming Scientific Papers into Patent

> Source: <https://arxiv.org/abs/2601.02589>
> Published: 2026-06-26 07:05:55+00:00

# Computer Science > Computation and Language

[Submitted on 5 Jan 2026 (

[v1](https://arxiv.org/abs/2601.02589v1)), last revised 23 May 2026 (this version, v4)]# Title:FlowPlan-G2P: A Structured Generation Framework for Transforming Scientific Papers into Patent Descriptions

[View PDF](/pdf/2601.02589)

[HTML (experimental)](https://arxiv.org/html/2601.02589v4)

Abstract: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.

## Submission history

From: Yoo Yongmin [[view email](/show-email/f39b310c/2601.02589)]

**Mon, 5 Jan 2026 22:40:15 UTC (10,029 KB)**

[[v1]](/abs/2601.02589v1)**Tue, 14 Apr 2026 08:59:16 UTC (1,358 KB)**

[[v2]](/abs/2601.02589v2)**Wed, 13 May 2026 12:15:30 UTC (1,358 KB)**

[[v3]](/abs/2601.02589v3)**[v4]** Sat, 23 May 2026 02:45:09 UTC (7,092 KB)

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