# FlowPlan-G2P: A Structured Generation Framework for Transforming Scientific Papers into Patent Descriptions

arXiv: https://arxiv.org/abs/2601.02589 · HTML: https://mlsanity.com/paper/2601.02589/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 33 references that are in the map.
- First posted on arXiv 5 Jan 2026.

## Builds on (most cited first, 14 of 14)

- [Plan-And-Write: Towards Better Automatic Storytelling](https://mlsanity.com/paper/1811.05701/) (2018)
- [Investigating Pretrained Language Models for Graph-to-Text Generation](https://mlsanity.com/paper/2007.08426/) (2020)
- [PAIR: Planning and Iterative Refinement in Pre-trained Transformers for Long Text Generation](https://mlsanity.com/paper/2010.02301/) (2020)
- [Natural Language Processing in the Patent Domain: A Survey](https://mlsanity.com/paper/2403.04105/) (2024)
- [AutoPatent: A Multi-Agent Framework for Automatic Patent Generation](https://mlsanity.com/paper/2412.09796/) (2024)
- [PatentScore: Multi-dimensional Evaluation of LLM-Generated Patent Claims](https://mlsanity.com/paper/2505.19345/) (2025)
- [PatentGPT: A Large Language Model for Intellectual Property](https://mlsanity.com/paper/2404.18255/) (2024)
- [Pap2Pat: Benchmarking Outline-Guided Long-Text Patent Generation with Patent-Paper Pairs](https://mlsanity.com/paper/2410.07009/) (2024)
- [Linguistically Informed Masking for Representation Learning in the Patent Domain](https://mlsanity.com/paper/2106.05768/) (2021)
- [PatentLMM: Large Multimodal Model for Generating Descriptions for Patent Figures](https://mlsanity.com/paper/2501.15074/) (2025)
- [An automatic patent literature retrieval system based on LLM-RAG](https://mlsanity.com/paper/2508.14064/) (2025)
- [PatentMind: A Multi-Aspect Reasoning Graph for Patent Similarity Evaluation](https://mlsanity.com/paper/2505.19347/) (2025)
- [Self-Filtered Distillation with LLMs-generated Trust Indicators for Reliable Patent Classification](https://mlsanity.com/paper/2510.05431/) (2025)
- [Pat-DEVAL: Chain-of-Legal-Thought Evaluation for Patent Description](https://mlsanity.com/paper/2601.00166/) (2026)

## Built on by (most cited first, 1 of 1)

- [Pointer-Augmented Autoregressive Generation of Patent Claims with Joint Topology and Content Decoding](https://mlsanity.com/paper/2607.24040/) (2026)

## Abstract (arXiv)

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.