A Structured Generation Framework for Transforming Scientific Papers into Patent
The paper presents FlowPlan-G2P, a structured generation framework that converts scientific papers into patent descriptions. It decomposes the transformation into concept graph induction, section-level planning, and graph-conditioned generation. Experimental results show the approach outperforms standard models when evaluated on legal compliance criteria.
- ▪FlowPlan-G2P introduces a graph-mediated pipeline to translate scientific papers into patent language.
- ▪The pipeline consists of three stages: extracting a concept graph, partitioning it into section-aligned subgraphs, and generating text conditioned on these subgraphs.
- ▪Standard natural language generation metrics were found to favor legally non‑compliant outputs, prompting a domain‑specific evaluation.
- ▪Under this evaluation, FlowPlan‑G2P consistently surpasses vanilla proprietary models, highlighting the importance of structured decomposition over model size.
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Record
| Original publisher | arXiv.org |
| Canonical URL | https://arxiv.org/abs/2601.02589 |
| Publication time | Fri, 26 Jun 2026 07:05:55 +0000 |
| Retrieval time | 2026-06-26T07:07:29.748Z |
| Last seen | 2026-06-26T07:07:29.748Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | Qy0W5FA2KLDs |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
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| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
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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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv.org.
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