FlowPlan-G2P: A Structured Generation Framework for Transforming Scientific Papers into Patent Descriptions
arXiv:2601.02589v4 Announce Type: replace 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) Sectio
The proliferation of complex scientific research and advancements in AI's natural language processing capabilities are converging, making automated knowledge transformation increasingly feasible and necessary.
This development allows for more efficient and accurate translation of scientific discoveries into intellectual property, accelerating innovation cycles and potentially changing the landscape of patent generation.
The laborious and specialized process of transforming scientific papers into patent descriptions could become significantly automated, reducing costs and accelerating IP protection for R&D.
- · AI software developers
- · R&D intensive industries
- · Patent attorneys (augmented)
- · Small research institutions
- · Traditional patent drafting services
- · Inexperienced patent agents
- · Human-only abstracting services
Scientific papers can be more rapidly converted into patent applications, increasing the volume of new IP filings.
This acceleration could lead to more robust IP portfolios for companies and faster commercialization of research breakthroughs.
The competitive advantage in innovation might shift towards entities with superior AI tools for IP generation, potentially centralizing power in those with advanced AI resources.
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Read at arXiv cs.CL