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New BLANC method maps patent white space using AI and NPMI

By PulseAugur Editorial · [1 sources] ·

Researchers have developed BLANC, a novel three-phase pipeline designed to identify unexplored areas within patent landscapes. This method utilizes multi-view neural topic modeling across application, novelty, and inventive step dimensions, coupled with Normalized Pointwise Mutual Information (NPMI) to quantify associations between clusters. BLANC identifies potential white space by detecting drops in NPMI when the corpus is filtered by a specific keyword, indicating areas that are established globally but unexplored locally. The system was evaluated on machine learning/AI and glass composition patent corpora, demonstrating its ability to recover artificially depleted technology combinations and identify novel associations, such as a fluorine surface treatment and warpage suppression candidate in glass manufacturing. AI

IMPACT This method could enhance strategic R&D planning by uncovering novel technological opportunities.

RANK_REASON The cluster contains an academic paper detailing a new method for patent analysis. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.IR (Information Retrieval) →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New BLANC method maps patent white space using AI and NPMI

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The cluster contains an academic paper detailing a new method for patent analysis. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Kensuke Fujii ·

    BLANC: Discovering Patent White Space via Changes in Normalized Pointwise Mutual Information Between Multi-View Clusters

    Identifying white space --- the unexplored but potentially valuable regions of a patent landscape --- is essential for strategic R&D planning, yet existing methods rely on manual patent mapping or apply single-view clustering without quantitative gap detection. We propose BLANC (…

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