发表机构
AGC Inc.（AGC株式会社）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出BLANC三阶段流程，通过多视图神经主题建模、NPMI及ΔNPMI检测，在USPTO等语料库中成功恢复专利空白区域，其特异性优于单视图聚类与现有共现度量。
AI 中文摘要
识别专利空白区域——即专利布局中未被探索但具有潜在价值的领域——对于战略研发规划至关重要，但现有方法依赖手动专利映射或应用单视图聚类，缺乏定量的空白检测。我们提出BLANC（Blank Landscape Analysis through NPMI Conditioning，即通过NPMI条件化的空白布局分析），这是一个三阶段流程，结合了：（1）沿三个语义维度（应用/用途、新颖性、创造性）的多视图神经主题建模；（2）归一化点间互信息（NPMI）以量化跨维度簇关联；（3）条件检测，即当语料库被用户指定关键词过滤时，标记NPMI下降的组合。这种下降由新指标ΔNPMI捕获，用于识别“全局已确立、局部未被探索”的组合。由于专利空白区域没有真实值，我们在两个公开的美国专利商标局（USPTO）语料库上评估BLANC：机器学习/人工智能（5417项专利， CPC分类G06N）和玻璃组合物（1982项专利， CPC分类C03C），通过人工去除已知技术组合并测试其恢复能力。当移除目标对四分之三的文档时，BLANC恢复了34.1%（机器学习/人工智能）和27.3%（玻璃）的已移除组合，而针对非目标的大小匹配移除（随机文档或不同已确立组合的文档）则基本无法恢复：在191次诱饵试验中均未恢复目标。将三个语义视图合并为一个则无法恢复任何组合，而现有共现度量在随机移除下也会标记目标，不具备特异性。在一个专有案例（302项浮法玻璃/微晶玻璃专利）中，关键词“氟”揭示了氟表面处理×翘曲抑制的候选组合（ΔNPMI高达0.48），该组合已被专家独立识别。
英文摘要
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 (Blank Landscape Analysis through NPMI Conditioning), a three-phase pipeline combining (1) multi-view neural topic modeling along three semantic dimensions (application/use, novelty, inventive step); (2) Normalized Pointwise Mutual Information (NPMI) to quantify cross-dimensional cluster association; and (3) conditional detection that flags combinations whose NPMI drops when the corpus is filtered by a user-specified keyword. The drop is captured by a new metric, $Δ$NPMI, which identifies combinations "established globally, unexplored locally." Because white space has no ground truth, we evaluate BLANC on two public USPTO corpora --- machine learning/AI (5,417 patents, CPC G06N) and glass compositions (1,982 patents, CPC C03C) --- by artificially depleting known technology combinations and testing recovery. When three-quarters of a target pair's documents are removed, BLANC recovers 34.1% (ML/AI) and 27.3% (glass) of the depleted combinations, whereas size-matched removals not aimed at them (random documents, or those of a different established combination) essentially never do: the target is never recovered in 191 decoy trials. Collapsing the three semantic views into one recovers nothing, while prior co-occurrence measures also flag the target under random removal, offering no specificity. In a proprietary case (302 float glass / glass-ceramics patents), the keyword "fluorine" reveals a fluorine surface treatment $\times$ warpage suppression candidate ($Δ$NPMI up to 0.48) that experts had independently identified.
Comments15 pages, 4 figures, 10 tables. A preliminary Japanese-language report covering the methodology and the industrial case study is scheduled to appear as AGC Research Report 76 (2026), ISSN 2434-0774. The present article is the full version, containing the entire quantitative evaluation
