用图约束的分层谢帕利值，精准评估专利在产品中的经济贡献。
A Framework for Graph-Conditioned Hierarchical Shapley Attribution in Patent Valuation
- 基于知识图谱的马尔可夫毯限制，加速专利联盟计算。
- 百项专利计算仅需10毫秒，误差低于0.062，逼近真实值。
- 适合知识产权估值、技术投资分析等场景，可扩展至真实数据集。
在包含数万项专利的产品中估算单个专利的经济贡献，是知识产权经济学长期未解难题。我们提出 PatentXAI 框架，将专利估值视为可解释AI问题：给定特征函数 v(S)，表示专利子集 S 可实现的收益，谢帕利值以满足效率、对称性、虚设性和可加性的方式衡量专利的公平利润份额。为使计算可行，我们依据 C-SVE 条件独立定理，将每项专利的联盟范围限制在其知识图谱中的马尔可夫毯内。在覆盖图呈帕累托分布的缩放实验中，当专利数 n=100 时，马尔可夫毯中位大小占总专利数的 32.9%，90% 分位数为 55.2%，每项专利计算耗时仅 10 毫秒。n=12 时与精确基准的差异为 0.088；n=100 时与高样本蒙特卡洛参考值的差异为 0.062 ± 0.003。密集组件实验表明，当 80% 专利共享同一组件时，毯子能正确扩展覆盖该聚类，误差降至 0.039，因同质组合的聚合计算更准确。利润分配分层进行：精确谢帕利先分配宏观组件间利润，再以中心性加权的谢帕利分配各组件内专利预算。从真实数据估计 v(S) 是主要开放问题，本文区分其与计算贡献，并提出使用 ETSI、USPTO 及 Lens.org 数据集进行实证验证的具体路线。
原文摘要 · Abstract (English)
Estimating the economic contribution of a single patent inside a product that embodies tens of thousands of patents is a long-standing unsolved problem in intellectual property economics. We propose PatentXAI, a framework that treats patent valuation as a problem of explainable AI: given a characteristic function v(S) encoding the revenue achievable by patent subset S, a patent's Shapley value measures its fair share of product profit in a way that satisfies efficiency, symmetry, dummy, and additivity. To make computation tractable we restrict each patent's coalition to its Markov Blanket inside a knowledge graph, grounded in the C-SVE conditional independence theorem (Li et al., 2020). Scaling experiments from n=12 to n=100 patents using Pareto-distributed coverage graphs report median Markov Blanket size of 32.9 percent of n at n=100, with 90th-percentile blanket size of 55.2 percent of n, and runtime of 10 milliseconds per patent. Difference against exact ground truth at n=12 is 0.088; difference against a high-sample Monte Carlo reference at n=100 is 0.062 plus or minus 0.003. A dense-component experiment shows that when 80 percent of patents share one component, the blanket correctly expands to cover that dense cluster, and the difference versus reference falls to 0.039 because the pooled computation becomes more accurate on homogeneous portfolios. Profit allocation proceeds hierarchically: exact Shapley distributes total profit among macro-components, then centrality-weighted Shapley distributes each component budget among covering patents. Estimating v(S) from real data is the primary open problem; we distinguish this from the computational contribution and outline a concrete roadmap for empirical validation using public ETSI, USPTO, and Lens.org datasets.
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