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Patents are essential for securing market leadership and protecting intellectual property, but traditional valuation methods often fail to fully capture their strategic value. This paper introduces a causality-driven framework for patent valuation that integrates domain expertise with advanced language models in a structured interview-like process. By analyzing semiconductor patents filed between 1997 and 2007-a pivotal period of technological transformation-our approach employs Directed Acyclic Graphs (DAGs) and Structural Equation Models (SEMs) to reveal causal relationships, providing precise value attribution. This integration enhances the analysis of complex patent data, offering a powerful tool for aligning technological innovation with market and legal strategies.

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Causality-Driven Patent Valuation: Integrating Domain Knowledge and Language Models in a Structured Interview-Like Selection Process

  • 会议论文

  • 发表日期：

    2025年

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  • Chuan-Wei Kuo,
  • Wen-Chih Peng,
  • Hsin-Ning Su

摘要

Patents are essential for securing market leadership and protecting intellectual property, but traditional valuation methods often fail to fully capture their strategic value. This paper introduces a causality-driven framework for patent valuation that integrates domain expertise with advanced language models in a structured interview-like process. By analyzing semiconductor patents filed between 1997 and 2007-a pivotal period of technological transformation-our approach employs Directed Acyclic Graphs (DAGs) and Structural Equation Models (SEMs) to reveal causal relationships, providing precise value attribution. This integration enhances the analysis of complex patent data, offering a powerful tool for aligning technological innovation with market and legal strategies.