ERA-IT：通过揭示的经济偏好对齐语义模型以实现实时和可解释的专利估值
ERA-IT: Aligning Semantic Models with Revealed Economic Preference for Real-Time and Explainable Patent Valuation
AI总结：
ERA-IT通过揭示的经济偏好对齐语义模型，实现实时且可解释的专利估值，提升预测准确性并增强决策透明度。
摘要：
在技术创新的战略管理中，由于高维技术规范中固有的信息不对称，对无形资产进行估值仍是一个关键挑战。传统的文献计量指标，如引用次数，由于数据积累中的系统延迟，无法及时解决这一摩擦。为此，本研究提出了经济推理对齐通过指令微调（ERA-IT）框架。我们理论上将专利续展历史视为揭示的经济偏好，并利用其作为客观监督信号，将大型语言模型（LLMs）的生成推理对齐于市场现实，这一过程我们称为生态语义对齐。使用随机采样的10,000份欧洲专利局专利数据集，涵盖多样化的技术领域，我们训练模型不仅预测价值层级，还从非结构化文本中逆向工程经济推理链。实证结果表明，ERA-IT在预测准确性上显著优于传统计量经济模型和零样本LLMs。更重要的是，通过生成明确且逻辑严谨的估值理由，该框架为决策者提供了透明的认知支架，减少了在高风险知识产权管理中的黑箱AI的不透明性。
英文摘要：
Valuing intangible assets under uncertainty remains a critical challenge in the strategic management of technological innovation due to the information asymmetry inherent in high-dimensional technical specifications. Traditional bibliometric indicators, such as citation counts, fail to address this friction in a timely manner due to the systemic latency inherent in data accumulation. To bridge this gap, this study proposes the Economic Reasoning Alignment via Instruction Tuning (ERA-IT) framework. We theoretically conceptualize patent renewal history as a revealed economic preference and leverage it as an objective supervisory signal to align the generative reasoning of Large Language Models (LLMs) with market realities, a process we term Eco-Semantic Alignment. Using a randomly sampled dataset of 10,000 European Patent Office patents across diverse technological domains, we trained the model not only to predict value tiers but also to reverse-engineer the Economic Chain-of-Thought from unstructured text. Empirical results demonstrate that ERA-IT significantly outperforms both conventional econometric models and zero-shot LLMs in predictive accuracy. More importantly, by generating explicit, logically grounded rationales for valuation, the framework serves as a transparent cognitive scaffold for decision-makers, reducing the opacity of black-box AI in high-stakes intellectual property management.
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