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.

翻译：在不确定性下评估无形资产仍然是技术创新战略管理中的一个关键挑战，这源于高维技术规范中固有的信息不对称。传统的文献计量指标，如引用次数，由于数据积累固有的系统性延迟，无法及时解决这种摩擦。为弥合这一差距，本研究提出了通过指令微调实现经济推理对齐（ERA-IT）框架。我们在理论上将专利续展历史概念化为一种揭示的经济偏好，并利用其作为客观的监督信号，使大语言模型（LLMs）的生成式推理与市场现实对齐，这一过程我们称之为生态语义对齐。利用一个涵盖不同技术领域的、随机抽样的10,000项欧洲专利局专利数据集，我们训练模型不仅能预测价值层级，还能从非结构化文本中逆向推导出经济思维链。实证结果表明，ERA-IT在预测准确性上显著优于传统的计量经济学模型和零样本LLMs。更重要的是，通过为估值生成明确、逻辑上站得住脚的理由，该框架为决策者提供了一个透明的认知支架，从而降低了高风险知识产权管理中黑盒人工智能的不透明性。