ERA-IT: Aligning Semantic Models with Revealed Economic Preference for Real-Time and Explainable Patent Valuation

• URL: http://arxiv.org/abs/2512.12869v1
• Date: Sun, 14 Dec 2025 23:04:07 GMT
• Title: ERA-IT: Aligning Semantic Models with Revealed Economic Preference for Real-Time and Explainable Patent Valuation
• Authors: Yoo Yongmin, Kim Seungwoo, Liu Jingjiang,
• Abstract summary: This study proposes the Economic Reasoning Alignment via Instruction Tuning (ERA-IT) framework.<n>We theoretically conceptualize patent renewal history as a revealed economic preference and leverage it as an objective supervisory signal.<n>We trained the model not only to predict value tiers but also to reverse-engineer the Economic Chain-of-Thought from unstructured text.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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