生成式 AI 条件下的专利判断失真机制 - Judgment Distortion Mechanisms in Patent Systems under Generative AI Conditions

Under conditions where generative AI is deeply embedded into patent drafting, search, and filing processes, global patent systems may experience a non-linear and structural form of judgment distortion. Grounded in the Three Laws of CESI Judgment Physics, this paper proposes an analytical framework for understanding patent judgment distortion under generative AI conditions.

It argues that so-called “abnormal patents” are neither primarily technological failures nor ethical deviations, but the inevitable consequence of judgment loop collapse under irreversibility pressure and misaligned incentive structures. When symbolic generation capacity is massively amplified by AI without a corresponding expansion of judgment-bearing and verification capacity, patent systems enter a state characterized by judgment outsourcing, responsibility displacement, and decoupling from real-world innovation constraints.

This process follows the joint constraints of the Judgment Irreversibility Theorem (JIT), the Judgment Cost Conservation Law (JCCL), and the Judgment Loop Displacement mechanism Law (JLDL). The paper further demonstrates that current compliance-based governance responses represent a post hoc attempt by national innovation systems to compensate for failures that should have been addressed at the level of judgment architecture.

Rather than proposing improved decision-making methods, this study establishes non-negotiable boundary conditions for patent governance in the AI era: any AI-augmented patent system that violates judgment physics constraints will fail structurally under scale.