Representation-Embedded Governance Sytems for Fidelity-Adaptive Regulation of Machine Intelligence Execution Without Explicit Control Variables
OwnerDarren Christopher Tindaleinventor
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OwnerDarren Christopher Tindaleinventor
Estimating the term…
Representation-embedded governance systems for regulating execution of machine intelligence processes are disclosed. A computational model generates internal representations that evolve within a constrained representational state space defining execution-permissible and execution-restricted conditions. Execution capability is determined by representational admissibility of the internal representations, such that restricted states structurally preclude formation, representation, dispatch, or execution of operations that lack valid executable mappings to outputs, tool invocations, memory accesses, scheduling entries, or system-level actions. Structural constraints may be encoded in transformation operators, learned parameters, decoder mappings, masks, or dynamical convergence behavior. Within permissible regions, execution availability, fidelity, probability, and resource allocation may be modulated. Execution regulation occurs without computing or evaluating independently defined control variables, providing intrinsic machine-intelligence execution control through representational structure.
A system for regulating execution of a computational process, comprising:
A system for regulating execution of a computational process, comprising:
A method for regulating execution of a computational process, comprising:
Dependencies are inferred from the claim text.