The Patent Covers This
OneShot Data→AI™: The Technical Foundation
The core of the framework is a patent-protected mathematical theorem specifying the necessary and sufficient conditions any learning rule must satisfy to preserve decision integrity across a cascade of AI models deployed in the field. It was derived analytically, not empirically, from the structural properties of LIF cascade systems.
The theorem defines five component-level conditions and four cascade and fleet-level extensions governing how validated updates propagate across multi-model systems and distributed deployments.
Why this matters: Every modern AI cascade deployed in production degrades when the operational environment differs from the training environment. The degradation is not linear. In a three-model cascade, a single miscalibrated component corrupts all downstream models simultaneously. The operator cannot distinguish "no threat present" from "system blind to the threat." Our patent-protected framework is the formal characterization of what must be true to prevent this failure class entirely.
Five Conditions the Framework Must Satisfy
1
Accuracy
End-to-end accuracy is maintained within tolerance across the full cascade during and after any in-field update.
2
Contract Maintenance
Inter-model representation binding is preserved. This is the condition no published learning rule satisfies. Representation binding is the statistical distribution match that allows each downstream model to correctly interpret upstream outputs. Any weight update must not corrupt the binding relationships with downstream models. This is the structural reason cascade failure is so severe: current learning rules optimize accuracy in isolation and have no mechanism to enforce binding contracts.
3
Hardware
The rule executes without DRAM reads, floating-point operations, or weight-matrix queries. This is the physical constraint imposed by neuromorphic hardware, not a performance optimization.
4
Locality
The rule uses only local synapse signals. No global error signal, no loss function, no communication with other nodes or external compute.
5
Convergence
The rule completes within one trial window, using only the data already on the processor. One shot.
No published learning rule satisfies all five conditions simultaneously. This is not a competitive claim. It is a verifiable statement about the published literature.
The Patent-Protected Learning Algorithm
Our patent-protected learning algorithm satisfies all five conditions by construction. It is derived from the steady-state dynamics of LIF neurons, whose convergence target is a hardware-physical constant computable from local neuron state using only integer arithmetic, without external signals and within a single trial window.
Because the algorithm operates on weight deltas rather than weight replacement, prior knowledge is preserved as the base weight matrix. Catastrophic forgetting is eliminated by construction, not engineered around. The same Recognition-Verification-Formulation mechanism that governs the learning algorithm also underlies ERIS, our DARPA-accepted proposal for verified machine thinking in real-time decision environments.
Seven hardware-mapper primitives translate the algorithm to any LIF substrate, enabling deployment on Intel Loihi 2, BrainChip Akida, BrainScaleS-2, and SpiNNaker2 without rewriting the rule.
USPTO Notice of Allowance: March 2026. The Recognition-Verification-Formulation mechanism and the autonomous delta dissemination determination are patent-protected. The framework is genuinely novel: the USPTO reviewed it against the prior art and agreed.
