CROSS-REFERENCES
CROSS-REFERENCE TO RELATED APPLICATIONS
This application claims the benefit of and priority to U.S. Provisional Patent Applications No. 63/967,576, No. 63/974,083, No. 63/974,099, No. 63/988,460, No. 63/988,475, No. 63/988,480, No. 63/991,254, and No. 63/991,263. All prior provisional applications are incorporated herein by reference in their entireties.
I.FIELD OF THE INVENTION
[0001]The present disclosure relates generally to computational resource control, machine learning, and digital twin simulation systems. More specifically, it relates to systems and methods for preventing accelerator allocation and dispatch for infeasible simulator actions in multi-clone tumor dynamics simulators by utilizing cryptographic feasibility tokens, structural omission of device memory allocation routines, and deterministic execution boundaries driven by factorized, knowledge-grounded sensitivity tensors.
II.BACKGROUND OF THE INVENTION
1.Parameter Non-Identifiability in Multi-Clone ODE Systems
[0002]Advanced digital twin platforms employ neural differential equations to model tumor dynamics, simulating the competitive growth of distinct subclonal populations (clones) under selective drug pressure and immune interactions. The differential equation solver requires precise parameterization, including a drug sensitivity parameter (β) and a growth rate (ρ) for each clone. However, a critical technical problem arises: bulk survival data is structurally insufficient to disambiguate which specific subclone responds to which specific drug. Mathematically, β and ρ are collinear in the Fisher Information Matrix for bulk survival data, rendering the parameters structurally under-identified.
2.Computational Consequences of Under-Identified Parameters
[0003]When these hallucinated, under-identified parameters are injected into the solver, the resulting simulated trajectories are mathematically unstable. This instability manifests as specific computational failure modes: generation of NaN or infinite trajectory values, solver step rejection loops where the adaptive numerical integrator repeatedly halves the step size until reaching machine epsilon, and wasteful allocation, fragmentation, and churn of GPU memory as the system attempts to retry divergent simulation batches.
III.SUMMARY OF THE INVENTION
[0004]The invention provides a system that: (1) factorizes drug sensitivity parameters using external pharmacological knowledge bases (e.g., GDSC cell-line response data, OncoKB clinical annotations) to deterministically generate per-clone β tensors grounded in real drug-pathway associations; (2) issues cryptographic feasibility tokens that bind specific parameter configurations to GPU execution authorization; (3) structurally omits device memory allocation for parameter configurations that violate identifiability constraints; and (4) enforces deterministic execution boundaries that prevent the ODE solver from processing under-identified parameter sets.
[0005]The knowledge-grounded approach replaces the learned (and under-identified) drug sensitivity parameter with a deterministic function of the patient's clonal architecture and known drug-pathway relationships, physically restricting the executable simulation run-set and preventing numerical divergence. The system includes a Mechanism Operator that computes Spearman correlations between pathway activations and drug response across cell lines, producing a drug-pathway sensitivity matrix that anchors per-clone β values.
IV.CLAIMS
20 claims covering knowledge-grounded parameter factorization, cryptographic feasibility tokens, structural memory allocation omission, deterministic execution boundaries, the Mechanism Operator for drug-pathway sensitivity profiling, and per-clone β anchoring in multi-compartment ODE tumor simulators.
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