P246 — AIEP — Canonical Cognitive State Snapshot Primitive

Applicant: Neil Grassby Classification: Patent Application — Confidential Priority: Claims priority from GB2519711.2 filed 20 November 2025 Architecture Layer: AIEP Phase 2 Hardware State Layer

Framework Context

[0001] This specification operates within an AIEP environment as defined in GB2519711.2 and GB2519798.9. The present specification defines a hardware primitive for capturing a canonical, cryptographically bound snapshot of the complete AIEP system cognitive state — the combined state of the CWSG, reasoning memory, active goal set, and governance state — at a defined instant.

Field of the Invention

[0002] The present invention relates to canonical state snapshot primitives for complete cognitive state capture in evidence-bound AI systems.

Background

[0003] AIEP system continuity after restart, migration, or failover requires the ability to capture and restore the full cognitive state without loss of epistemic fidelity. A canonical snapshot must capture: the CWSG with all node and edge attributes; the evidence ledger state; active goals and their progress; long-term memory contents; and the governance policy state. A hardware primitive that coordinates this multi-component capture atomically prevents inconsistency between snapshot components.

Summary of the Invention

[0005] The invention provides a Canonical Cognitive State Snapshot Primitive (CCSSP) that: issues a globally synchronised quiesce signal to all AIEP subsystems; waits for in-flight operations to reach stable boundaries; captures the state vector of each subsystem via registered read handlers; constructs a canonical snapshot record containing all component state vectors with their individual hashes; computes an aggregate snapshot hash; signs the snapshot record; and stores it to the snapshot repository.

ASCII Architecture

Snapshot Trigger (scheduled / on-demand)
              |
              v
+------------------------------------------+
| Canonical Cognitive State Snapshot       |
|   Primitive (CCSSP)                      |
|                                          |
|  Global quiesce signal                  |
|  Stable boundary wait                   |
|  Component state capture:               |
|    - CWSG snapshot (P200)               |
|    - Evidence ledger state             |
|    - Active goal set (P210)            |
|    - LTM state (P208)                  |
|    - Governance state (P215)           |
|  Canonical record construction         |
|  Aggregate hash + signature            |
|  Snapshot repository admission         |
+------------------------------------------+

Detailed Description

[0005] Global Quiesce. The CCSSP issues a quiesce signal to all registered subsystems via a hardware quiesce bus. Subsystems halt at their next stable operation boundary — typically at the completion of an atomic state transition.

[0006] Component Capture. Each subsystem provides a registered state read handler that returns a serialised state vector and its SHA-256 hash. The CCSSP calls all handlers in canonical order (defined by subsystem registration index) and collects the vectors.

[0007] Canonical Record. The snapshot record is a structured document containing each component vector with its hash, a global timestamp, a session identifier, and a schema version. The components are stored in canonical serialisation order to ensure identical records for identical system states.

[0008] Aggregate Hash. The aggregate snapshot hash is computed as the SHA-256 of the concatenated component hashes in canonical order, providing a single identity for the complete cognitive state.

[0009] Resume. After snapshot completion, the quiesce signal is released and subsystems resume.

Technical Effect

[0010] The invention provides a system-wide atomic cognitive state snapshot for evidence-bound AI systems, enabling deterministic migration, fault recovery, and cross-node state transfer with epistemic fidelity. By asserting a global quiesce signal before capture and collecting all subsystem state vectors via canonical-order registered handlers, the primitive guarantees a consistent whole-system state snapshot free from partial-update artefacts. The aggregate SHA-256 hash provides a single cryptographic identity for the snapshot enabling equality comparison, integrity verification, and ledger-based provenance tracking of cognitive state.

Claims

1. A method of capturing a canonical cognitive state snapshot in an evidence-bound artificial intelligence system, comprising the steps of: (a) asserting a global quiesce signal to suspend all AIEP subsystems from new state mutations during snapshot capture; (b) calling the registered state read handler of each subsystem in canonical registration order, collecting the serialised state vector and its SHA-256 hash from each handler; (c) constructing a canonical snapshot record comprising each component state vector, its hash, a global timestamp, a session identifier, and a schema version, with all components stored in canonical serialisation order; (d) computing an aggregate snapshot hash as the SHA-256 of the concatenated component hashes in canonical order; (e) releasing the quiesce signal to resume subsystem operations and admitting the completed snapshot record with its aggregate hash to the evidence ledger.

2. The method of claim 1, wherein canonical registration order is determined by a fixed subsystem registration index assigned at system initialisation.

3. The method of claim 1, wherein snapshot records are used as transfer packages for cognitive state migration between AIEP nodes, verified by checking the aggregate hash on the receiving node before activation.

4. The method of claim 1, wherein the snapshot record is used for fault recovery, enabling the system to restore cognitive state from a stored snapshot on restart.

5. The method of claim 1, wherein the quiesce signal is propagated to all subsystems before capture begins, and the snapshot is not initiated until all subsystems have acknowledged quiesce.

6. A canonical cognitive state snapshot primitive for an evidence-bound artificial intelligence system, comprising: a quiesce controller asserting and managing the global quiesce signal; a state capture coordinator calling registered subsystem handlers in canonical order; an aggregate hash computer computing SHA-256 over concatenated component hashes; and a ledger admission module admitting completed snapshot records to the evidence ledger.

7. A computer-readable medium carrying instructions for implementing the method of any preceding method claim.

Abstract

A canonical cognitive state snapshot primitive for evidence-bound artificial intelligence asserts a global quiesce signal across all AIEP subsystems, then collects serialised state vectors from each subsystem via registered handlers called in canonical order. A canonical snapshot record is constructed containing all component vectors and hashes and an aggregate SHA-256 hash computed over concatenated component hashes in canonical order. The snapshot record is admitted to the evidence ledger. The snapshot enables deterministic cognitive state migration, fault recovery, and cross-node transfer with cryptographically verifiable epistemic fidelity.