Primary technical disclosure
Secondary technical
Runtime-Signed Adaptation Artifacts Spatial adaptation produces governed adaptation artifacts: each artifact carries a cryptographic integrity attestation, a capability scope, and a provenance-lineage record, and is sandbox-evaluated before activation. Consuming agents admit artifacts through the composite admissibility evaluator.Sandbox Pre-Activation Certification Each adaptation artifact is evaluated in a governance-credentialed sandboxed execution environment prior to activation, producing a lineage-recorded certification record with an admit-or-reject activation-gate outcome under U.S. Provisional Application No. 64/049,409.Admissibility as Skill Router The composite admissibility evaluator performs a unified governance gate and skill-domain-routing function, deciding whether a candidate generation step falls within an active adaptation artifact's capability scope.Always-Active Personal Layer The context-aware adapter-routing mechanism keeps an always-active personal-adaptation layer active across all generation contexts, excluded from routing de-weighting, so the consuming agent's individual adaptation continuity is preserved during governed generation.Cascade Deactivation Dependencies Adaptation artifacts declare prerequisite dependencies by identifier, minimum version, and compatibility-version-range. A dependency-validation gate admits a dependent only when its strict prerequisites are available; the cascade-deactivation mechanism deactivates a dependent upon deactivation of a strict prerequisite, with the sequence recorded in dependency lineage.Cross-Model Adaptation Portability Adaptations port across base cognitive substrates through credentialed compatibility evaluation and parameter remapping. Each artifact carries compatibility metadata; a compatibility evaluator and parameter-remapping generator transfer it to a structurally compatible substrate, with every determination recorded in lineage.Federated Skill Training Skill training operates federated across mesh participants. Each participant contributes local-adaptation updates under credentialed identity, and a secure-aggregation function combines them into a federated adaptation artifact whose provenance records the participants, contribution weights, aggregation method, and privacy-budget consumption.Composite Licensing Intersection When adaptation artifacts are composed, each source carries a licensing specification per Chapter 22. The composite artifact inherits a composite licensing specification equal to the most restrictive intersection of the source licensing specifications, alongside a fresh certification record requiring sandbox evaluation before activation.Decentralized Mesh Adaptation Distribution Adaptation artifacts and spatial adaptations propagate through the governed mesh by multi-hop relay and store-and-forward carriage, admitted by composite admissibility evaluation and sandbox pre-activation, without a central distribution server.
Applications · general
Governed In-Field AI Model Adaptation for Defense Operations How to adapt tactical AI/ML models in field operations without losing the audit trail: credentialed, admissibility-gated adaptation artifacts under bounded autonomy, built on the Spatial Adaptation layer of the governed spatial mesh (U.S. Provisional 64/049,409) and mapped to the DoD AI Hierarchy of Needs, DARPA Compass-class mission-aware learning, and edge-MLops for forward-deployed inference.Safe Runtime Updates for AI-Driven Industrial Robots: Governed Adaptation Under ISO 10218 and the EU Machinery Regulation Pushing AI model and skill updates to certified industrial robots breaks the static-certification assumption behind ISO 10218, IEC 62443, and the EU Machinery Regulation. The Spatial Adaptation inventive step (U.S. Provisional 64/049,409) makes each runtime update a credentialed, sandbox-gated, lineage-recorded governed observation, so adaptation stays authorized, attestable, and reversible across a robot fleet.Governing Adaptive Medical Device and SaMD Updates Under FDA PCCP and EU MDR How to update AI/ML-based medical devices (SaMD) in the field under FDA PCCP and EU MDR governance, using credentialed, sandbox-evaluated, lineage-recorded adaptation that stays attestable and reversible.Safe Rapid Security Updates for Safety-Critical Systems How to ship security patches fast enough for NIS2, UN R156, and FDA 524B deadlines without breaking safety certification: a credentialed, sandbox-gated, reversible update substrate built on the Spatial Adaptation inventive step of U.S. Provisional Application No. 64/049,409.Regulatory-Aware LLM Adaptation: Verifiable Governance for EU AI Act and FDA Compliance How to make LLM adaptation EU AI Act and FDA compliant: governance-credentialed adaptation artifacts that are signed, sandbox-certified before activation, runtime scope-gated, and portable across model substrates. Built on the Spatial Adaptation inventive step.IEC 62304 Compliance for Continuously Adapting Medical Device Software How to keep continuously adapting, AI-enabled medical device software inside the IEC 62304 lifecycle: governed signed adaptation artifacts, sandbox pre-activation certification, and jurisdiction-aware activation built on Spatial Adaptation (U.S. Provisional 64/049,409).Enforcing NIST AI RMF Compliance at Runtime for AI Model Fleets How the Spatial Adaptation layer disclosed in U.S. Provisional Application No. 64/049,409 turns NIST AI RMF (AI RMF 1.0) and the AI 600-1 Generative AI Profile from a document-based audit posture into a runtime-enforced, cryptographically attestable property of deployed AI model fleets across federal and commercial operators.UNECE R156 SUMS Compliance for In-Vehicle Software Updates How UNECE R156 Software Update Management System (SUMS) obligations for in-vehicle over-the-air updates are met natively by the Spatial Adaptation governed update primitive: signed artifacts, sandbox pre-activation evaluation, applicability-scoped activation, lineage, and certified rollback.
Applications · specific
Governed Adaptation Beyond Anthropic Skills: Admissibility-Gated, Reversible Capability Loading How the Spatial Adaptation layer of U.S. Provisional 64/049,409 relates to Anthropic Skills: governance-credentialed adaptation artifacts, sandbox pre-activation evaluation, admissibility-gated activation, lineage-recorded provenance, and reversible cascade-deactivation.OpenAI Fine-Tuning vs Governed Model Adaptation OpenAI fine-tuning adapts GPT models for customer-specific behavior. The spatial-adaptation primitive of U.S. Provisional 64/049,409 adds credentialed, admissibility-gated, lineage-recorded adaptation artifacts that make each adaptation authorized, attestable, and reversible.Tesla FSD Updates vs Governed Adaptation Artifacts How the governed adaptation-artifact model of U.S. Provisional 64/049,409 relates to Tesla FSD over-the-air updates: runtime-signed artifacts with sandbox pre-activation, declared scope, and credentialed cascade-deactivation as a structural layer beneath a mature OTA pipeline.AWS Bedrock vs Governed Adaptation: The Certifiable-Artifact Layer How the spatial-adaptation layer (U.S. Provisional 64/049,409) relates to AWS Bedrock: governed, credentialed, certifiable adaptation artifacts above the model-execution substrate.Databricks Mosaic AI vs Governed Adaptation Artifacts How the Spatial Adaptation inventive step of U.S. Provisional 64/049,409 compares to Databricks Mosaic AI on governed adaptation artifacts, sandbox pre-activation certification, federated-training provenance, and admissibility-gated activation.Google Vertex AI vs. Governed Adaptation: Who Owns Model-Adaptation Governance? How the spatial-adaptation primitive of U.S. Provisional 64/049,409 supplies credentialed, sandbox-certified, reversible adaptation governance as a layer above Google Vertex AI.Hugging Face Hub Alternative for Governed Model Adaptation How the Spatial Adaptation inventive step (U.S. Provisional 64/049,409) supplies a credentialed, admissibility-gated adaptation substrate above the Hugging Face Hub.Governed Agent Adaptation vs Anthropic Claude MCP How the Spatial Adaptation substrate of U.S. Provisional 64/049,409 relates to Anthropic Claude MCP and Agent Skills: signed adaptation artifacts, sandbox pre-activation, admissibility gating, and cascade-deactivation as a governance layer above capability composition.
How-to guides
How to Meet Regulatory Software-Update Rules for Autonomous Vehicles An architectural how-to for building a regulation-ready software-update pipeline for autonomous vehicles, using governed, signed adaptation and policy artifacts validated against authority credentials, sandbox evaluation, and continuity before activation.How to Safely Push Field Updates to Autonomous Systems An architectural how-to for delivering field updates to autonomous systems as signed, governed artifacts that are admitted only after passing authority, policy, continuity, and sandbox checks.How to Safely Roll Back a Bad Update to an Autonomous Fleet An architectural approach to reversible autonomous-fleet updates: signed adaptation artifacts, pre-activation sandbox evaluation, atomic policy-state transitions, and version-lineage rollback, disclosed in U.S. Provisional Application No. 64/049,409.
