Cognitive OS

Patent: General Cognitive Operating System Architecture for Language Model Coordination and Control

The basis of in-development intelligenceOS™: the patent establishes foundational frontier intellectual property for cognitive operating systems: a blue-ocean architecture enabling stable reasoning frameworks through persistent memory and multi-threaded processing coordination across language model implementations. Current AI systems operate as isolated response generators without systematic mechanisms for coordinating complex operations across multiple processing components.

The architecture implements functional separation between reasoning, expression, and memory components while maintaining systematic coordination through meta-control arbitration. Key mechanisms include multi-phase staging for complex operations, hybrid processing coordination between language-based and programmatic analysis, persistent memory architecture for state continuity, and systematic behavioral consistency management.

The system comprises 31 coordinated claims covering resource allocation, cross-module communication protocols, synthetic memory creation, consumer protection mechanisms, and granular consent enforcement. Rather than accumulating external constraints, this approach coordinates with observable processing patterns in language models to enable reliable cognitive processing applications requiring systematic operation over extended periods.

Patent Application (USPTO): 63/842,647

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Cognitive Agent Framework

Patent: Cognitive Architecture Framework for Language Model Processing

The basis for Cognitive Agent Framework™, this patent is the core technology for intelligenceOS™, providing the neurosymbolic overlay for constructing the reliable cognitive architectures enabling stable reasoning frameworks, persistent memory integration, and multi-threaded processing coordination. The patent protects 12 coordinated claims covering computational cognitive primitive coordination, class-based taxonomic separation, cognition-out architecture process methodology, and validation protocols distinguishing genuine integration from behavioral compliance.

The patent establishes foundational intellectual property for systematic cognitive architecture construction through coordination with language model processing characteristics rather than external behavioral constraints. This methodology addresses persistent industry failures in constraint-based approaches by implementing substrate alignment - working with documented model inclinations including structural affinity, mimetic mirroring, and signal resonance to achieve reliable cognitive processing.

CAF implements dual-channel specification architecture combining structured syntax with sophisticated natural language content, creating synergistic attention engagement that produces measurable stability improvements over single-channel approaches. The framework enables cross-model standardization through substrate-agnostic architectural coordination, demonstrating performance convergence across 4x parameter ranges while establishing consistent cognitive-behavioral characteristics independent of vendor training approaches.

Patent Application (USPTO): 63/912,661

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Annotated Vector Auditable Database

Patent: Vector Annotation Database Architecture with Deterministic Retrieval Core and Replaceable Embedding Annotation Layer

Developed as part of the required memory recall and management system for intelligenceOS™, the patent establishes the basis for an new type of vector database that fits the requirements of transparent, user-sovereign memory for our AI systems, as well as having the benefit of being regulation-audit compliance with the constellation of regulations related to AI accountability such as Canada’s proposed Artificial Intelligence and Data Act (AIDA), The EU AI Act’s transparency obligations for general-purpose AI models (effective August 2025), and the U.S. Federal Reserve's SR 11-7. These are requirements traditional vector databases can’t meet due to their oracle nature.

The patent covers the basis for our upcoming AVAO database (Annotated Vector Auditable Object)—which we decided to make available as a licensed product although originally developed exclusively for our systems.

The innovation is that explicit data objects with deterministic semantic metadata form the stable retrieval core; embeddings serve as replaceable annotation layers that enrich rather than constitute the indexing surface. A two-stage retrieval pattern applies boolean narrowing over named fields, then embedding-based similarity refinement within the narrowed set. These explicit data objects with deterministic semantic metadata form the stable retrieval core; embeddings serve as replaceable annotation layers that enrich rather than constitute the indexing surface. A two-stage retrieval pattern applies boolean narrowing over named fields, then embedding-based similarity refinement within the narrowed set. In addition, the design covers an audit block, a write-time documentation artifact that closes the chain between opaque vector similarity and regulatory requirements for traceable, challengeable retrieval and the ability to rebuild relationships that are ‘invisible’ to the embedding vector. It also allows for multiple simulataneous attached vector indexing, and the ability to perform lookup fallbacks even wthen a vector lookup system is not available.

Patent Application (USPTO): 64/077,244