The foundation is built.
The proof is public.

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US 12,050,846 B1 — The Learning System

Describes how Estimators scale into a learning system — using wave patterns to find relationships and build fast inference paths.

US 12,572,717 B2 — Ripple Pattern Operations

Provides operational specifics of the ripple patterns — how the system propagates signals and executes inference along established paths.

US 2024/0362378 A1 — Universal learning fabric based on cellular automata

Published application covering the universal memory/logic grid and its application as a scalable learning architecture.

US 2025/0201304 A1 — Artificial intelligence based on cellular automata

Continuing application extending protection of the core cellular automata-based AI architecture.

WO 2024/178099 A1 — Universal Learning Fabric Based on Cellular Automata

International protection for the unified memory/logic grid and asynchronous communication protocols. Filed under the Patent Cooperation Treaty for global market coverage.

WO 2025/136734 A1 — Artificial Intelligence Based on Cellular Automata

Global rights regarding the identification of relationships through ripple propagation and signal collisions in a cellular automata-based architecture.

Decision Locality: A Substrate for AI Inference and Continual Learning

Establishes decision locality as the architectural substrate enabling inference and continual learning to happen in the same place, on the same hardware, without a round trip to a data center.

A Hardware-Native Bit-Serial Learner with Exact Statistical Structure

Proves that the Estimator’s bit-serial learning process is not an approximation — it carries exact statistical structure, validating the mathematical foundation of the Cincinnati Algorithm.

Convergence-Based Learning Fabrics: From Elemental Devices to Communication-Minimal Distributed AI Architectures

Traces the path from a single Estimator to a full Learning Fabric, demonstrating how convergence-based logic scales into a communication-minimal distributed architecture.

Bootstrap Discovery of Sensory Primitives for Embodied Visual Learning

Shows how the Learning Fabric can bootstrap its own sensory primitives directly from visual input, without pre-engineered feature extraction.

A Uniform, Tessellated Architecture for Energy-Efficient Learning and Inference

Proves that tessellated grid provides optimal spatial packing and communication efficiency for massively parallel, decentralized AI hardware. The foundational geometric case for the Learning Fabric.

Brain-CA: Dual-Speed, Low-Energy AI Architecture for Cognitive Learning

Demonstrates how separating fast signal propagation from slow state updates enables high-speed cognition at a fraction of standard GPU power consumption.

Brain-CA: A Cellular Automata-Inspired Architecture for Energy-Efficient CPU Design

Details the integration of the Cincinnati Algorithm into standard CPU environments to offload high-energy inference tasks to the Learning Fabric — the practical path to enterprise deployment.

Casting Off the Old Guard: Achieving Superior A.I. Performance through Simplification

Provides the theoretical framework for moving beyond the Von Neumann bottleneck — replacing power-hungry floating-point arithmetic with bit-level logic. The philosophical and scientific case for Teleomorphic Computing.