Micro AI Basic Units for Scalable Edge Control
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Solution Overview
Problem
Existing AI control systems face limitations such as latency, lack of scalability, limited resilience, and dependency on continuous cloud connectivity, with centralized architectures failing to integrate intelligent decision-making at the edge of the network.
Innovation Solution
A modular AI control system composed of Micro AI Basic Units (μAI Units) with localized AI reasoning, multi-protocol communication, and device-level control, forming scalable, self-healing, and collaborative networks through a hybrid communication infrastructure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If centralized processing architectures or cloud-based models are used, then powerful AI processing is achieved, but latency and dependency on continuous cloud connectivity increase
Solution Approach 1:
The patent divides the centralized AI system into distributed Micro AI Basic Units (μAI Units) that can operate independently at the edge. Each μAI Unit contains local AI reasoning capabilities, allowing processing to occur closer to the data source rather than requiring all data to travel to a centralized cloud server, thereby reducing latency while maintaining processing power through distributed computation.
Solution Approach 2:
The patent transitions from a single-dimensional centralized cloud architecture to a multi-dimensional distributed edge architecture. By deploying AI capabilities across multiple spatial dimensions (different locations, devices, and networks), the system achieves both low latency through local processing and high processing power through collective distributed intelligence.
2Power
If centralized processing architectures are used, then powerful AI processing is achieved, but scalability and resilience are limited
Solution Approach 1:
By segmenting the monolithic centralized AI system into numerous independent μAI Units, the patent creates a resilient distributed architecture where failure of individual units does not compromise the entire system. Each unit can continue operating autonomously, and the system can dynamically reconfigure around failed nodes, significantly improving reliability while maintaining aggregate processing power.
Solution Approach 2:
The patent changes the fundamental parameter of system architecture from centralized to distributed, transforming the reliability characteristics. This parameter change enables the system to maintain processing power while achieving superior resilience through redundancy, fault tolerance, and dynamic reconfiguration capabilities inherent in distributed networks.
3Reliability
If mesh networking technologies are used, then reliability and range of wireless communication are improved, but intelligent decision-making capabilities at the edge are not integrated
Solution Approach 1:
The patent merges mesh networking communication capabilities with embedded AI reasoning engines within the same μAI Units. This combination allows the edge devices to not only communicate reliably through mesh networks but also to autonomously process data and make decisions locally, eliminating the need for external processors while enhancing both communication reliability and edge intelligence.
Solution Approach 2:
The μAI Units are designed as universal edge devices that simultaneously perform multiple functions: mesh network communication, local AI inference, sensor data processing, and actuator control. This multi-functionality enables the system to achieve both improved communication reliability through mesh networking and enhanced edge decision-making capabilities within a single integrated platform.
Data Source
AI summary
A scalable AI control system is disclosed, based on modular AI micro-models that operate within embedded or distributed environments. Each micro-model is a compact, self-contained unit optionally configured to perform data-driven or symbolic reasoning, or a combination thereof. The invention includes secure containers with runtime enforcement, symbolic fallback mechanisms, and dynamic protocol adaptation. These micro-models may be deployed on hardware-independent platforms and are capable of autonomous or coordinated operation across mesh or non-mesh networks. The system enables flexible, verifiable control logic suitable for resource-constrained or adaptive embedded applications.