Distributed Intelligence System Hierarchical AI Communication
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Solution Overview
Problem
Existing distributed intelligence systems face challenges in managing real-time communications between components, particularly in large systems with multiple service instances, leading to synchronization issues and inefficiencies.
Innovation Solution
A distributed intelligence system architecture that utilizes a hierarchical structure for efficient AI-to-AI communications, allowing multiple AI models to operate in concert, aggregate, or break down different types and sizes of AI models, and optimize data transfer and processing between models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If REST-based communications are used between components in a large system, then component communication is enabled, but synchronization issues and inefficiencies occur in real-time communications
Solution Approach 1:
The patent introduces an intermediary communication layer that translates between traditional REST-based component communications and AI model-specific data formats. This intermediary enables efficient AI-to-AI communications by automatically formatting outputs from one model to match the input requirements of another model, resolving synchronization issues without requiring manual intervention or complex error handling.
Solution Approach 2:
The system dynamically changes communication parameters based on the specific AI models involved. By detecting the input and output schemas of different models, the system automatically adjusts data formatting parameters (such as data types, structures, and protocols) to optimize communication efficiency and synchronization for each specific model pair, rather than using a fixed REST protocol.
2Adaptability or versatility
If multiple AI models are deployed to perform different functions, then system functionality is improved, but managing real-time communications becomes more complex
Solution Approach 1:
The patent creates a universal communication framework that can handle multiple AI models with different functions and data requirements through a single unified interface. The system automatically detects and adapts to various model schemas, enabling one communication infrastructure to serve multiple purposes across different models without requiring separate communication management for each model pair.
Solution Approach 2:
The system implements automatic feedback loops where the communication manager detects the output format of one AI model and automatically adjusts the input format for the next model. This closed-loop feedback mechanism continuously optimizes communication based on actual model behaviors, reducing complexity by automating what would otherwise require manual configuration and monitoring.
3Adaptability or versatility
If AI models are aggregated or broken down into different sizes and types, then system flexibility is improved, but data transfer and processing becomes more difficult
Solution Approach 1:
The patent dynamically changes data transfer parameters based on the specific models involved in the aggregation or breakdown. The communication manager automatically adjusts data formatting, compression, and transmission protocols to match the requirements of different model sizes and types, making data transfer as easy as with uniform models while maintaining the flexibility to handle diverse model architectures.
Data Source
AI summary
A method is disclosed for analyzing a manifest of an application. The method includes accessing an application that is tasked with performing a set of functions. The set of functions are outlined in a manifest for the application. The method further includes accessing the manifest, parsing the manifest to identify each function in the set of functions, and selecting one or more artificial intelligence (AI) models to perform each function in the set of functions.