AI Model Chaining for Sensor System Optimization
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Solution Overview
Problem
Coupling multiple data models to simulate real-world systems is challenging due to differences in objects and object properties, and existing technologies are limited in optimizing sensor systems and presenting data efficiently.
Innovation Solution
A system that uses artificial intelligence to determine relationships between model outputs and inputs by training an AI model to predict node connections, allowing for the chaining of models and optimization of sensor systems by monitoring model health and creating a model hierarchy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple data models are coupled to simulate real-world systems, then the simulation capability is improved, but the complexity of coupling and optimizing sensor systems increases
Solution Approach 1:
The patent introduces an AI-based node connection predictor as an intermediary system that automatically determines relationships between model outputs and inputs. This mediator analyzes model parameters, object properties, and simulated information to predict appropriate node connections, eliminating the need for manual coupling configuration and reducing the complexity of integrating multiple data models.
Solution Approach 2:
The system employs self-service mechanisms where the AI model automatically optimizes sensor system configurations by analyzing model health metrics and generating optimal node connections without human intervention. The system self-adjusts model hierarchies, replaces models based on health comparisons, and autonomously refines simulation parameters to maintain optimal performance.
2Ease of operation
If manual coupling of models is used, then the control over model relationships is improved, but the time required for optimization and configuration increases
Solution Approach 1:
The patent implements preliminary action through pre-training of the AI node connection predictor on extensive datasets of model relationships and parameters. The system pre-establishes frameworks for model health assessment and optimization strategies, enabling rapid automated configuration without requiring time-consuming manual analysis during actual model coupling operations.
3Quantity of substance
If existing sensor systems are used, then the hardware infrastructure is maintained, but the efficiency of data analysis and system optimization is limited
Solution Approach 1:
The patent applies parameter changes by transforming the operational parameters of existing sensor systems through AI-enhanced data processing. The system modifies how sensor data is interpreted by dynamically adjusting analysis parameters based on model health metrics, object properties, and predicted relationships, thereby significantly improving data analysis efficiency without replacing the physical sensor infrastructure.
Data Source
AI summary
Systems, computer program products, and computer-implemented methods for determining relationships between one or more outputs of a first model and one or more inputs of a second model that collectively represent a real world system, and chaining the models together. For example, the system described herein may determine how to chain a plurality of models by training an artificial intelligence system using the nodes of the models such that the trained artificial intelligence system predicts related output and input node connections. The system may then link related nodes to chain the models together. The systems, computer program products, and computer-implemented methods may thus, according to various embodiments, enable a plurality of discrete models to be optimally chained.