Intelligent Agents for Computer-Aided Dispatch Data Analysis
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Solution Overview
Problem
Current computer-aided dispatch systems rely on rule-based logic and struggle to efficiently analyze vast amounts of data to make quick and accurate dispatch decisions, particularly in complex emergency situations, often requiring human intervention to sift through information.
Innovation Solution
The implementation of a virtual dispatch assist system with Intelligent Agents that leverage artificial intelligence and machine learning to autonomously analyze CAD data, detect outliers, patterns, and correlations, and provide notifications to dispatchers, enabling proactive decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If rule-based logic is used in CAD systems, then the system structure is simple and easy to understand, but the system cannot efficiently analyze vast amounts of data to make quick and accurate dispatch decisions
Solution Approach 1:
The patent replaces traditional rule-based mechanical logic with artificial intelligence and machine learning systems. The AI/ML engine autonomously analyzes CAD data, detects patterns, and generates notifications without relying on pre-programmed rules, thereby dramatically improving data analysis efficiency while accepting increased system complexity
Solution Approach 2:
The AI/ML system performs self-learning and autonomous analysis of dispatch data without requiring constant human intervention or manual rule updates. The system automatically identifies patterns, outliers, and correlations in the data, enabling it to improve its performance over time while reducing the need for human operators to sift through information
2Loss of time
If human intervention is used to sift through information, then decision accuracy can be maintained, but the time required for dispatch decisions increases
Solution Approach 1:
The patent replaces human operators with an AI/ML-based virtual dispatch assist system that autonomously analyzes CAD data, detects patterns, and generates notifications. This substitution eliminates human response time limitations while maintaining or improving decision accuracy through advanced pattern recognition and data analysis capabilities
Solution Approach 2:
The AI/ML system continuously pre-analyzes CAD data in the background, detecting patterns, outliers, and correlations before dispatch decisions are needed. This preliminary analysis enables the system to provide immediate, accurate notifications when relevant events occur, eliminating the delay associated with human information processing
3Measurement precision
If vast amounts of CAD data are analyzed, then more accurate dispatch decisions can be made, but the computational resources and time required increase
Solution Approach 1:
The AI/ML system extracts only the most relevant features and patterns from vast amounts of CAD data, focusing computational resources on identifying critical dispatch opportunities rather than processing every data point equally. This selective extraction maintains high decision accuracy while reducing overall computational resource consumption
Solution Approach 2:
The system dynamically adjusts analysis parameters and data processing depth based on the specific dispatch context and available resources. By changing parameters such as analysis granularity, time windows, and pattern complexity, the system optimizes the balance between decision accuracy and computational resource consumption for different operational scenarios
Data Source
AI summary
Exemplary embodiments of the present invention provide a virtual dispatch assist system in which various types of Intelligent Agents are deployed (e.g., as part of a new CAD system architecture or as add-ons to existing CAD systems) to analyze vast amounts of historic operational data and provide various types of dispatch assist notifications and recommendations that can be used by a dispatcher or by the CAD system itself (e.g., autonomously) to make dispatch decisions.