AI Agent Dispatch System for Emergency Response Time Reduction
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Solution Overview
Problem
Current emergency response systems face challenges in reducing response time during major incidents, as calls often enter a waiting queue, delaying the transfer of critical information to first responders.
Innovation Solution
A computer-implemented method and system utilizing an AI agent that monitors emergency environments in real-time, analyzes emergency incident information, and ad-hoc dispatches first responders using Reinforcement Learning to optimize call distribution and information sharing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If calls are directed towards a waiting queue during high-volume events, then call operators can manage the influx of calls, but critical information is delayed being transferred to first responders
Solution Approach 1:
The AI agent performs preliminary analysis of calls in the waiting queue before they reach call operators, extracting critical information and preparing dispatch decisions in advance. This allows the system to act immediately when calls are answered, rather than waiting for complete information gathering during the call.
Solution Approach 2:
The AI agent serves as an intermediary between the waiting queue and first responders, analyzing calls and preparing dispatch information without requiring call operators to manually process each call. This intermediary layer enables parallel processing of multiple calls while maintaining information quality.
2Loss of information
If call operators manually process each emergency call, then complete information can be gathered, but the system cannot handle massive call volumes during catastrophic events
Solution Approach 1:
The AI agent enables self-service processing of emergency calls by automatically analyzing call content, extracting critical information, and preparing dispatch decisions without human intervention. This allows the system to handle massive call volumes while maintaining information quality comparable to manual processing.
Solution Approach 2:
The patent replaces the mechanical system of manual call processing with an automated AI-based analysis system. The AI agent uses machine learning models to transcribe and analyze call content, substituting human operators' information gathering capabilities with automated computational processes that can handle unlimited call volumes.
3Loss of time
If call operators have overall situation awareness, then appropriate emergency departments can be informed timely, but this requires manual monitoring of all calls which is impossible during high-volume events
Solution Approach 1:
The AI agent provides universal monitoring capabilities across all calls in the system simultaneously, rather than requiring individual call operators to monitor specific calls. The single AI agent can analyze thousands of calls in parallel, providing overall situation awareness that no human operator could achieve alone.
Data Source
AI summary
A computer-implemented method and system for response-time-reduction in emergency incident events can include an artificial intelligent agent (AI Agent) that can monitor an emergency environment in real-time upon an occurrence or change in one or more emergency incident information values. The AI Agent can analyze if one or more emergency incident information values occurs and/or reaches across one or more predefined thresholds. One or more first responders can then be automatically dispatched if the analysis has shown that this is necessary. The AI Agent can be rewarded based on its performance of monitoring and/or dispatching and can adjust the one or more predefined thresholds according to the reward.