Asynchronous Emergency Incident Reporting System
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Solution Overview
Problem
In large-scale emergency incidents, the high volume of concurrent calls overwhelms emergency call-takers, leading to significant delays in resource dispatch and potentially life-threatening response times.
Innovation Solution
A computer-implemented method and system for asynchronous reporting of emergency incidents, allowing eyewitnesses to capture and send data (such as photos or videos) directly to an emergency service platform, which processes and verifies the data using AI/ML to automatically dispatch necessary resources without human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If synchronous emergency calls (audio, video, text) are used, then callers can communicate with call-takers in real-time, but response time increases significantly during high-volume incidents due to call-taker bottlenecks
Solution Approach 1:
The system enables self-service by allowing eyewitnesses to autonomously capture incident data using their electronic devices and submit it directly to the emergency service platform without requiring call-taker intervention. The AI/ML system then automatically processes this data, classifies the emergency type, and dispatches appropriate resources, eliminating the human bottleneck entirely for initial assessment and dispatch.
Solution Approach 2:
The patent replaces the mechanical human call-taker system with an automated AI/ML processing system. Instead of human agents manually assessing calls, determining emergency types, and coordinating dispatch, an intelligent algorithm automatically analyzes submitted data, classifies incidents, and triggers resource deployment, significantly reducing response time while maintaining accuracy.
2Productivity
If multiple call-takers are deployed to handle concurrent calls, then service capacity increases, but operational complexity and coordination overhead increase
Solution Approach 1:
The patent extracts the complex decision-making and coordination functions from the call-taker system and consolidates them into a single centralized AI/ML processing platform. This eliminates the need for multiple human operators and their associated coordination complexities, as the automated system handles all incident assessments and dispatch decisions through a unified algorithmic approach.
Solution Approach 2:
The AI/ML processing system serves as a universal platform that can handle all types of emergency incidents (medical, fire, police, etc.) through a single integrated system. Instead of requiring specialized call-takers for different incident types, the multi-functional algorithm automatically classifies and routes any incident type appropriately, simplifying the overall system architecture.
3Reliability
If call-takers manually assess and coordinate each emergency call, then accurate resource dispatch is achieved, but significant time is lost during assessment and coordination
Solution Approach 1:
The system performs preliminary action by having eyewitnesses capture and submit incident data (photos, videos, location information) immediately upon observing an emergency, before any assessment or dispatch coordination is needed. This pre-captured data is then rapidly processed by the AI/ML system, eliminating the time required for manual assessment while ensuring accurate resource dispatch based on the pre-collected evidence.
Data Source
AI summary
A computer-implemented method and corresponding system that can allow an eyewitness of an emergency incident to instantly report it to the emergency services by sending data such as a photo of the emergency scene, without having to wait for a call-taker or agent to answer a call. This data is processed by an emergency service platform, which automatically dispatches the required resources to the emergency scene, thus managing to significantly reduce the response time of emergency services.