AI Emergency Call Triage for Faster PSAP Situational Awareness
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Solution Overview
Problem
Current emergency response systems at PSAPs are limited by human dispatcher effectiveness and lack integration of multimedia data, leading to inefficiencies and potential delays in diagnosing emergencies and detecting large-scale incidents.
Innovation Solution
An AI engine that processes audio, visual, and text data from emergency calls to provide prompts and recommendations to human operators, while aggregating and analyzing data across multiple PSAPs for pattern recognition and real-time alerting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human operators manually analyze emergency calls and determine question priorities, then the system can handle complex diagnostic reasoning, but the response time is delayed and productivity is reduced
Solution Approach 1:
An AI engine is introduced as an intermediary between the emergency call data and human operators. The AI engine automatically extracts questions, determines their priorities using machine learning models trained on emergency data patterns, and presents them to operators in ranked order. This intermediary handles the time-consuming analysis work while preserving human expertise for final decision-making, thus resolving the contradiction between diagnostic accuracy and response speed.
Solution Approach 2:
The patent replaces the manual mechanical process of human operators reading through entire call transcripts and mentally prioritizing questions with an automated AI-based system. The AI engine uses natural language processing and machine learning to automatically analyze call data, extract critical information gaps, and rank questions by priority. This substitution dramatically reduces the time required while maintaining or improving diagnostic accuracy through consistent application of trained models.
2Measurement precision
If PSAPs process only individual calls independently, then each call receives focused attention, but large-scale incidents involving multiple locations are not detected
Solution Approach 1:
The patent merges data from multiple geographically distributed PSAPs into a centralized analysis system. The AI engine aggregates call data, location information, and contextual data from numerous PSAPs simultaneously, enabling it to detect patterns and correlations that span multiple locations. This merging allows the system to identify large-scale incidents such as widespread power outages, natural disasters, or public health events by analyzing aggregated data across the entire network rather than isolated individual calls.
Solution Approach 2:
The AI engine is designed with multi-functionality to handle both individual call analysis and aggregate pattern recognition. The same system that processes single calls for diagnostic support also continuously analyzes streams of data from multiple PSAPs to detect broader incident patterns. This universal system performs multiple functions—local diagnostic assistance and global incident detection—within a unified architecture, resolving the contradiction between focused individual processing and comprehensive surveillance.
3Reliability
If the AI engine analyzes all incoming data in real-time without pre-processing, then no critical information is lost, but the computational load increases processing time
Solution Approach 1:
The patent implements preliminary action through automated pre-processing of incoming call data before full AI analysis. The system performs initial steps such as automatic transcription of voice calls to text, extraction of key entities and keywords, and structuring of unstructured data into standardized formats. These preliminary actions prepare the data in advance, reducing the computational burden during real-time analysis while ensuring no critical information is lost. The pre-processed data is then fed into the main AI engine for priority determination and diagnostic support.
Data Source
AI summary
A system for providing emergency assistance includes receiving audio, visual, or text data about an ongoing emergency at a public safety answering point, comparing that data to a database of other emergencies with an artificial intelligence engine, recognizing patterns in aggregated and correlated data by the artificial intelligence engine, and generating appropriate audio, visual, and text output for a human operator to respond to the emergency. An AI engine receives audio data, visual data, and text data related to ongoing emergencies being received at public safety answering points and compares it with data from other emergencies. The AI engine identifies key words and patterns and generates appropriate messages for operators. The system continuously generates feedback to the AI engine to aid in machine learning. The feedback includes: “message was correct; continue drawing this conclusion”; and “message was wrong; do not continue drawing this conclusion.”