Communication Classification Engine for PSAP Priority Queues
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Solution Overview
Problem
PSAPs face challenges in efficiently filtering and classifying communications, including emergency, non-emergency, and irrelevant messages, which divert operator attention from genuine emergencies due to understaffing and increasing volumes of non-emergency and spam communications.
Innovation Solution
A communication analysis engine uses AI and machine learning to classify communications as emergency, non-emergency, or irrelevant, triaging them into separate queues for prioritized handling by human operators, and engages in scripted dialogues to gather additional information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If PSAP operators manually handle all communications, then all communications receive attention, but operator workload increases and response efficiency decreases
Solution Approach 1:
A communication analysis engine is introduced as an intermediary system between the communication interface and PSAP operators. This engine automatically classifies communications into emergency, non-emergency, and irrelevant categories, filtering out non-essential messages before they reach operators. The intermediary handles the complex classification task, allowing operators to focus only on emergency communications while maintaining high response efficiency.
Solution Approach 2:
The communication analysis engine performs self-service by automatically analyzing and classifying communications without requiring operator intervention. The system uses machine learning models to autonomously determine communication priorities, reducing the burden on operators and improving overall productivity while keeping the system relatively simple to operate.
2Reliability
If PSAP operators manually filter communications, then communication quality improves, but operator stress increases and time is lost
Solution Approach 1:
The manual mechanical process of operator-based communication filtering is replaced with an automated computer-based system. The communication analysis engine uses machine learning algorithms to automatically classify communications, achieving high classification accuracy without requiring operator time. This substitution eliminates the time loss associated with manual filtering while maintaining reliable communication triage.
3Loss of information
If all communications are routed to PSAP, then no messages are lost, but irrelevant messages divert attention from genuine emergencies
Solution Approach 1:
The communication stream is segmented into three distinct categories: emergency communications, non-emergency communications, and irrelevant communications. The communication analysis engine processes each segment differently, routing emergency messages to operators while filtering out non-emergency and irrelevant messages. This segmentation prevents irrelevant communications from distracting operators from genuine emergencies while maintaining complete information capture through the analysis engine's logging capabilities.
Data Source
AI summary
Particular example embodiments described herein can provide for a system, an apparatus, and a method for analyzing a communication from an electronic device to a PSAP, determining a classification for the communication, wherein the classification is one of an emergency PSAP classification, a non-emergency PSAP classification, or a not related to PSAP services classification, and sending the communication and the determined classification for the communication to the PSAP. In some examples, communications with the emergency PSAP classification are sent to a high priority queue at the PSAP, communications with the non-emergency PSAP classification are sent to a medium priority queue at the PSAP, and communications with the not related to PSAP service classification are sent to a low priority queue at the PSAP. In some examples, a computer model used to determine the classification for the communication.