Dispatcher Virtual Assistant for Emergency Dispatch Automation
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Solution Overview
Problem
Emergency dispatch centers face inefficiencies due to reliance on human skill and experience, leading to high workload, response time issues, and potential human errors, compounded by a low supply of dispatchers and high turnover rates, with existing CAD systems failing to fundamentally address these challenges.
Innovation Solution
The implementation of a Dispatcher Virtual Assistant (DVA) system utilizing machine learning and deep learning algorithms, comprising a virtual assistant control unit, dispatcher language model, incident-status tracker, natural language generator, database, and graphic user interface, which can update incident status in real time, recommend actions, answer inquiries, and generate reports, and can be used in conjunction with existing CAD systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual incident status updating is used, then dispatchers can maintain control over emergency responses, but response time increases and human errors occur
Solution Approach 1:
The system enables self-service automation where the incident status tracking system automatically updates status without requiring manual dispatcher intervention. The system monitors incident parameters, processes information autonomously, and maintains status records automatically, freeing dispatchers from repetitive manual updating tasks while ensuring consistent and accurate status tracking.
Solution Approach 2:
The patent replaces the mechanical manual process of status updating with an automated computational system. Machine learning models and algorithms substitute human cognitive and manual operations, processing incident information and updating status automatically. This substitution eliminates human error and reduces time delays associated with manual intervention.
2Productivity
If more dispatchers are trained to handle increased workload, then service capacity increases, but training costs and time increase due to low supply and high turnover
Solution Approach 1:
The system creates a virtual assistant that copies and automates expert dispatcher knowledge and decision-making processes. Instead of continuously training new human dispatchers, the system captures expert knowledge in machine learning models and algorithms, replicating their capabilities in an automated virtual assistant that can be deployed immediately without training time.
Solution Approach 2:
The patent transforms the human resource parameter (dispatcher availability) into a technological parameter (automated system capability). By changing from relying on human dispatcher quantity and expertise to relying on automated system parameters, the organization can scale service capacity without being constrained by training time, cost, or human turnover rates.
3Loss of information
If manual incident report preparation is performed, then accurate incident documentation is achieved, but time and manpower consumption increase
Solution Approach 1:
The system maintains continuous automatic documentation throughout the incident lifecycle. Rather than requiring discrete manual report preparation after incidents, the system continuously tracks, records, and updates incident information in real-time, ensuring that complete and accurate reports are automatically generated and available immediately when needed, eliminating the time-consuming manual compilation process.
4Productivity
If automated systems are implemented, then response time and accuracy improve, but system complexity increases
Solution Approach 1:
The virtual assistant system is designed as a multi-functional platform that handles multiple tasks including incident status tracking, report generation, dispatcher support, and data analysis within a single integrated system. This universality consolidates what would otherwise require multiple separate complex systems into one cohesive platform, managing complexity while delivering comprehensive automated capabilities.
Data Source
AI summary
A dispatcher virtual assistant (DVA) that can augment the capability of emergency dispatchers while reducing human errors. Major functions of the DVA include updating an emergency incident's status in real time, recommending or reminding the dispatcher to take proper actions at the right timing, answering the dispatcher's inquiries for task-related information, and fulfilling the dispatcher's request for an incident report. The DVA system includes a dispatcher language model based on machine-learning and deep-learning algorithms, for extracting the status of a live incident from incoming incident logs, and for processing and answering inquiries or requests from the dispatcher. It is customizable for different types of emergencies and for different local communities. The DVA can be used in tandem with an existing CAD system.