Inter-Agency Incident Response Recommendation System
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Solution Overview
Problem
Multi-agency public safety responses are hindered by the limitations of training, as it is impossible to train for every possible incident type, and individual agencies may not be aware of each other's standard operating procedures, leading to potential disarray when deviations occur from expected protocols.
Innovation Solution
A system equipped with the standard operating procedures of all involved agencies, using AI and machine learning to provide real-time, contextually relevant recommendations to responders, monitoring for deviations, and adjusting recommendations based on historical responses to ensure effective inter-agency collaboration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If cross-agency training is conducted to improve collaboration, then inter-agency coordination is improved, but it is impossible to train for every possible incident type
Solution Approach 1:
The system pre-loads standard operating procedures for multiple agencies into the machine learning model before incidents occur. This preliminary preparation allows the system to provide appropriate recommendations during actual incidents without requiring exhaustive training for every possible scenario.
Solution Approach 2:
The system continuously learns from historical incident responses and feedback data, improving its recommendations over time. This feedback mechanism enables the system to adapt to new incident types and refine its coordination strategies without requiring explicit retraining for each scenario.
2Productivity
If individual agencies maintain their own standard operating procedures, then each agency operates efficiently according to its protocols, but agencies are not aware of each other's procedures leading to potential disarray
Solution Approach 1:
The system combines standard operating procedures from multiple different agencies into a single integrated machine learning model. This merging allows the system to understand and coordinate across agency boundaries while each agency maintains its own procedural integrity.
Solution Approach 2:
The machine learning model serves as a universal platform that handles multiple agencies' procedures simultaneously. It provides contextually relevant recommendations that adapt to which agencies are responding to which incidents, making the system multi-functional across different agency combinations.
3Reliability
If real-time recommendations are provided to responders during incidents, then response effectiveness is improved, but system complexity increases
Solution Approach 1:
The machine learning model autonomously processes incident data and generates recommendations without requiring complex human-in-the-loop approval systems. The system serves itself by automatically learning from historical data and making real-time decisions, reducing the need for additional control infrastructure.
Data Source
AI summary
Techniques for inter-agency recommended course of action are provided. An indication of an incident requiring a response from a first and a second public safety agency is received. An expected incident response based on standard operating procedures of the first and the second public safety agency is retrieved from a machine learning engine. A deviation from the expected incident response attributable to the second public safety agency is identified. A recommended course of action for the first public safety agency is retrieved from the machine learning engine. The recommended course of action based at least in part on historical incident responses. The recommended course of action is sent to the first public safety agency. Feedback related to the incident that includes when the recommended course of action was accepted and an incident outcome is received. The machine learning engine is updated based on the feedback.