Emergency Response Assistant for Multi-Source Incident Correlation
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Solution Overview
Problem
Existing emergency response systems fail to integrate and correlate various data sources effectively, leading to delays in communication and response times due to the isolation of 911 calls from building sensor data, resulting in incomplete emergency data and inefficient resource allocation.
Innovation Solution
An AI-powered emergency response digital assistant integrates smart device data with emergency response systems, leveraging federated learning and generative AI to aggregate and analyze multiple data sources, providing real-time insights and automated response suggestions to emergency management personnel.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If emergency response systems maintain separate data sources for 911 calls and building sensor data, then system simplicity is preserved, but response time and detection accuracy deteriorate due to incomplete emergency data
Solution Approach 1:
The patent merges 911 call data with building sensor data (temperature, smoke, carbon monoxide levels) into a unified emergency response system. The system combines information from multiple sources including emergency calls, building sensors, and social media to create a comprehensive view of emergency situations, enabling faster and more accurate response decisions.
Solution Approach 2:
The emergency response system is designed with multi-functionality to handle various data types and sources. It can process 911 calls, monitor building sensors, analyze social media, and correlate different data streams to provide comprehensive emergency response support, making the system adaptable to diverse emergency scenarios.
2Measurement precision
If emergency response systems integrate multiple data sources, then detection accuracy and response efficiency improve, but data processing complexity and system requirements worsen
Solution Approach 1:
The patent introduces an intermediary processing layer that correlates and contextualizes different data sources. This intermediary system processes and relates 911 call information with building sensor data and social media content, making the complex multi-source data manageable and actionable for emergency responders through structured analysis and presentation.
3Productivity
If real-time analysis of multiple data sources is performed, then emergency response efficiency improves, but computational resources and processing time requirements worsen
Solution Approach 1:
The system performs preliminary actions by pre-processing and contextualizing data from multiple sources before emergency events occur. Building sensor data is continuously monitored and stored, and emergency response protocols are pre-configured, enabling rapid analysis and response when actual emergencies occur without requiring intensive real-time computational resources for all data processing.
Data Source
AI summary
An emergency response assistant system may generate emergency response insights and provide the insights to an emergency responder application for an emergency communications center (ECC), an operations center, or a first responder. The emergency response assistant system may include a data retrieval system and an augmentation and generation system. The data retrieval system may be configured to transform emergency response procedures into vectors that are stored in a vector database. The augmentation and generation system may be configured to perform a vector search of the vector database with an AI model using context from call (e.g., a 911 call) characteristics and external data sources and may be configured to display the results on a user interface of an emergency management application to provide emergency response procedure suggestions to a user of an ECC computing system, an operations center computing system, or a first responder computing device.