Alarm Event Data Processing for AI-Assisted False-Alarm Filtering
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Solution Overview
Problem
Existing emergency notification systems, particularly 9-1-1 systems, face inefficiencies in accurately identifying and dispatching first responders due to reliance on outdated landline technology and limited contextual information, leading to potential false positives and delayed responses.
Innovation Solution
A case management server integrates with residential devices like smart home security cameras to analyze multimedia data feeds, using facial recognition and calendar data to confirm authorized individuals, and provides a rapid-response interface for PSAPs, allowing human operators to verify and expedite emergency responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional 9-1-1 systems are used with landline technology, then system simplicity is maintained, but response accuracy and speed deteriorate due to limited contextual information
Solution Approach 1:
The patent combines multiple data sources including call data, geolocation data, motion data, imaging system data, and smart home system data into a unified emergency notification system. This integration allows the system to process comprehensive contextual information from diverse sources to accurately identify and respond to emergencies, resolving the contradiction between improved accuracy and system complexity by systematically merging previously separate systems.
Solution Approach 2:
The system is designed to handle multiple types of emergencies and process various data formats from different sources through a unified platform. The case management server can process alarm data records from multiple sources including smart home systems, mobile devices, and traditional alarm systems, making the system universally applicable across different emergency scenarios while maintaining consistent processing accuracy.
2Productivity
If AI systems are deployed for automated threat detection, then processing speed is improved, but classification accuracy worsens due to lack of contextual information
Solution Approach 1:
The system incorporates feedback loops where alarm data records are processed, analyzed, and used to refine future threat detection. The case management server continuously receives alarm data, processes it through the workflow, and uses the results to improve subsequent classifications. This feedback mechanism allows the system to maintain high processing speed while improving classification accuracy over time through learned patterns from processed data.
Solution Approach 2:
The system performs preliminary processing and filtering of alarm data records before they reach the final classification stage. The case management server pre-processes incoming data, organizing it into structured formats and extracting key features, which prepares the data for faster and more accurate AI classification. This preliminary action reduces the computational burden on the classification algorithms while maintaining high accuracy.
3Reliability
If confirmation processes are implemented to avoid false positives, then response accuracy is improved, but response time deteriorates
Solution Approach 1:
The system implements selective confirmation processes rather than requiring full confirmation for all alarms. The case management server analyzes alarm data records and determines the level of confirmation needed based on the nature and severity of the detected threat. For high-confidence detections with clear contextual evidence, the system can proceed with minimal or no confirmation, while lower-confidence alarms receive appropriate verification. This partial action approach maintains reliability by reducing false positives without unnecessarily delaying critical emergency responses.
Data Source
AI summary
Improved systems and methods for providing a notification of an emergent condition using automation, artificial intelligence, visual recognition, and other logic to automatically suggest identifications and classifications of information in audiovisual or other multimedia data about an emergency or alarm and modify a rapid-response display and/or alarm handling workflow to expedite the dispatch of first responds to true emergencies and quickly filter and eliminate false alarms to reduce waste.