Emergency Data Manager for False Alarm Verification
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Solution Overview
Problem
Emergency networks face challenges in distinguishing between genuine and false alarms, leading to inefficient resource allocation and potential delays in responding to legitimate emergencies due to the high frequency of false alarms.
Innovation Solution
An emergency data manager system that processes alarm notifications by verifying alarms through sensor data and machine learning algorithms, assigning severity scores, and prioritizing alerts to reduce false alarm dispatches, utilizing an alarm signal processor and queue entry correlation logic to consolidate and prioritize emergency data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If alarm feeds are transmitted directly to emergency networks without verification, then response speed to alarms is improved, but the number of false alarm dispatches increases
Solution Approach 1:
The patent introduces an alarm verification system as an intermediary component between alarm feeds and emergency network dispatch. This system includes verification centers that receive alarm notifications, validate alarm authenticity through multiple criteria (geospatial data, sensor information, historical patterns), and only forward verified alarms to emergency networks. This intermediary layer enables selective filtering that reduces false alarm dispatches while maintaining rapid response to genuine emergencies through automated verification processes.
Solution Approach 2:
The system implements feedback mechanisms where verification centers continuously monitor alarm patterns, update verification algorithms based on historical data, and adjust dispatch priorities dynamically. The system learns from past verification outcomes and refines its decision-making models, creating a self-improving verification process that adapts to changing alarm characteristics and reduces false alarm rates over time while maintaining high response effectiveness.
2Reliability
If emergency responders manually verify each alarm, then false alarm rate is reduced, but resource allocation efficiency deteriorates
Solution Approach 1:
The verification system performs self-service through automated algorithms that independently evaluate alarm notifications without requiring manual intervention from emergency responders. The system uses machine learning models, geospatial analysis, and pattern recognition to automatically determine alarm validity, making the verification process self-executing and scalable. This automation eliminates the need for manual verification while maintaining high accuracy, thereby preserving resource allocation efficiency.
Solution Approach 2:
The patent replaces manual mechanical verification processes with automated electronic systems. Instead of human operators manually checking each alarm, the system uses computer-based verification centers that automatically process alarm data, cross-reference with sensor information and historical records, and make dispatch decisions through algorithmic decision-making. This substitution of mechanical manual verification with automated electronic processing maintains verification accuracy while dramatically improving resource allocation efficiency.
3Device complexity
If all alarms are treated equally, then system complexity is reduced, but response priority to critical emergencies is compromised
Solution Approach 1:
The verification system applies local quality differentiation by assigning varying levels of verification strictness and response priority to different alarm types based on their characteristics. Critical alarms (e.g., fire, medical emergencies) receive expedited verification and immediate dispatch priority, while less critical alarms undergo standard verification procedures. The system dynamically adjusts verification intensity and response urgency according to alarm severity, threat level, and geographic context, creating a differentiated response strategy that maintains manageable system complexity while ensuring appropriate emergency priority.
Data Source
AI summary
A disclosed method includes: receiving an alarm notification; determining that there is a verification for the alarm notification such that the alarm notification is not a false alarm; and pushing the alarm notification to an emergency network entity along with an indication that the alarm has been verified in response to determining that there is a verification. Another disclosed method includes monitoring an emergency queue at an emergency network entity for incoming queue entries from an emergency data manager; determining correlations for the incoming queue entries based on data associated with each incoming queue entry; and generating a link for each correlated queue entry linking each correlated queue entry to a primary queue entry in response to determining correlations.