M2M Emergency Alert System for Proactive Harm Detection
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Solution Overview
Problem
Current machine-to-machine communication systems lack the capability to proactively detect and respond to harmful activities or situations in real-time, such as emotional states of individuals or environmental hazards, which can lead to delayed or inadequate responses.
Innovation Solution
A system that utilizes machine-to-machine communication to determine the state of entities and environments, enabling the sending of alert messages based on calculated likelihoods of harmful activities, allowing for proactive responses, including alerts to security personnel, law enforcement, and automated actions like disabling vehicle functions or locking down areas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If machine-to-machine communication systems are used for monitoring entities and environments, then real-time detection capability is improved, but the system complexity increases
Solution Approach 1:
The system divides the monitoring function into separate components: detection devices (sensors, cameras) that collect data, communication modules that transmit data, and processing systems that analyze data and determine harmful activity likelihood. This segmentation allows each component to be optimized independently while working together for real-time detection.
Solution Approach 2:
The patent introduces an intermediary processing layer between data collection and response actions. This intermediary system receives raw data from detection devices, processes it through algorithms that calculate likelihood of harmful activities, and then triggers appropriate responses. This mediator simplifies the overall system architecture by centralizing the decision-making logic.
2Loss of time
If proactive alert systems are implemented to detect harmful activities, then response time is improved, but false alarms increase
Solution Approach 1:
The system dynamically adjusts detection parameters and alert thresholds based on contextual information and likelihood calculations. Instead of using fixed thresholds that cause false alarms, the system modifies its sensitivity and criteria based on the calculated probability of actual harmful activity, thereby reducing false positives while maintaining fast response times.
Solution Approach 2:
The system incorporates feedback mechanisms where the results of likelihood calculations and alert outcomes are fed back into the system to refine future detections. This feedback loop allows the system to learn from previous alerts and adjust its parameters to reduce false alarms while maintaining effective detection capability.
Data Source
AI summary
System, methods, and devices may provide alerts, such as emergency alerts, using machine-to-machine communications. A method may include receiving a state of a mammal associated with a device, receiving a state of an environment that is approximate to the location of the mammal associated with the device, determining a likelihood of a harmful activity based on the aforementioned states, and automatically sending alert message based on the determined likelihood of the harmful activity.