eSIM Sensor Alerting for Real-Time Behavioral Deviation Detection
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Solution Overview
Problem
Existing systems fail to effectively utilize embedded Subscriber Identity Modules (eSIM) and sensor data for near real-time event detection and alerting, particularly for users with impairments, lacking personalized and timely responses based on user preferences.
Innovation Solution
A device with an embedded eSIM monitors near-real-time data using machine learning models to identify patterns, detect deviations, and initiate actions when thresholds are exceeded, providing alerts in user-specific modalities such as audible, haptic, or optical based on user preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models are used to monitor data in near real-time, then event detection accuracy is improved, but computational complexity and processing time increase
Solution Approach 1:
The system segments the data monitoring process into multiple stages: initial data filtering at the device level, pattern identification through machine learning models, deviation detection, and alert generation. This segmentation allows complex computations to be distributed and performed only when necessary, reducing overall computational complexity while maintaining high detection accuracy.
Solution Approach 2:
The system performs preliminary pattern identification and baseline establishment using machine learning models before actual event detection occurs. By pre-training models to recognize normal patterns and deviations, the system reduces real-time computational requirements while maintaining high accuracy in event detection.
2Speed
If near real-time data monitoring is implemented, then response time to events is improved, but energy consumption increases
Solution Approach 1:
The system implements periodic monitoring of data patterns rather than continuous intensive analysis. By monitoring for deviations from established patterns at scheduled intervals and only intensifying computation when deviations are detected, the system achieves near real-time response while significantly reducing overall energy consumption compared to continuous monitoring.
Solution Approach 2:
The system uses the device's existing eSIM and sensor capabilities to perform self-monitoring and self-alerting without requiring constant external power or computational resources. The embedded components serve themselves by automatically detecting patterns and generating alerts when deviations occur, reducing energy consumption while maintaining rapid response capability.
3Adaptability or versatility
If personalized alerts based on user preferences are provided, then user relevance and effectiveness are improved, but system complexity increases
Solution Approach 1:
The system applies local quality by customizing alert delivery methods according to individual user preferences and capabilities. Instead of a uniform alert system, the system adapts the type, timing, and delivery method of alerts to match specific user needs, such as providing haptic feedback to users with visual impairments or prioritizing certain types of events based on user preferences.
Solution Approach 2:
The system changes operational parameters dynamically based on user profile data. By adjusting alert parameters such as notification methods, timing intervals, and event thresholds according to stored user preferences, the system achieves personalized functionality without requiring fundamentally different system architecture, thus managing complexity through parameter variation rather than structural complexity.
Data Source
AI summary
Aspects herein capture methods, media, devices, and systems for a device having an embedded Subscriber Identity Module (eSIM), wherein the device can leverage sensors, networks, and machine learning capabilities to identify a behavioral pattern of a user of the device and to identify a deviation from that pattern based on a qualifying event. Based on identifying a deviation from that pattern, the device may initiate an action based on that deviation, and the action may include communicating an alert that the deviation might be associated with a hazardous condition or event. The device may alert a user of the hazardous condition or event by communicating the alert in a mode that is sensible to the user, based on the user preferences that define the accessibility setting that is specific to the physical characteristics of the user. Such modes may include audible, haptic, or optical presentations of the alert.