Image-Based Detection of Mobile Modem Crashes
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Solution Overview
Problem
Detecting silent crashes of communications processors in mobile devices is challenging due to their temporary nature and lack of accessible data, affecting device performance and reliability, especially with the increasing complexity of 5G devices.
Innovation Solution
A mechanism using a trained image processing model analyzes signal strength indicators, such as signal strength icon images, to predict communications processor crashes, allowing for detection and recording of state data for anonymization and aggregation to improve crash detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional monitoring methods are used to detect communications processor crashes, then the detection system is simple, but the detection capability is insufficient due to the temporary nature of silent crashes and lack of accessible data
Solution Approach 1:
The patent introduces an image processing model as an intermediary between the communications processor and the detection system. This model analyzes signal strength indicator images to indirectly detect processor crashes, enabling detection without direct access to processor state data during crashes
Solution Approach 2:
The patent replaces traditional mechanical monitoring methods (direct processor state monitoring) with an optical-based approach (image analysis of signal strength indicators). This substitution enables detection of silent crashes through visual signal analysis rather than direct processor interrogation
2Reliability
If comprehensive testing is performed to limit communications processor crashes, then crash incidence is reduced, but testing complexity and time requirements increase significantly
Solution Approach 1:
The patent enables the device to self-diagnose communications processor crashes through automated image analysis. The image processing model continuously monitors signal strength indicators and automatically detects crashes without requiring external testing infrastructure or complex test procedures
Solution Approach 2:
The patent implements a feedback mechanism where the image processing model continuously analyzes signal strength indicators and provides real-time detection of processor crashes. This ongoing feedback enables reliable crash detection without requiring exhaustive prior testing
3Measurement precision
If direct access to communications processor state data is used for detection, then detection accuracy is high, but external devices cannot access this data during crashes
Solution Approach 1:
The image processing model serves as an intermediary that accesses publicly visible signal strength indicators (which external devices can observe) and translates them into crash detection information, bridging the gap between inaccessible processor state data and external detection capabilities
Data Source
AI summary
To detect “silent” crashes of communications processors in a mobile device, the mobile device analyzes signal strength icon images using a trained image processing model. During operation of a mobile device, the device captures images of a signal strength icon that is displayed by the device and that visually represents a telecommunications signal strength detected by the mobile device. The mobile device applies the captured images to the trained image processing model, which is configured to output a prediction of whether the communications processor in the mobile device crashed.