Adaptive Self-Executing Diagnostics for User Equipment
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Solution Overview
Problem
User equipment (UE) experiences faults and errors during runtime operations, leading to performance inefficiencies and diagnostic challenges due to unreported or under-reported issues, which are difficult to diagnose and troubleshoot, especially when transient problems occur without contextual data being obtained.
Innovation Solution
An adaptive self-executing diagnostics system on UE that monitors application and communication logs, establishes baselines, and creates custom device profiles to detect deviations, triggering data capture and transmission to a network analysis server for further diagnosis, allowing for dynamic threshold adjustments and improved resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional operating system error logs with defined intervals are used for diagnostics, then device complexity is reduced and ease of operation is maintained, but measurement precision and reliability of error detection deteriorate due to fixed intervals without regard to actual device usage patterns or network conditions
Solution Approach 1:
The diagnostic system dynamically adjusts logging intervals and thresholds based on real-time device usage patterns, network conditions, and application behavior. Instead of fixed intervals, the system monitors device state changes and adapts diagnostic parameters accordingly, allowing precise error detection while maintaining manageable complexity through adaptive rather than static configurations
Solution Approach 2:
The system automatically generates custom device profiles and diagnostic thresholds without requiring manual configuration. The diagnostic agent self-adjusts monitoring parameters based on observed usage patterns, eliminating the need for complex manual setup while maintaining high measurement precision through learned device-specific baselines
2Reliability
If comprehensive monitoring of all applications and network communications is implemented, then reliability of fault detection is improved, but use of energy and processor resources increases due to continuous logging and analysis
Solution Approach 1:
The system implements selective monitoring by establishing baseline behavior patterns for each application and network communication type. Instead of continuously monitoring all activities at full depth, the system logs only deviations from established baselines, achieving reliable fault detection while significantly reducing energy consumption by focusing computational resources on anomalous events rather than routine operations
Solution Approach 2:
The diagnostic system dynamically changes monitoring parameters such as logging interval, detection thresholds, and analysis depth based on device state. During normal operation, monitoring is reduced to conserve energy; when anomalies are detected or device performance degrades, the system intensifies monitoring to maintain high reliability while managing overall energy consumption through adaptive parameter adjustment
3Adaptability or versatility
If fixed diagnostic thresholds are used for all devices, then ease of manufacture and deployment is improved, but adaptability to different users, networks, and applications deteriorates
Solution Approach 1:
The system automatically generates customized device profiles by monitoring and learning each device's unique usage patterns, network characteristics, and application behavior. This self-configuration capability enables high adaptability to different users and environments while maintaining ease of deployment, as the system requires no manual customization and automatically adapts to each device's specific context
Solution Approach 2:
The system performs preliminary monitoring during an onboarding period to establish baseline behavior patterns before activating full diagnostic functionality. This preliminary action allows the system to pre-configure adaptive thresholds and profiles specific to each device, achieving both adaptability and deployment simplicity by automating the customization process before the device is fully operational
4Loss of information
If diagnostic data is logged continuously without selective capture, then completeness of diagnostic information is improved, but loss of time for data transmission and processing increases due to unnecessary data transfer
Solution Approach 1:
The system implements selective data capture by comparing current device state against established baselines. Instead of continuously logging all diagnostic data, the system captures and transmits only information representing deviations from normal operation, maintaining diagnostic completeness for actual issues while dramatically reducing data transmission time by filtering out routine, non-anomalous operations
Solution Approach 2:
The diagnostic system extracts and isolates only the relevant anomalous data from the full set of device operations. By identifying and extracting specific deviation events from the continuous data stream, the system maintains complete diagnostic information for actual problems while removing unnecessary routine data, thereby reducing transmission time without sacrificing diagnostic completeness
Data Source
AI summary
A method comprises a client executing on a processor of a UE, where the client is monitoring an application log and communication log for a plurality of user applications and communication applications. The client measures a volume of transactions and a set of performance metrics associated with UE resource usage. The client generates a custom device profile that establishes baselines for each of the plurality of user applications and identifies any deviations from the baselines. The client creates a bounded threshold based on the deviations, and detects that at least one of the plurality of user applications has exceeded the bounded threshold. The client determines that the bounded threshold was exceeded based on an application trigger and a network trigger, and in response, initiates a response action. The client also captures the communication log, the application log and application cache corresponding to the user application.