User-Initiated Error Reporting in Mobile Communication Systems
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Solution Overview
Problem
Current network monitoring solutions fail to detect intermittent or dispersed problems, 'soft events,' and device-specific issues in communication networks, leading to inefficient problem resolution and user frustration, as they rely on automated detection and user-initiated reporting that may be delayed and lacking in contextual data.
Innovation Solution
Implementing user-initiated data collection triggers on communication devices, allowing users to collect and upload data related to network and device states when issues occur, along with optional annotations, to provide a more comprehensive analysis of system errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If automated detection systems are used to monitor network errors, then systematic failures can be detected, but intermittent or dispersed problems and soft events cannot be detected
Solution Approach 1:
The monitoring system is segmented into multiple components: automated detection agents deployed on individual devices, centralized collection servers, and analysis systems. This segmentation allows the system to detect both systematic failures (through centralized monitoring) and device-specific intermittent problems (through distributed agents), resolving the contradiction between reliability and adaptability
Solution Approach 2:
Detection agents serve as intermediaries between the network infrastructure and the centralized monitoring system. These agents collect local device state information and transmit it to collection servers, enabling the system to detect soft events and intermittent problems that would otherwise be invisible to automated monitoring, thus expanding detection coverage while maintaining systematic failure detection
2Reliability
If pre-programmed service monitors are deployed to emulate real-world transactions, then systematic failures can be caught, but intermittent problems and device-specific issues cannot be detected
Solution Approach 1:
The detection agents on individual devices perform self-service monitoring by automatically collecting device state information, application performance metrics, and error logs. This eliminates the need for manual probing by care agents and enables continuous monitoring of device-specific issues, making intermittent problems detectable while maintaining systematic failure detection capabilities
Solution Approach 2:
Detection agents continuously collect and buffer device state information before problems occur. When an error or intermittent issue arises, the pre-collected data is immediately available for analysis, eliminating the time lag associated with manual probing and enabling timely detection of intermittent problems
3Loss of information
If customer care calls with data probing are used to collect diagnostic information, then some events can be identified, but users experience delays and data may be outdated by the time analysis occurs
Solution Approach 1:
Detection agents continuously monitor and collect device state information in the background without interrupting user activities. This continuous data collection ensures that when problems occur, up-to-date contextual information is already available, eliminating the time loss associated with manual data probing during care calls while maintaining comprehensive information collection
4Ease of operation
If manual probing by care agents is performed to diagnose user-reported issues, then contextual data can be collected, but users must wait in queues and provide detailed problem descriptions
Solution Approach 1:
The system enables self-service diagnostics by automatically collecting device state information, application logs, and network configuration data without requiring user intervention. Users simply report the problem, and the detection agent autonomously gathers all necessary contextual data, eliminating waiting times while maintaining ease of operation
Solution Approach 2:
The detection agent continuously pre-collects device state information and buffers it for rapid retrieval when problems occur. This preliminary data collection eliminates the need for real-time manual probing during care calls, reducing waiting time while maintaining comprehensive data collection capabilities
Data Source
AI summary
Systems and methods that automatically collect data associated with system-identified errors as well as data associated with events associated with user-initiated actions. A data collection profile defines data to be collected and a user-initiated trigger. When the user-initiated trigger is sensed, data is collected according to the data collection profile. The collected data can be uploaded immediately, or stored for some period of time before being transmitted to a collection system. A user recognizes an event which may not be recognizable by the system and the user provides an input defined as the user-initiated trigger. Data may be collected for a brief time before, during and a brief time after sensing the user-initiated trigger and may be uploaded to a system. The user may annotate the collected data by explaining the error, after which the explanation is correlated with the collected data.