Intelligent Trouble Detection System for Proactive Mobile Device Issue Resolution
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Solution Overview
Problem
Users of mobile devices often face usability issues due to problematic applications, leading to increased contact with call centers for technical support, which can be inefficient with generic IVR systems.
Innovation Solution
An Intelligent Trouble Detection System (ITDS) that uses machine learning and AI to analyze device snapshots for trouble markers, proactively identifies issues, and sends alerts or notifications to users and call centers, allowing for preemptive action such as uninstalling problematic applications and bypassing generic IVR menus.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If generic IVR systems are used to handle user calls, then call center operations are simplified, but user experience deteriorates due to inefficiency and frustration
Solution Approach 1:
The system performs preliminary analysis of device snapshots and identifies trouble conditions before users call the call center. This advance detection enables the IVR system to prepare personalized menus and routing information in advance, so when users call, their specific issues are already known and prepared for resolution, eliminating the need for generic menu navigation
Solution Approach 2:
The system implements a feedback loop where device snapshots are continuously monitored, trouble conditions are detected, and this information is fed back to personalize the IVR experience. The IVR system receives real-time information about the user's device state and adjusts its behavior accordingly, creating a dynamic feedback-driven interaction that adapts to individual user needs
2Measurement precision
If device snapshots are continuously monitored for trouble markers, then trouble detection accuracy is improved, but system resource consumption increases
Solution Approach 1:
The system extracts only the essential trouble markers from device snapshots rather than analyzing all device data continuously. By identifying and monitoring only the specific parameters that indicate trouble conditions (such as application crash rates, battery anomalies, or network connectivity issues), the system achieves high detection accuracy while minimizing the computational resources required for analysis
Solution Approach 2:
The system applies partial monitoring by focusing on critical trouble markers rather than comprehensive continuous analysis of all device parameters. This selective approach captures sufficient information to detect troubles accurately while avoiding the excessive resource consumption that would result from analyzing every device parameter at all times
3Ease of operation
If proactive alerts are sent to users about trouble conditions, then user frustration is reduced, but communication overhead increases
Solution Approach 1:
The system extracts and communicates only the essential trouble condition information to users through alerts, rather than transmitting complete device diagnostic data. By sending concise, user-friendly notifications about specific issues detected (such as 'Your device is experiencing battery drain issues') rather than raw technical data, the system improves user experience while minimizing communication overhead and information loss
Data Source
AI summary
A system described herein may use automated techniques, such as machine learning techniques, to analyze device snapshots from a group of User Equipment (“UEs”), and determine trouble conditions that are experienced by the UEs. The system may identify markers of the trouble conditions based on the snapshots, and may use these markers to predict or identify trouble conditions at other UEs based on snapshots received from the other UEs. Further, once a trouble condition is predicted or identified at the other UEs, the trouble condition may be proactively addressed, without requiring an explicit request from the other UEs to address the trouble condition.