Diagnostic Bot for Real-Time QoS Problem Detection
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Solution Overview
Problem
Existing electronic collaboration systems lack efficient methods for quickly recognizing, diagnosing, and correcting quality of service (QoS) problems in real-time, leading to user dissatisfaction due to manual analysis requirements, limited log file availability, and lack of automatic corrective actions.
Innovation Solution
A system and method utilizing a diagnostic bot that monitors textual messages in collaboration sessions to identify QoS issues, retrieves relevant system logs, and takes corrective actions automatically, such as adjusting resource allocation or suggesting user interventions, to improve communication quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis methods are used to diagnose QoS problems, then users can identify root causes, but the process is time-consuming and log files may be overwritten before analysis
Solution Approach 1:
The system automatically monitors chat messages, detects QoS problems, retrieves relevant log files, and diagnoses root causes without requiring manual user intervention. The collaboration client autonomously performs text analytics on chat messages, identifies problematic conditions, and presents diagnosed issues to users, eliminating the time loss associated with manual analysis and log retrieval.
2Loss of information
If additional debugging tools are enabled to capture QoS problems, then more diagnostic information is available, but system complexity and resource usage increase
Solution Approach 1:
The system introduces a diagnostic bot as an intermediary component that automatically monitors chat messages and retrieves diagnostic information. This bot acts as a mediator between users and the complex debugging infrastructure, automatically capturing relevant log files and diagnostic data without requiring users to manually enable or configure additional debugging tools, thus maintaining simplicity while ensuring information availability.
3Reliability
If QoS tagging is implemented to request sufficient network resources, then resource allocation improves, but QoS tags are not supported in all networks and no automatic corrective action is provided
Solution Approach 1:
The system implements a feedback mechanism by continuously monitoring chat messages for user reports of QoS problems and automatically responding with diagnosed issues and corrective actions. When degradation is detected through text analytics of chat communications, the system retrieves relevant log data, diagnoses the root cause, and presents actionable recommendations to users, creating a closed-loop feedback system that adapts to various network conditions without requiring QoS tag support.
Data Source
AI summary
System and method to respond to a streaming media link quality problem in a communication session, the method including: monitoring textual messages in the communication session for an indication of a quality of service (QoS) problem; forming a hypothesized cause of the QoS problem; retrieving evidence relevant to the hypothesized cause, the relevant evidence comprising system logs; and determining whether the retrieved evidence supports the hypothesized cause. The system includes: a processor coupled to a memory; a monitoring module configured to monitor textual messages in the communication session for an indication of a quality of service (QoS) problem; an inference module configured to form a hypothesized cause of the QoS problem; a retrieval module configured to retrieve evidence relevant to the hypothesized cause, the relevant evidence comprising system logs stored in the memory; and a calculation module configured to determine whether the retrieved evidence supports the hypothesized cause.