Issue Detection Architecture Using Feedback Knowledge Base
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Solution Overview
Problem
Computing system manufacturers face challenges in detecting and resolving issues in deployed systems due to limited capture and utilization of implicit feedback and contextual information, leading to delayed recognition and resolution of functional and performance problems.
Innovation Solution
An issue detection and correction architecture that collects explicit and implicit feedback, diagnostic, and environmental data to generate a knowledge base, enabling proactive issue resolution by accessing and applying previously identified successful resolutions during runtime.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If manufacturers rely on customer feedback forums to capture issues, then some feedback is obtained, but the feedback is relatively limited and lacks contextual information
Solution Approach 1:
The system implements automated feedback collection by having the computing system automatically send notifications to manufacturers about detected issues, including contextual information such as environment data, configuration details, and diagnostic information. This creates a continuous feedback loop that captures both explicit user reports and implicit system-generated data, significantly expanding feedback coverage beyond manual forum submissions.
Solution Approach 2:
The computing system performs self-diagnosis and self-reporting by automatically detecting issues, gathering relevant contextual information, and sending notifications to the manufacturer without requiring user intervention. This self-service approach enables the system to capture comprehensive feedback including environmental context and configuration details that would otherwise be unavailable.
2Reliability
If manufacturers wait for user support requests to identify issues, then support resources can be allocated, but issue detection is delayed and resolution is slower
Solution Approach 1:
The system performs preliminary issue detection and analysis automatically before users need to contact support. The computing system continuously monitors its own operation, detects potential issues, and prepares diagnostic information in advance, enabling the manufacturer to receive pre-analyzed issue reports with contextual information already gathered, thus reducing both detection time and the need for extensive support investigation.
Solution Approach 2:
The system replaces manual support request processing with automated electronic notification and diagnosis. Instead of users manually describing issues through support forms or forums, the system automatically detects issues, gathers diagnostic data, and transmits structured information to the manufacturer, significantly accelerating issue detection and providing more accurate problem descriptions.
3Measurement precision
If comprehensive diagnostic and environmental data is collected, then issue resolution accuracy improves, but data processing complexity increases
Solution Approach 1:
The data collection system is segmented into specialized components that gather specific types of information: diagnostic data collectors, environment data collectors, configuration data collectors, and usage data collectors. Each component focuses on a specific data category, and the structured organization of these segmented data sources enables efficient processing and analysis without overwhelming system complexity.
Data Source
AI summary
Explicit and implicit feedback information, that is indicative of an issue in a deployed computing system, is collected. Information identifying attempted resolutions for the issue is collected as well. A knowledge base is generated that identifies issues and successful resolutions for those issues. During runtime, issues are detected, either explicitly or implicitly, and the knowledge base is accessed to determine whether a resolution has already been identified. If so, it can be proactively provided to the computing system to address the issue.