Automated Diagnostic Agent for Software Service Recovery
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Solution Overview
Problem
Hosted services face challenges in diagnosing and recovering from errors, especially with large user bases, as manual processes are inefficient and often require expert intervention, degrading user experience and increasing costs for service providers.
Innovation Solution
Implementing personalized diagnostics, troubleshooting, and notification systems that utilize telemetry data, user credentials, and system configuration data to automatically detect issues, perform recovery actions, and escalate unresolved problems to support departments, while collecting data for error pattern analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual diagnostic and recovery processes are used with expert help, then issue resolution accuracy is improved, but service scalability and response time deteriorate when user base is large
Solution Approach 1:
The system enables self-service through automated diagnostic agents that independently collect telemetry data, analyze issues, and execute recovery actions without requiring expert intervention. The diagnostic agent runs locally on user devices, autonomously gathering system state information and comparing it against known issue patterns in the issue database to resolve problems automatically.
Solution Approach 2:
The patent replaces the mechanical system of manual expert diagnosis with an automated computational system. The diagnostic agent uses algorithmic analysis of telemetry data and pattern matching against the issue database to substitute human expert workflows, enabling scalable automated troubleshooting that maintains resolution accuracy while handling large user bases.
2Measurement precision
If manual diagnostic processes are used, then comprehensive issue analysis is improved, but resource usage and operational costs increase
Solution Approach 1:
The system performs preliminary action by continuously collecting and pre-processing telemetry data in the background before issues occur. The diagnostic agent maintains a ready state with pre-configured analysis rules and issue patterns, enabling rapid automated diagnosis without requiring extensive manual investigation resources when problems arise.
Solution Approach 2:
The patent uses copying by creating standardized diagnostic templates and issue patterns that can be replicated across all user instances. Instead of performing unique comprehensive analysis for each manual case, the system copies and applies proven diagnostic workflows and recovery actions from the issue database, reducing operational costs while maintaining analysis quality through standardized procedures.
3Productivity
If automated diagnostic systems are implemented, then service scalability is improved, but system complexity increases
Solution Approach 1:
The system applies segmentation by dividing the diagnostic functionality into modular components: a lightweight diagnostic agent running on user devices, a separate issue database storing known problems and solutions, and an automated recovery execution module. This segmentation distributes system complexity across multiple independent elements, improving scalability while managing complexity through clear separation of concerns.
Solution Approach 2:
The patent introduces an intermediary diagnostic agent that mediates between the user device and the issue database. This intermediary component simplifies the overall system architecture by handling data collection, analysis, and recovery execution locally, reducing direct communication complexity between distributed users and central servers while maintaining scalable automated diagnostics.
Data Source
AI summary
Personalized diagnostics, troubleshooting, recovery, and notification based on application state is provided. In some examples, system, application, and device level configuration and usage data may be collected as telemetry data. Upon detection of a crash or similar problem, or upon user activation, an assistance service and/or a local assistance application component may execute diagnostics on the crashed application based on the telemetry data, user credentials, known problems, and other factors which may be recorded by the assistance application and/or other system elements. Suitable recovery actions may be taken. If recovery actions are unsuccessful, diagnostic information may be provided to a support system and the issue elevated. Moreover, collected information may be provided to a system database for generating data insights and determining error patterns.