Self-learning troubleshooter with dynamic solution ranking
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Solution Overview
Problem
Conventional troubleshooting systems lack the ability to replicate customer data and solutions across different systems, leading to repetitive processes and inefficiencies due to interface differences, and fail to autonomously gather data from multiple sources or update solution lists based on success rates.
Innovation Solution
A self-learning troubleshooting system that centralizes data across multiple systems, tracks customer interactions, and dynamically updates solution lists based on success rates, allowing for optimized solution ordering and prevention of redundant steps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is shared across different troubleshooting systems, then solution consistency is improved, but interface differences create data sharing complexity
Solution Approach 1:
The patent introduces a centralized data repository that acts as an intermediary between multiple troubleshooting systems. This repository stores customer data, device information, and troubleshooting history in a unified format, allowing different systems (IVR, website, mobile app) to access consistent data without direct interface complexity between them.
Solution Approach 2:
The system creates a universal data structure that serves multiple troubleshooting systems simultaneously. The centralized repository provides multi-functional access to various systems, enabling them to share data through a common interface rather than requiring system-specific integration protocols.
2Device complexity
If troubleshooting systems operate independently, then system simplicity is maintained, but repetitive troubleshooting steps occur
Solution Approach 1:
The patent merges the functionality of multiple independent troubleshooting systems into a unified architecture where data is centrally stored and shared. This combination allows systems to maintain operational independence while achieving data collaboration, preventing repetitive troubleshooting steps through shared history and context.
Solution Approach 2:
The system implements feedback mechanisms where troubleshooting actions taken in one system are recorded and fed back to other systems. This ensures that when a customer interacts with different troubleshooting channels, the system remembers previous actions and avoids repeating them, reducing overall troubleshooting time.
3Productivity
If solution lists are static, then system implementation is simple, but problem-solving efficiency decreases
Solution Approach 1:
The patent transforms static solution lists into dynamic, adaptive lists that automatically adjust based on customer interactions and troubleshooting outcomes. The system learns from resolved cases and updates solution recommendations in real-time, improving problem-solving efficiency without requiring complex manual reconfiguration.
Solution Approach 2:
The system performs self-learning by automatically analyzing troubleshooting outcomes and updating its own solution lists without human intervention. This self-service capability allows the system to optimize its problem-solving efficiency autonomously, eliminating the need for manual updates while maintaining simplicity in operation.
Data Source
AI summary
A method, system, and computer program product to troubleshoot a problem with a device are provided herein. According to the method, based on a symptom associated with the device, a first solution is transmitted to a first troubleshooting system. A second solution is transmitted to a second troubleshooting system while tracking the troubleshooting session from the first troubleshooting system. A list of solutions associated with the symptom is automatically updated based on percentage success rates for the solution.