Technical Support Graph Augmentation for Issue Resolution
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Solution Overview
Problem
Customers often face difficulties in resolving computing system issues, leading to increased reliance on technical support, and existing systems lack efficient methods for providing real-time feedback to technical support personnel to effectively address recurring issues.
Innovation Solution
A technical support system that analyzes historical sessions to provide real-time feedback to technical support personnel, including duplicate question detection, question path graphs, and relevant historical session retrieval, to aid in resolving customer issues during technical support sessions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If technical support personnel manually analyze historical sessions and customer issues without automated assistance, then they can provide personalized support, but the time required to resolve issues increases and efficiency decreases
Solution Approach 1:
The system performs preliminary analysis of historical technical support sessions and customer issues before the actual support interaction. By pre-processing and indexing historical data, the system prepares relevant information in advance, enabling rapid retrieval and comparison during live support sessions without adding real-time processing delays.
Solution Approach 2:
The system implements a feedback mechanism where the analysis results from historical sessions are continuously fed back to improve future support interactions. The system learns from past resolutions and adjusts its recommendations, creating a closed-loop system that progressively improves efficiency while maintaining personalized support quality.
2Loss of information
If technical support systems store and analyze extensive historical session data, then better insights and patterns can be found, but system complexity and data processing requirements increase
Solution Approach 1:
The system extracts only the essential and relevant features from extensive historical session data, rather than processing the complete raw data. By identifying and extracting key patterns, common issues, and successful resolution strategies, the system maintains comprehensive historical analysis capabilities while reducing processing complexity to manageable levels.
3Reliability
If the system provides comprehensive real-time feedback during technical support sessions, then support quality improves, but the complexity of the support system increases
Solution Approach 1:
The system provides comprehensive real-time feedback by focusing on local, context-specific recommendations rather than attempting to analyze and respond to all possible aspects simultaneously. By identifying the specific issue at hand and providing targeted feedback relevant to that particular situation, the system maintains high support quality while keeping the immediate processing complexity manageable.
Data Source
AI summary
In general, embodiments relate to a method for managing a technical support session, comprising: determining a technical support issue (TSI) for a technical support session; identifying a question path graph (QPG) associated with the TSI; and displaying at least a portion of the QPG to a technical support person (TSP) during the technical support session.