Dynamic Sequencing Platform for Contact Center Troubleshooting
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Solution Overview
Problem
Contact centers face inefficiencies in troubleshooting technical issues due to varying levels of experience among service agents, leading to inconsistent and time-consuming resolution processes that are difficult to adapt to rapidly evolving products and services.
Innovation Solution
A dynamic sequencing platform that utilizes machine learning, graph analytics, and deep learning models to analyze historical and real-time data, identifying optimal paths of actions for service agents to efficiently resolve technical issues, regardless of their experience level, and autonomously guides agents or consumers through troubleshooting processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If service agents rely on their own experience and judgment to troubleshoot issues, then they can handle complex situations flexibly, but less experienced agents produce inconsistent and slower resolutions
Solution Approach 1:
The patent introduces an intermediary system (the sequencing platform with machine learning models) that mediates between the service agent and the troubleshooting process. This platform analyzes historical data, determines optimal action sequences, and guides agents through troubleshooting steps, thereby ensuring consistent resolutions without requiring agents to manually manage complex decision trees.
Solution Approach 2:
The system enables self-service troubleshooting by automatically generating and guiding the troubleshooting sequence based on issue symptoms and historical data. The machine learning model autonomously determines the optimal action sequence without requiring human intervention in the decision-making process, allowing consistent application of best practices across all agents.
2Productivity
If service agents follow standardized troubleshooting guidelines, then resolutions become more consistent, but the process becomes slower and less adaptable to new issues
Solution Approach 1:
The patent implements dynamics by using machine learning models that continuously learn from new historical data to generate adaptive troubleshooting sequences. The system dynamically adjusts the recommended action sequences based on the specific issue symptoms, product information, and learned patterns from historical resolutions, enabling both speed and adaptability simultaneously.
Solution Approach 2:
The system changes parameters by transforming static troubleshooting guidelines into dynamic, data-driven action sequences. The machine learning model processes historical data to determine optimal sequences, and the system continuously updates its parameters based on new information, allowing the troubleshooting process to adapt to evolving products and services while maintaining high resolution speed.
3Reliability
If contact centers train more experienced agents to handle complex issues, then resolution quality improves, but training time and resource consumption increase
Solution Approach 1:
The patent enables self-service troubleshooting where the system automatically generates appropriate action sequences based on issue symptoms. This eliminates the need for agents to learn complex troubleshooting procedures through extensive training, as the system adapts to each situation and provides guided recommendations, thereby improving resolution quality without increasing training time.
Solution Approach 2:
The system replaces the mechanical process of training and experience accumulation with an automated machine learning-based sequencing platform. Instead of relying on agents' learned expertise, the system uses historical data and machine learning models to determine optimal troubleshooting sequences, substituting human knowledge with an automated intelligent system that provides consistent high-quality resolutions.
Data Source
AI summary
A device may receive historical data and real-time data associated with a troubleshooting service, identify, using a machine learning model, an optimal resolution based on the historical data and the real-time data, and identify, using a graph analytics model, an optimal path of actions based on the optimal resolution. The machine learning model may be trained to identify one of the set of historical issues associated with the unresolved issue, and identify the optimal resolution based on one of the set of historical resolutions associated with the one of the set of historical issues. The graph analytics model may be trained to generate a set of paths of actions based on the historical data, and identify the optimal path based on respective numbers of actions associated with the set of paths. The device may identify optimal action based on the optimal path and the prior action.