Intelligent Support Framework Using ML for Resolution
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current assisted support channels in enterprises rely on generic and static tools, leading to inefficient and inaccurate issue resolution due to varying agent skill levels, resulting in increased time and costs, and reduced customer satisfaction.
Innovation Solution
Implementing a machine learning-based intelligent support framework that trains models on historical support case data to analyze and recommend resolutions, and suggests alternate elements if needed parts are unavailable, thereby standardizing support across agents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If generic and static support tools are used, then device complexity is reduced, but resolution accuracy and productivity deteriorate due to varying agent skill levels
Solution Approach 1:
The patent replaces manual troubleshooting processes with an automated machine learning-based resolution recommendation system. The system uses trained models to analyze support case data and generate resolution recommendations, substituting the mechanical process of human agent analysis with an automated intelligent system that consistently delivers accurate recommendations regardless of agent skill level.
Solution Approach 2:
The patent introduces a resolution recommendation system as an intermediary between the support case data and the agent. This intermediary processes the case information, applies trained machine learning models, and provides structured resolution recommendations, thereby standardizing the support process and reducing dependency on individual agent expertise.
2Loss of time
If manual troubleshooting is used, then device complexity is minimized, but loss of time increases due to varying agent expertise levels
Solution Approach 1:
The patent applies preliminary action by training machine learning models in advance using historical support case data. These pre-trained models are then deployed to quickly analyze new support cases and provide resolution recommendations, eliminating the need for agents to manually troubleshoot from scratch and significantly reducing resolution time.
Solution Approach 2:
The patent replaces the manual mechanical process of human troubleshooting with an automated machine learning system that rapidly analyzes case data and provides recommendations, thereby reducing the time loss associated with varying agent expertise levels.
3Reliability
If static support tools are used, then ease of operation is maintained, but reliability of resolution deteriorates due to lack of adaptive intelligence
Solution Approach 1:
The patent introduces dynamics by implementing machine learning models that can adapt and learn from historical support case data. The system evolves its resolution recommendations based on patterns learned from training data, making the support framework dynamic and intelligent rather than static, thereby improving resolution accuracy and reliability.
Solution Approach 2:
The patent incorporates feedback mechanisms where the machine learning models are trained on historical support case data including resolution outcomes. This feedback loop allows the system to learn from past performance and continuously improve its resolution recommendations, enhancing reliability through data-driven insights.
Data Source
AI summary
A method comprises training at least one machine learning model with training data from a plurality of support cases, and receiving an input comprising data associated with at least one support case. The input is analyzed using the at least one machine learning model to determine one or more resolution options for the at least one support case, and the one or more resolution options are transmitted to an agent.