Automated Customer Support System Using NLP for IT Ticket Resolution
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Solution Overview
Problem
Existing customer support systems rely on manual processes, which are inefficient and time-consuming, especially in handling IT support tickets that require automated workflows and resolutions.
Innovation Solution
An automated customer support system that uses IT ticketing software, artificial intelligence (AI), and natural language processing (NLP) to extract initial states, goal states, and actions from natural language conversations, creating a formal planning model and producing executable plans to resolve IT support tickets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual processes are used for customer support, then the system is simple to implement, but the productivity and resolution speed are low
Solution Approach 1:
The system enables self-service through automated chatbots and AI agents that can independently handle customer inquiries, extract information from conversations, and resolve tickets without human intervention for routine matters. This increases productivity while the complexity is managed through modular AI components.
Solution Approach 2:
Manual mechanical processes of customer service agents are replaced with automated AI systems using natural language processing and machine learning. The AI agents process and respond to customer queries automatically, significantly increasing resolution speed while reducing the need for large human support teams.
2Productivity
If automated workflows are implemented, then the productivity increases, but the device complexity increases
Solution Approach 1:
The automated workflow system is segmented into distinct modular components: chatbot for initial interaction, NLP engine for information extraction, ticketing system for tracking, and resolution automation for execution. Each component handles a specific function, making the overall complex system manageable and maintainable while achieving high productivity.
3Measurement precision
If AI and NLP are used to extract information from conversations, then the measurement precision of customer issues improves, but the device complexity increases
Solution Approach 1:
An NLP engine acts as an intermediary between the chatbot conversation and the ticketing system. It processes natural language inputs, extracts key information and intent, and structures data for the ticketing system. This intermediary layer improves issue identification accuracy while isolating the complexity of NLP processing from the core ticketing workflow.
Data Source
AI summary
In an approach for building an automated customer support system, a processor receives a set of sentences extracted from a natural language conversation occurring between an IT support system and a user. A processor extracts an initial state and a goal state from the set of sentences using a Natural Language Classifier. A processor extracts one or more actions from the set of sentences. A processor creates a formal planning model. A processor determines the one or more formal actions are not complete using a first machine learning model. A processor completes the one or more formal actions with one or more missing parts. A processor produces an executable plan using a planner. A processor implements one or more executable scripts according to a sequence of the one or more formal actions of the executable plan using a plan executor.