AI Chatbot Intent Detection for Customer Support Automation
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Solution Overview
Problem
Conventional customer support systems rely heavily on human agents, which leads to high training and labor costs, as well as delays in responding to customer queries. Additionally, these systems often struggle with categorizing and routing customer issues efficiently.
Innovation Solution
An autonomous AI chatbot equipped with a large language model is implemented to automatically handle incoming customer questions. The AI detects the intent behind each question and determines whether to trigger an automatic workflow or initiate an information retrieval process. An evaluation engine assesses the performance of the AI chatbot, including generating true solve rates based on determination of helpfulness and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If human agents are used to service customer support issues, then customer queries can be handled with human judgment and adaptability, but training and labor costs increase significantly
Solution Approach 1:
The system enables self-service through automated intent detection and workflow triggering. The AI chatbot autonomously analyzes customer queries, identifies intents, and executes appropriate workflows without human intervention, allowing the system to serve itself for routine customer support tasks.
Solution Approach 2:
The patent replaces the mechanical system of human agents with an AI-based automated system. The AI chatbot uses natural language processing and intent detection algorithms to substitute human judgment and adaptability with automated computational processes, eliminating training and labor costs while maintaining service capability.
2Measurement precision
If human experts manually label and route tickets, then accurate categorization and routing decisions can be made, but the process is resource intensive and causes delays
Solution Approach 1:
The system performs preliminary action by automatically detecting intents and routing tickets before human experts would normally process them. The AI chatbot analyzes incoming queries, identifies customer intents, and routes tickets to appropriate workflows or agents in advance, eliminating waiting time in queues while maintaining accurate categorization.
Solution Approach 2:
The patent replaces the manual mechanical process of human expert labeling and routing with an automated AI system. The intent detection mechanism uses natural language processing to automatically categorize and route tickets, substituting human judgment with computational analysis that is both accurate and instantaneous.
3Device complexity
If a small number of customer issue categories are used, then ticket routing is simpler and more manageable, but the system cannot effectively handle diverse and complex customer queries
Solution Approach 1:
The system applies segmentation by breaking down customer queries into distinct intent categories. The AI chatbot analyzes queries and segments them into specific intent types (e.g., information retrieval, workflow triggering, escalation), allowing for fine-grained categorization that handles diverse queries while maintaining manageable complexity through structured classification.
Solution Approach 2:
The patent adds another dimension to the categorization system by introducing intent detection as an intermediate layer between raw queries and traditional ticket routing. This dimensional addition allows the system to handle diverse and complex queries by analyzing semantic intent rather than relying solely on keyword-based categorical classification.
Data Source
AI summary
An AI chatbot responds to the intent of a customer question by triggering an automatic workflow appropriate for the intention of the question. An information retrieval pipeline may be initiated to response to question corresponding to an information request. A Large Language Model may be initiated to generate a workflow to respond to other types of questions. The Large Language Model is provided with policies, tools and prompts to implement workflows. An evaluation engine evaluates factualness and helpfulness of responses to information questions and workflow intent accuracy and workflow appropriateness. Overall conversation resolution verification ay also be performed.