AI Intent Detection Workflow for Customer Support Emails
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Solution Overview
Problem
Conventional customer support systems face inefficiencies due to the need for extensive human agent training, labor costs, and delays in responding to customer queries, particularly when dealing with complex or non-routine issues, as they rely on macros and templates that are not always effective and require manual searches through institutional knowledge.
Innovation Solution
The implementation of a system that uses a granular taxonomy to detect the intent of freeform customer support emails, employing AI and machine learning to map summaries to topics, and select interactive workflows, including slot-filling and API calls, to automate responses and route tickets efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If human agents manually search institutional knowledge to respond to customer queries, then they can access comprehensive information, but response time increases and productivity decreases
Solution Approach 1:
The patent replaces the mechanical manual search process with an automated AI system that uses natural language processing and machine learning to retrieve relevant institutional knowledge. The system automatically queries knowledge bases, searches documentation, and retrieves relevant information without human intervention, thus maintaining comprehensive information access while dramatically improving response time.
Solution Approach 2:
The system enables self-service by allowing the AI agent to independently search and retrieve institutional knowledge without requiring human agents to manually query databases or documentation. The automated system serves itself by autonomously accessing and synthesizing information from multiple internal and external knowledge sources.
2Reliability
If human experts manually label and route tickets, then routing accuracy improves, but labor costs increase and processing speed decreases
Solution Approach 1:
The patent replaces manual human expert labeling and routing with an automated AI system that uses natural language processing to analyze ticket content, determine intent, and route tickets to appropriate teams or agents. The system achieves high routing accuracy by understanding semantic meaning and contextual nuances, while processing tickets in seconds rather than requiring human expert intervention.
Solution Approach 2:
The system performs preliminary classification and routing decisions automatically before human agents need to review tickets. By pre-processing tickets with AI intent detection and automated routing, the system prepares tickets in advance, reducing the time human experts need to spend on labeling and routing while maintaining or improving accuracy.
3Device complexity
If a small number of ticket categories are used, then device complexity is reduced, but adaptability to handle diverse customer issues decreases
Solution Approach 1:
The patent implements a dynamic category system where the AI can automatically create, modify, and adapt ticket categories based on learned patterns from historical data and emerging customer issues. Rather than using a fixed small number of categories, the system dynamically expands and refines its classification structure, allowing it to handle diverse issues while maintaining manageable complexity through intelligent organization.
Solution Approach 2:
The system segments ticket classification into multiple hierarchical levels and dimensions, allowing diverse customer issues to be categorized along different axes (e.g., product type, issue type, priority, team). This multi-dimensional segmentation enables the system to handle a wide variety of issues without requiring an excessively large flat number of categories, as tickets can be routed based on combinations of category attributes.
4Reliability
If extensive training is provided to human agents, then service quality improves, but training costs and time investment increase
Solution Approach 1:
The patent replaces extensively trained human agents with an AI system that achieves high service quality through machine learning and natural language processing capabilities. The AI system is trained on historical data and institutional knowledge, encoding service quality standards directly into its algorithms, thereby eliminating the need for ongoing human training while maintaining or improving service quality consistency.
Solution Approach 2:
The system copies and encodes expert knowledge and service quality standards into its training data and algorithms. Rather than requiring human agents to undergo extensive training to acquire this knowledge, the AI system is provided with comprehensive training datasets that capture best practices, institutional knowledge, and service quality requirements, allowing it to perform at expert levels without human training overhead.
Data Source
AI summary
A system and method are disclosed for detecting the intent of a freeform customer support email question and performing a workflow to respond to the customer support email. In one implementation, a granular taxonomy of topics is generated and intent detection includes mapping a summary of the email to a topic in the taxonomy. An interactive workflow may be selected based on the topic of the email. The interactive workflow may include slot-filling using generative artificial intelligence. The interactive workflow may include API calls.