Conversational Intent Suggestion Interface for Customer Service Routing
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Solution Overview
Problem
Conventional customer service systems rely on manual routing of service requests, which is slow, error-prone, and inefficient, leading to high churn rates among personnel and dissatisfaction among customers due to misclassification of intents.
Innovation Solution
A conversational AI system that uses a user interface and machine learning models to help customers select their intent or purpose, with a dynamic intent tree structure that updates based on user feedback, allowing for accurate routing of requests to the appropriate handler.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual routing of service requests is used, then personnel can handle complex and ambiguous customer inquiries, but the process becomes slow and error-prone
Solution Approach 1:
The patent introduces an intent suggestion interface as an intermediary between the customer's request and the routing decision. This interface presents multiple possible intents to the customer, allowing them to select or confirm the correct intent, thereby improving routing accuracy while maintaining efficient automated processing
Solution Approach 2:
The system implements feedback by allowing customers to confirm or correct the suggested intent. This feedback loop enables the system to learn from customer corrections and improve future routing decisions, while also ensuring accurate routing in the current interaction
2Productivity
If automated routing with rule-based systems is used, then routing speed increases, but accuracy decreases due to ambiguity in user requests
Solution Approach 1:
Instead of attempting to fully automate the routing decision without customer input, the system takes a partial automation approach by suggesting intents based on automated analysis but requiring customer confirmation. This balances automated processing speed with customer-verified accuracy
Solution Approach 2:
The intent suggestion interface serves as an intermediary that bridges automated rule-based analysis and final routing decisions. It presents multiple possible interpretations of the customer's request and allows the customer to select the correct one, resolving ambiguities that rule-based systems cannot handle
3Reliability
If manual review of every service request is performed, then routing accuracy improves, but the process becomes tedious and increases staffing requirements
Solution Approach 1:
The system enables self-service routing by allowing customers to confirm or correct the suggested intent themselves. This eliminates the need for manual review of every request while maintaining high routing accuracy, as customers are best positioned to understand their own intent
Solution Approach 2:
By collecting feedback from customers on intent suggestions, the system creates a learning mechanism that improves automated routing over time. This feedback loop reduces the need for ongoing manual intervention and staffing while maintaining or improving accuracy
Data Source
AI summary
Systems, apparatuses, and methods for providing a more effective customer service support system. In some embodiments, the systems, apparatuses, and methods include a user interface (UI) and process flow for implementing a conversation-based customer service support system. The user interface and associated processing enable a user/customer to quickly and accurately select an intent, goal, or purpose of their customer service request. In some embodiments, this is achieved by a set of user interface displays or screens and an underlying logic that provide a user with multiple ways of selecting, identifying, or describing the intent, goal, or purpose of their request for service or assistance.