Query Tree Navigation via Keyword Analysis
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Solution Overview
Problem
Existing query tree systems in call centers and help desks often lead callers astray due to misunderstood questions or unrecognizable voice responses, causing frustration and inefficiency, especially when users are not familiar with the terminology or make mistakes in navigating the system.
Innovation Solution
A computer-implemented method that navigates a query tree by analyzing user responses to identify branching criteria and keywords, comparing them to associated keywords, and redirecting the user to the appropriate node, ensuring accurate navigation and providing relevant information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated query tree systems are used to reduce operational costs, then labor costs are reduced, but user frustration increases due to misunderstood questions and unrecognizable voice responses
Solution Approach 1:
The system continuously monitors user interactions and feedback signals to detect when a caller is confused or encountering difficulties. When feedback indicates user frustration or navigation errors, the system automatically adjusts its behavior by providing guidance, repeating questions, or offering alternative navigation paths, thereby reducing user frustration while maintaining automated operation
Solution Approach 2:
The system performs preliminary actions by anticipating potential user errors before they occur. It pre-provides guidance hints, validates user inputs in advance, and prepares alternative navigation paths so that when users make mistakes, the system can quickly correct them without increasing frustration or requiring human intervention
2Extent of automation
If voice recognition techniques are used for automated navigation, then automation level increases, but navigation accuracy decreases due to misunderstood or unrecognizable voice responses
Solution Approach 1:
The system introduces an intermediary validation layer between voice recognition and navigation decisions. This intermediary component analyzes voice recognition outputs, cross-references them with contextual information, and verifies accuracy before executing navigation changes. The intermediary acts as a buffer that filters out misrecognitions while preserving high automation levels
Solution Approach 2:
The system applies beforehand cushioning by implementing error tolerance mechanisms and recovery protocols that are activated only when voice recognition failures occur. It cushions against navigation errors by having pre-prepared alternative paths and guidance mechanisms that kick in automatically when recognition accuracy drops, maintaining both high automation and reliable navigation
3Reliability
If numerical responses with detailed explanations are used for navigation, then recognition accuracy improves, but the complexity of questions increases making them difficult for callers to remember and evaluate
Solution Approach 1:
The system segments the navigation process into multiple simple steps rather than presenting complex multi-option questions at once. It breaks down the decision tree into sequential binary choices or limited-set questions, each with clear and simple prompts. This segmentation maintains high recognition accuracy through clear options while improving ease of operation by reducing cognitive load on callers
Solution Approach 2:
The system dynamically adapts the complexity of questions based on real-time analysis of caller responses and navigation progress. When callers show confusion or take time to respond, the system automatically simplifies subsequent questions and provides additional guidance. This dynamic adjustment maintains recognition accuracy while continuously optimizing ease of operation for each individual caller
Data Source
AI summary
System, computer implemented method and computer program product for preparing and navigating a query tree including a plurality of query nodes and informational nodes. Each query node is associated with a prompt, branching criteria and keywords. A current query node provides a prompt to a user and a user response is received and analyzed to identify branching criteria and keywords from the user response. The method navigates to another node in the query tree in consideration of the branching criteria received in the user response and a comparison between the keywords received in the user response and the keywords associated with the query nodes. The comparison may validate navigation to a destination node corresponding to the branching criteria or the comparison may indicate incorrect navigation of the query tree. Corrective navigation can be implemented in various ways based upon the keywords received in the user response.