Semantic Clustering Interface for IVR Call Steering
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Solution Overview
Problem
Developing call steering applications for IVR systems is complex due to the numerous possible responses to open-ended questions, requiring manual association of semantic meaning, which is time-consuming and requires specialized expertise.
Innovation Solution
A user interface that automatically clusters user responses into semantically related groups, assigns preliminary tags, and allows developers to validate and edit these tags, with features for merging groups, checking inconsistencies, and applying semantic clustering to new responses, facilitating the development of call steering applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual association of semantic meaning is used for each response, then accuracy in understanding user intent is improved, but development time and complexity increase significantly
Solution Approach 1:
The system performs preliminary automatic clustering of responses into semantic groups before the developer needs to review them. This advance preparation reduces the time required for semantic association by having the groundwork ready beforehand, while the developer retains the ability to review and correct the pre-clustered groups to ensure accuracy.
Solution Approach 2:
Automatic semantic clustering algorithms serve as an intermediary between raw user responses and final semantic tagging. This intermediary layer groups similar responses together based on semantic similarity, reducing the number of individual responses that require manual review while maintaining accurate semantic categorization.
2Measurement precision
If manual semantic tagging of each response is performed, then semantic accuracy is improved, but the number of operations required increases
Solution Approach 1:
The system merges multiple similar responses into semantic groups based on their similarity. Instead of tagging each response individually, the clustering algorithm identifies and combines responses with the same or similar semantic meaning, allowing the developer to tag entire groups rather than individual responses, thereby significantly improving productivity while maintaining semantic accuracy.
Solution Approach 2:
Responses are pre-grouped into semantic clusters before the developer performs tagging operations. This preliminary organization reduces the total number of tagging operations required by grouping similar items together, allowing the developer to work with consolidated groups rather than individual responses.
3Measurement precision
If comprehensive review of all user responses is conducted, then semantic clustering accuracy is improved, but the complexity of the process increases
Solution Approach 1:
The large set of user responses is segmented into smaller, manageable semantic groups by the automatic clustering algorithm. This segmentation reduces process complexity by organizing responses into coherent clusters that can be reviewed and validated more easily, while still achieving comprehensive coverage of all responses through systematic grouping.
Data Source
AI summary
A system and method for providing an easy-to-use interface for verifying semantic tags in a steering application in order to generate a natural language grammar. The method includes obtaining user responses to open-ended steering questions, automatically grouping the user responses into groups based on their semantic meaning, and automatically assigning preliminary semantic tags to each of the groups. The user interface enables the user to validate the content of the groups to ensure that all responses within a group have the same semantic meaning and to add or edit semantic tags associated with the groups. The system and method may be applied to interactive voice response (IVR) systems, as well as customer service systems that can communicate with a user via a text or written interface.