Voice User Interface Design Clustering Natural Language Intent
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional voice user interface (VUI) design methods are cumbersome, inflexible, and limited in handling natural language utterances and large dialog prompts, often resulting in ambiguous and inefficient interactions.
Innovation Solution
A computer-implemented method and apparatus that identifies user intentions from interaction data, extracts features from natural language utterances, computes distance metrics to cluster similar intentions, and generates VUI design recommendations, enabling unambiguous and flexible VUI design capable of handling natural language and multiple dialog prompts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional VUI design methods are used, then design simplicity is maintained, but the VUI becomes ambiguous and inflexible in handling natural language
Solution Approach 1:
The patent replaces conventional manual VUI design methods with automated machine learning-based intent clustering. The system automatically identifies user intentions from interaction data, extracts features from natural language utterances, computes distance metrics, and generates clustered intent structures without requiring manual design intervention, thereby resolving the contradiction between design simplicity and natural language adaptability
Solution Approach 2:
The patent transforms VUI design from a static, rule-based approach to a dynamic, data-driven approach by changing the fundamental parameters: using machine learning models to process interaction data, computing distance metrics between utterances, and generating intent clusters based on similarity thresholds. This enables the VUI to adapt flexibly to natural language variations while maintaining clear, structured intent definitions
2Productivity
If conventional VUI design approaches are used, then design process is simple, but handling large number of dialog prompts becomes challenging
Solution Approach 1:
The patent segments the complex space of natural language utterances into distinct intent clusters based on computed distance metrics. By grouping similar utterances together and identifying representative intents for each cluster, the system reduces the complexity of handling large numbers of dialog prompts while improving productivity through automated intent classification and routing
Solution Approach 2:
The patent creates simplified intent cluster representations that copy and generalize from the full complexity of natural language interactions. By extracting key features from interaction data and generating condensed intent models, the system maintains efficient dialog prompt handling without requiring the full complexity of every possible user utterance to be explicitly programmed
3Reliability
If automated intent clustering is implemented, then VUI design clarity is improved, but computational complexity increases
Solution Approach 1:
The patent performs preliminary feature extraction from interaction data before conducting intent clustering. By pre-processing the data to extract relevant features from natural language utterances and storing them in a structured format, the system reduces the computational complexity of the subsequent clustering process while maintaining high intent identification accuracy through the use of pre-computed distance metrics
Data Source
AI summary
A computer implemented method and an apparatus for facilitating voice user interface (VUI) design are provided. The method comprises identifying a plurality of user intentions from user interaction data. The method further comprises associating each user intention with at least one feature from among a plurality of features. One or more features from among the plurality of features are extracted from natural language utterances associated with the user interaction data. Further, the method comprises computing a plurality of distance metrics corresponding to pairs of user intentions from among the plurality of user intentions. A distance metric is computed for each pair of user intentions from among the pairs of user intentions. Furthermore, the method comprises generating a plurality of clusters based on the plurality of distance metrics. Each cluster comprises a set of user intentions. The method further comprises provisioning a VUI design recommendation based on the plurality of clusters.