Classifier Voice Interface for Dynamic Query Routing
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Solution Overview
Problem
Conventional voice-based user interfaces are limited in addressing information-seeking needs of users without well-defined preferences, as they rely on fixed sequences of questions and require costly training data, failing to provide dynamic responses to exploratory queries.
Innovation Solution
A classifier voice interface system that parses user queries to select appropriate domain-specific voice interfaces based on attributes, applying scoring functions to generate utility scores for query response templates and audibly outputting responses, allowing for dynamic interaction and efficient information retrieval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional spoken dialogue systems ask a user a series of fixed questions in a fixed order to narrow a field of possible answers, then the system can systematically gather information to provide answers, but the interaction fails to address the information seeking needs of users who do not have well-defined preferences or who wish to explore the space of possibilities
Solution Approach 1:
The patent implements dynamic dialogue by allowing the system to adapt its questioning strategy based on user responses and detected preferences. Instead of following a fixed question sequence, the system dynamically adjusts which questions to ask next based on the user's indicated preferences and the space of possibilities being explored, enabling flexibility while managing complexity through adaptive control.
Solution Approach 2:
The system changes dialogue parameters such as question selection, information gathering depth, and response generation based on detected user preferences. By monitoring user interactions and adjusting dialogue parameters in real-time, the system adapts to different user information-seeking behaviors without requiring completely different system architectures for each user type.
2Extent of automation
If conventional algorithms model dialogue as a Markov Decision Process and optimize the model via reinforcement learning, then the system can learn from human-machine interactions, but the algorithms rely on complex and costly training data derived from large numbers of human-machine interactions or simulations
Solution Approach 1:
The patent incorporates pre-defined preference detection mechanisms and dialogue strategies that are prepared in advance rather than requiring extensive runtime learning. By pre-configuring the system with preference detection capabilities and dialogue management rules, the system achieves automatic adaptation without needing large quantities of training data collected during operation.
Solution Approach 2:
The system uses lightweight, computationally efficient preference detection algorithms that do not require heavy machine learning models trained on massive datasets. By employing simpler, more economical processing methods for real-time preference detection and dialogue management, the system achieves automation with reduced computational resources and data requirements.
Data Source
AI summary
A classifier voice interface of a user terminal may receive a query, may parse the query to identify an attribute, and may process the query to select a first domain-specific voice interface of a plurality of domain-specific voice interfaces based on the attribute, wherein each of the domain-specific voice interfaces comprises specialized information to process queries of different types. The classifier voice interface may further instruct the first domain-specific voice interface to process the query.