Home Appliance Feature Mapping for Personalized Voice Commands
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Solution Overview
Problem
Natural language commands for home appliances vary among users, making it difficult to accurately interpret and translate them into actionable commands, particularly due to differences in past command input habits and usage environments.
Innovation Solution
A method and device that utilize a server or home appliance to generate feature sets and accuracies through a preprocessing module, theme module, and personalization module to accurately reflect user intent and preferences, incorporating generative artificial intelligence (AI) to process voice commands and adjust features such as color, speech, and function sets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If natural language commands are used for controlling home appliances, then ease of operation is improved, but measurement precision of user intent deteriorates due to variations in user speech patterns and command formulations
Solution Approach 1:
The system performs preliminary actions by collecting and analyzing user command history before processing new commands. The command history analysis module pre-processes stored commands to identify user-specific patterns, enabling more accurate interpretation of future natural language inputs while maintaining ease of operation.
Solution Approach 2:
The system implements feedback mechanisms where the interpretation accuracy of natural language commands is continuously improved based on user corrections and command history. The system learns from past interactions and adjusts its command mapping, thereby enhancing measurement precision of user intent over time while preserving the natural interaction mode.
2Measurement precision
If multiple processing modules are used to improve command interpretation accuracy, then measurement precision is improved, but device complexity increases due to additional modules for command history analysis and personalized mapping
Solution Approach 1:
The system merges multiple processing functions into an integrated command processing framework. The command history analysis module, personalized mapping module, and theme module work together as a unified system rather than separate independent components, reducing overall device complexity while maintaining high measurement precision through collaborative processing.
Solution Approach 2:
The command processing system is designed with universal modules that can handle multiple types of commands and adapt to different users. The personalized mapping module and command history analysis module serve multiple functions including pattern recognition, intent classification, and command translation, thereby reducing the need for separate specialized components and lowering device complexity.
Data Source
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AI summary
The present invention includes a first operation of generating, by a server or a home appliance, a first feature set and a first accuracy corresponding to a feature command, a second operation of generating, by the server or the home appliance, a second feature set and a second accuracy corresponding to the feature command using a theme module disposed in at least one of the server, the home appliance, or an external server when the first accuracy is a reference value or less, a third operation of generating, the server or the home appliance, a third feature set using personalized information corresponding to device information of the home appliance and the feature command when the second accuracy is the reference value or less, and a fourth operation of changing any one of the first feature set, the second feature set, or the third feature set to a feature of the home appliance.