Voice Command Context Learning System
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Solution Overview
Problem
Conventional voice command systems face difficulties in restricting voice commands based on user situations, requiring complex execution condition definitions and corrections, which can lead to erroneous recognition and increased command complexity.
Innovation Solution
A learning system that obtains information around the user and learns it as a condition for executing voice commands, allowing for dynamic execution condition learning and elimination of pre-defined conditions, enabling voice commands to be restricted based on user contexts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If execution conditions are defined in advance for voice commands, then voice command accuracy is improved, but system complexity and operation burden increase
Solution Approach 1:
The system automatically learns and acquires execution conditions by observing user behavior patterns and contextual information, eliminating the need for manual definition of execution conditions. The voice command system self-adjusts and self-optimizes based on observed data, reducing operational burden while maintaining accuracy.
Solution Approach 2:
The system continuously observes user interactions and feedback to refine execution conditions. By monitoring actual usage patterns and contextual data, the system iteratively improves its understanding of when voice commands should be executed, achieving high accuracy without requiring pre-defined complex conditions.
2Adaptability or versatility
If multiple execution conditions are defined for a single voice command, then adaptability to different situations is improved, but ease of operation deteriorates
Solution Approach 1:
The system automatically adapts to multiple situations by observing user behavior patterns and contextual information, eliminating the need for users to manually define multiple execution conditions. The system self-adjusts its execution conditions based on observed data, maintaining versatility while simplifying operation.
Solution Approach 2:
The system dynamically changes execution condition parameters based on observed user behavior and contextual data. Instead of requiring static pre-defined conditions, the system adapts its parameters in real-time based on learning from usage patterns, achieving versatility without increasing operational complexity.
3Measurement precision
If execution conditions are manually defined and corrected, then voice command precision is improved, but loss of time increases
Solution Approach 1:
The system automatically learns and corrects execution conditions by observing user behavior patterns, eliminating the need for manual definition and correction processes. This self-service approach achieves high execution precision while completely eliminating the time loss associated with manual condition setup and adjustment.
Solution Approach 2:
The system performs preliminary learning and observation before executing voice commands, automatically acquiring execution conditions in advance. This preliminary action of learning from usage patterns eliminates the need for manual pre-definition, reducing time loss while maintaining or improving execution precision.
Data Source
AI summary
A learning system includes an obtainer and a learner. The obtainer obtains information observed around a user who has uttered a voice command. The learner learns the information obtained by the obtainer as a condition for executing the voice command.