Context-Aware Utterance Suggestions for Registered IoT Devices
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Solution Overview
Problem
Voice assistants often suggest fixed utterances for IoT devices without considering user context, leading to incorrect suggestions and a negative user experience.
Innovation Solution
Generate a suggested-utterance list tailored to the user's registered devices based on state information, extracting utterances capable of successful execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a fixed utterance list is provided for IoT devices, then the voice assistant service can be implemented with simple structure, but the utterance suggestion accuracy deteriorates because user context is not considered
Solution Approach 1:
The system dynamically generates utterance suggestions based on real-time device states and user context rather than using fixed pre-defined utterances. The utterance list changes according to device operational states, user preferences, and interaction history, making the system adaptive and context-aware while maintaining manageable complexity through automated state monitoring and matching algorithms
Solution Approach 2:
The system changes the parameters of utterance suggestions based on device states and user context. Instead of static utterances, the system modifies suggestion parameters such as device state, user preference weightings, and context relevance scores to generate appropriate utterances for each situation, improving accuracy without requiring complex manual configuration
2Measurement precision
If context-aware utterance suggestions are implemented, then the utterance suggestion accuracy is improved, but the device complexity increases due to the need to process user context information
Solution Approach 1:
The system automatically monitors device states, retrieves user context information, and generates appropriate utterance suggestions without requiring manual intervention or complex configuration. The voice assistant service self-manages the complexity of processing user context by implementing automated context acquisition, state monitoring, and utterance matching mechanisms that adapt to user needs dynamically
Solution Approach 2:
The system implements feedback loops where user interactions and device states are continuously monitored and used to refine utterance suggestions. The system learns from successful and unsuccessful voice commands, adjusting future suggestions based on feedback about what works and what doesn't, thereby improving accuracy while managing complexity through iterative optimization rather than complex upfront design
3Ease of operation
If fixed utterances are used for device control, then the ease of operation is maintained, but the user experience deteriorates due to incorrect suggestions
Solution Approach 1:
The system performs preliminary actions by pre-processing device state information and user context before generating utterance suggestions. It proactively prepares context-aware suggestion lists based on current device states and user preferences, ensuring that the most relevant and successful utterances are presented first, thereby maintaining ease of operation while improving execution success rates through pre-filtered, context-appropriate suggestions
Data Source
AI summary
An example electronic device may include a memory configured to store instructions and a processor electrically connected to the memory and configured to execute the instructions. When the instructions are executed by the processor, the processor may be configured to generate a first suggested-utterance list providable to a user and generate a second suggested-utterance list by extracting, from the first suggested-utterance list, utterances capable of successful execution for a device registered to the user, according to state information about the registered device.