Smart Home Voice Comprehension Using Device Context and Clarification
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Solution Overview
Problem
Home automation systems often struggle to comprehend user utterances that do not match pre-designated commands, leading to unsatisfactory responses and increased user friction.
Innovation Solution
The system collects information from smart devices and knowledge bases to dynamically develop a knowledge base, simulates various scenarios, and prompts users for clarifying information when necessary to improve comprehension.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the home automation system uses a fixed set of pre-designated utterances for command recognition, then the system structure remains simple and easy to implement, but the system cannot comprehend user utterances that do not match the pre-designated commands, leading to unsatisfactory responses
Solution Approach 1:
The system dynamically adapts its command recognition capability by switching between two modes: a first mode using a fixed set of pre-designated utterances for simple, structured commands, and a second mode using natural language processing for more complex, unstructured utterances. This dynamic switching allows the system to handle diverse user inputs while maintaining implementation simplicity for routine commands.
Solution Approach 2:
The system introduces an intermediary natural language processing component that bridges the gap between fixed pre-designated commands and arbitrary user utterances. When an utterance does not match pre-designated commands, the NLP component acts as a mediator to interpret the user's intent, enabling the system to comprehend diverse commands without completely redesigning the command structure.
2Measurement precision
If the system prompts users to provide additional information when commands are unclear, then comprehension accuracy improves, but user friction increases due to additional interaction steps
Solution Approach 1:
The system performs preliminary natural language processing on user utterances before determining whether clarification is needed. By pre-processing the utterance to extract potential intent and parameters, the system can more accurately assess whether additional information is required, reducing unnecessary clarification prompts and improving comprehension accuracy for clearly expressed commands.
Solution Approach 2:
The system uses feedback from the NLP analysis to intelligently determine when clarification is needed. The NLP component provides feedback about the clarity and completeness of the user's utterance, allowing the system to selectively prompt for additional information only when necessary, thereby maintaining high comprehension accuracy while minimizing user friction.
Data Source
AI summary
According to at least one embodiment, an artificial intelligence apparatus includes at least one audio sensor configured to capture audio data in an environment, and at least one processor. The at least one processor is configured to: detect an utterance in the audio data; determine an inability to comprehend the utterance based on information being missing in the utterance; and acquire the missing information. The at least processor is further configured to acquire the missing information by at least: acquiring the missing information from at least one smart device of a plurality of smart devices located in the environment; or acquiring the missing information from a knowledge base of the artificial intelligence apparatus. The at least one processor is further configured to complete comprehension of the utterance based on the acquired missing information.