Artificial intelligence apparatus
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Solution Overview
Problem
Conventional water purifiers using speech recognition technology often malfunction due to noise interference in the environment, leading to misrecognition of user commands, and lack customization based on usage history.
Innovation Solution
An artificial intelligence apparatus that determines whether a container is seated on a water dispensing apparatus using weight data and adjusts speech recognition sensitivity, and trains an AI model using usage history information to improve command recognition accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If speech recognition is activated through start word to increase accuracy, then speech recognition accuracy is improved, but device complexity increases
Solution Approach 1:
The system performs preliminary actions by detecting container presence through weight sensors before activating speech recognition. This preliminary detection allows the system to prepare appropriate recognition modes in advance, improving accuracy without requiring complex real-time processing adjustments.
Solution Approach 2:
The speech recognition sensitivity is dynamically adjusted based on container presence detection. When a container is detected, the system switches to a mode with higher speech recognition sensitivity, allowing it to accurately recognize commands even without start words. This dynamic adaptation resolves the contradiction by making the system flexible rather than statically complex.
2Measurement precision
If speech recognition sensitivity is increased to recognize commands in noisy environments, then command recognition accuracy is improved, but misrecognition due to noise increases
Solution Approach 1:
The system applies different speech recognition sensitivity levels to different operational contexts. When a container is detected, high sensitivity mode is activated locally for that operational state, while maintaining lower sensitivity when no container is present. This localized adaptation allows accurate command recognition without overall increased susceptibility to noise.
Solution Approach 2:
The speech recognition sensitivity parameter is changed based on container presence detection. The system adjusts this parameter dynamically - increasing sensitivity when a container is detected to enable accurate command recognition, and maintaining appropriate sensitivity levels to avoid excessive noise-induced misrecognition in other states.
3Adaptability or versatility
If AI model is trained using usage history information to provide customization, then user experience is improved, but data processing requirements increase
Solution Approach 1:
The system provides self-service customization by automatically training the AI model using its own usage history data. The water dispenser learns from its operational patterns and user preferences over time, adapting its behavior without requiring external intervention or complex external processing systems.
Solution Approach 2:
The system implements feedback loops where usage history information is continuously collected, processed, and used to retrain the AI model. This feedback mechanism enables the system to improve its customization capability progressively, adapting to user preferences while managing data processing requirements through iterative learning.
Data Source
AI summary
Disclosed herein are an artificial intelligence apparatus and a method of operating the same. The artificial intelligence apparatus includes one or more processors that obtain weight data of a container and speech data, determines whether the container is seated on a seating portion of a water dispensing apparatus using the weight data, adjusts a speech recognition sensitivity according to whether the container is seated on the seating portion, inputs the first speech data to a speech recognition model and allows the water dispensing apparatus to perform a first water dispensing operation corresponding to first water dispensing information when the speech recognition model outputs the first water dispensing information based on the first speech data.