Electronic automatically adjusting bidet with machine learning software
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Solution Overview
Problem
Modern bidets do not provide a uniform cleaning experience for users of different sizes, shapes, and genders, as they are typically preset to eject water in a fixed direction, leading to cumbersome manual adjustments and potential accidents due to misalignment.
Innovation Solution
An electronic bidet system incorporating machine learning computer vision technology with internal cameras to identify and model the user's lower body orifices, automatically adjusting nozzle direction, pressure, and cleaning patterns for personalized cleaning and drying cycles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If bidets are preset to eject water in a fixed direction, then the device structure is simple and easy to manufacture, but the cleaning effectiveness deteriorates for users of different sizes, shapes, and genders
Solution Approach 1:
The bidet system dynamically adjusts the nozzle direction and cleaning parameters based on real-time detection of user anatomy. The nozzle position is no longer fixed but can be repositioned automatically according to the detected location of orifices, allowing the system to adapt to different user sizes, shapes, and genders while maintaining effective cleaning
Solution Approach 2:
The system changes multiple parameters simultaneously including nozzle direction angle, water pressure, temperature, and cleaning duration based on detected user characteristics. This allows the bidet to optimize cleaning effectiveness for each user without requiring complex manual adjustments
2Adaptability or versatility
If manual adjustment of nozzle direction is provided, then the cleaning experience can be personalized, but the ease of operation deteriorates due to cumbersome adjustments before each use
Solution Approach 1:
The bidet system performs self-adjustment by automatically detecting user anatomy and positioning the nozzle accordingly without requiring manual intervention. The system serves itself by using sensors and machine learning algorithms to determine the optimal cleaning configuration, eliminating the need for users to manually adjust settings before each use
Solution Approach 2:
The manual mechanical adjustment mechanism is replaced with an automated detection and control system using sensors, computer vision, and machine learning. The system substitutes physical manual positioning with intelligent automated control that detects orifice locations and calculates optimal nozzle angles
3Device complexity
If fixed nozzle direction is used, then the device complexity is low, but the reliability deteriorates due to potential accidents from misalignment
Solution Approach 1:
The bidet system incorporates continuous feedback loops where sensors detect user presence and anatomy, the system processes this information through machine learning algorithms, and adjusts nozzle positioning and cleaning parameters in real-time. This feedback mechanism ensures reliable operation by continuously verifying proper alignment before and during cleaning
Solution Approach 2:
The system performs preliminary detection and positioning actions before the actual cleaning process begins. The sensors scan and identify orifice locations in advance, and the nozzle is pre-positioned to the correct angle before water ejection starts, preventing misalignment accidents
4Measurement precision
If machine learning computer vision technology is implemented, then the measurement precision of orifice location improves, but the device complexity increases
Solution Approach 1:
Complex mechanical measurement mechanisms are replaced with optical sensors and machine learning computer vision algorithms. The system uses cameras and image processing to detect and locate orifices, substituting mechanical measurement with intelligent visual recognition that achieves high precision without complex mechanical structures
Solution Approach 2:
The system introduces an intermediary layer of machine learning software that processes sensor data and translates it into actionable cleaning parameters. This software intermediary bridges the gap between simple sensor input and complex cleaning control, enabling high measurement precision while managing system complexity through intelligent processing
Data Source
AI summary
An electronic bidet system that uses one or more internal cameras to capture images or video of a user as he or she sits on the bidet. The images or video are analyzed using machine learning computer vision technology to identify, and locate, the types, sizes, shapes, and positions of the lower body orifices, and conditions (e.g. hemorrhoids), in the user's genital and rectal areas as well as update the computational model of the user's genital and rectal areas and the computational model for cleaning the user's rectal and genital areas. Based on these analyzed images or video, the system automatically and repeatedly adjusts the bidet settings for the specific conditions (e.g. hemorrhoids) and types of orifices, locations of orifices, sizes of orifices, shapes of orifices, gender, body type, and weight of the user. Furthermore, machine learning software is used to control the cleaning and drying cycles.