Hot water supply system
The hot water supply system uses entry/exit detection and machine learning to estimate bathroom cleaning, addressing the lack of user notification and mold prevention in bathrooms.
JP2026105172APending Publication Date: 2026-06-26NORITZ CORP +1
Patent Information
- Authority / Receiving Office
- JP · JP
- Patent Type
- Applications
- Current Assignee / Owner
- NORITZ CORP
- Filing Date
- 2024-12-16
- Publication Date
- 2026-06-26
AI Technical Summary
Technical Problem
There is no effective means to notify users whether they have cleaned the bathroom after bathing, leading to potential mold growth due to inadequate cleaning.
Method used
A hot water supply system equipped with entry/exit detection units, sensors, and a machine learning model to estimate bathroom cleaning based on user behavior data, including time spent in the bathroom and hot water usage patterns.
Benefits of technology
Accurately estimates bathroom cleaning with high precision, enabling timely notifications to encourage users to clean, thereby preventing mold growth.
✦ Generated by Eureka AI based on patent content.
Smart Images
Figure 2026105172000001_ABST
Abstract
This system provides a hot water supply system that can estimate whether or not the user has cleaned the bathroom. [Solution] The water heater 1 has an entry / exit detection unit that detects when a user enters and leaves the bathroom 6, and an entry / exit detection unit that detects when a user enters and leaves the bathtub 60, and an estimation unit 24 that estimates whether or not bathroom cleaning is performed by inputting predetermined data into a trained model 26. The predetermined data input into the trained model 26 includes first data regarding the time spent in the bathroom after bathing, which is the time from when the user leaves the bathtub 60 closest to the reference time, to when the user leaves the bathtub 60 closest to the reference time, with the time of occurrence of a predetermined event that occurs after the last user has left the bathtub 60 filled with water as the reference time. The trained model 26 is generated by machine learning with explanatory variable data including the first data as explanatory variables and whether or not the user has performed bathroom cleaning as the objective variable.
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