Dishwasher with personalized utensil detection and scanning aids therefor

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Solution Overview

Problem

Traditional dishwasher spray arms waste resources by equally distributing wash fluid, fail to thoroughly clean utensils in corners, and generate unnecessary noise due to directional spray jets, while existing image recognition systems lack specificity for diverse utensils.

Innovation Solution

A dishwasher with personalized utensil detection using a machine learning model trained on user-specific utensils, facilitated by scanning aids, to optimize wash cycles based on utensil type, location, and orientation.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If traditional spray arms distribute wash fluid equally over all areas, then all areas receive wash fluid, but resources are wasted and cleaning performance in corners is reduced

Engineering Contradiction:

Improvecleaning performanceVSAvoidresource waste

Core Design Contradiction:

ReliabilityVSLoss of energy

Solution Approach 1:

The spray system transitions from uniform spray distribution to localized targeted spraying based on detected utensil positions. The controller activates specific nozzles only in areas where utensils are present, concentrating wash fluid where needed while avoiding waste in empty areas, particularly improving corner coverage without excessive resource consumption

Inventive Principle:

Principle #3Local quality

Solution Approach 2:

The spray pattern becomes dynamic and adaptive rather than static. The system uses image data to determine utensil locations and orientations, then adjusts which nozzles are activated and their spray directions in real-time, allowing the spray configuration to change based on the actual load arrangement in the dishwasher

Inventive Principle:

Principle #15Dynamics

2Reliability

If spray arms follow a circular path to cover all areas, then broad coverage is achieved, but corners are not covered thoroughly

Engineering Contradiction:

Improvecorner cleaning performanceVSAvoidspray system complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The spray system is divided into multiple independently controllable nozzle segments rather than a single rotating spray arm. This segmentation allows specific nozzles to be activated for corner areas while others remain inactive, achieving thorough corner coverage without requiring the entire spray arm system to operate in complex circular paths

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

Image sensors and a controller act as intermediaries between the spray system and the utensils. The image sensor detects utensil positions, the controller processes this information to determine optimal spray targets, and then activates appropriate nozzles, replacing the mechanical intermediary of rotating spray arms with an intelligent control intermediary

Inventive Principle:

Principle #24Intermediary (Mediator)

3Reliability

If spray jets are directed to sides of wash tub during rotation, then coverage is maintained, but unnecessary noise is generated

Engineering Contradiction:

Improvewash coverageVSAvoidnoise

Core Design Contradiction:

ReliabilityVSObject-generated harmful factors

Solution Approach 1:

The system performs preliminary detection of utensil positions using image sensors before initiating the wash cycle. Based on this advance knowledge of where utensils are located, the controller pre-determines which nozzles to activate and their optimal spray directions, ensuring wash coverage is maintained while avoiding spray directed at empty tub sides that would generate noise

Inventive Principle:

Principle #10Preliminary action

4Adaptability or versatility

If generalized image recognition systems are used for any dishwasher, then versatility is achieved, but performance on specific utensils is poor

Engineering Contradiction:

Improveutensil detection versatilityVSAvoidutensil detection accuracy

Core Design Contradiction:

Adaptability or versatilityVSMeasurement precision

Solution Approach 1:

The dishwasher system performs self-personalization by automatically capturing images of the user's utensils during a training period and using these images to train a customized machine learning model specific to that user's kitchen. This self-service approach eliminates the need for manual configuration while achieving high detection accuracy for the user's specific utensils

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system changes the parameters of the image recognition model from generic to specific by training on user-specific utensil images. The machine learning model's parameters (weights, biases, feature detectors) are adjusted during training to recognize the particular shapes, patterns, and characteristics of the user's utensils, transforming a generalized system into a specialized one

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS12539015B2Dishwasher with personalized utensil detection and scanning aids therefor

Publication Date: 2026.02.03 MIDEA GROUP CO LTD

AI summary

A dishwasher may utilize personalized utensil detection to detect the utensils regularly washed by the dishwasher, in part based upon training of a personalized machine learning model used in personalized utensil detection. One or more scanning aids may also be used to provide a common frame of reference when capturing images of utensils used to train the personalized machine learning model.