A washing machine appliance includes a wash basket that is rotatably mounted within a wash tub and that defines a wash chamber for receiving a load of clothes. A dispensing assembly selectively dispenses wash fluid through a discharge nozzle into the wash tub and a camera assembly is used to monitor the load of clothes and diagnose the presence of excessive bubbles after a wash cycle. Specifically, a controller of the washing machine appliance uses the camera assembly to obtain one or more images of the load of clothes after the drain phase of the wash cycle and analyzes the one or more images using a machine learning image recognition process to determine a bubble level within the wash tub. If the bubble level is greater than a predetermined bubble threshold, corrective action is taken to reduce the bubbles and detergent concentration.

A laundry appliance includes a camera assembly mounted within view of a chamber configured for receiving a load of clothes. A controller is operably coupled to the camera assembly for waking the camera only when certain conditions occur. Specifically, the controller is configured to detect an activity trigger indicative of interaction with the laundry appliance, obtain one or more images of the chamber using the camera assembly in response to detecting the activity trigger, and analyze the one or more images using a machine learning image recognition process to identify the occurrence of a predetermined condition or event.

A laundry appliance includes a camera assembly mounted within view of a chamber configured for receiving a load of clothes. A controller is operably coupled to the camera assembly for waking the camera only when certain conditions occur. Specifically, the controller is configured to detect an activity trigger indicative of interaction with the laundry appliance, obtain one or more images of the chamber using the camera assembly in response to detecting the activity trigger, and analyze the one or more images using a machine learning image recognition process to identify the occurrence of a predetermined condition or event.

An electronic apparatus is provided. An electronic apparatus includes a memory in which an application is stored, a display, and a processor which executes the application so that, when context information about a user is received, the care course and the care order of each of a plurality of clothing care devices registered in the application are identified on the basis of the received context information about the user, and which controls the display so that a user interface (UI) including customized care information about the user is provided on the basis of the identified care course and care order of each of the plurality of clothing care devices.

An electronic apparatus is provided. An electronic apparatus includes a memory in which an application is stored, a display, and a processor which executes the application so that, when context information about a user is received, the care course and the care order of each of a plurality of clothing care devices registered in the application are identified on the basis of the received context information about the user, and which controls the display so that a user interface (UI) including customized care information about the user is provided on the basis of the identified care course and care order of each of the plurality of clothing care devices.

A washing machine appliance includes a wash basket that is rotatably mounted within a wash tub and that defines a wash chamber for receiving a load of clothes. A dispensing assembly selectively dispenses a flow of wash fluid through a discharge nozzle into the wash tub and a camera assembly is used to monitor the wash fluid and diagnose issues related to the detergent dispenser. Specifically, a controller of the washing machine appliance uses the camera assembly to obtain one or more images of the flow of dispensed wash fluid, analyze the one or more images using a machine learning image recognition process to determine an actual detergent content of the flow of wash fluid, determine that the actual detergent content is different than a target detergent content, and implement a responsive action, e.g., such as stopping the operating cycle and notifying a user.

A washing machine appliance includes a wash basket that is rotatably mounted within a wash tub and that defines a wash chamber for receiving a load of clothes. A dispensing assembly selectively dispenses a flow of wash fluid through a discharge nozzle into the wash tub and a camera assembly is used to monitor the wash fluid and diagnose issues related to the detergent dispenser. Specifically, a controller of the washing machine appliance uses the camera assembly to obtain one or more images of the flow of dispensed wash fluid, analyze the one or more images using a machine learning image recognition process to determine an actual detergent content of the flow of wash fluid, determine that the actual detergent content is different than a target detergent content, and implement a responsive action, e.g., such as stopping the operating cycle and notifying a user.

A prediction method includes acquisition step, prediction step, determination step, and processing step. In acquisition step, parameters for a drying operation are acquired. In prediction step, based on the acquired parameters, a drying time required for the drying operation is predicted by a first prediction method. In determination step, when the restart of the drying operation is detected while the drying operation is stopped, whether the first prediction method performed in prediction step is applicable is determined. In processing step, when the first prediction method is determined to be applicable, the first prediction method is continued and when the first prediction method is determined not to be applicable, predetermined processing is performed.

A prediction method includes acquisition step, prediction step, determination step, and processing step. In acquisition step, parameters for a drying operation are acquired. In prediction step, based on the acquired parameters, a drying time required for the drying operation is predicted by a first prediction method. In determination step, when the restart of the drying operation is detected while the drying operation is stopped, whether the first prediction method performed in prediction step is applicable is determined. In processing step, when the first prediction method is determined to be applicable, the first prediction method is continued and when the first prediction method is determined not to be applicable, predetermined processing is performed.

An operation analysis method includes a first acquisition step, a second acquisition step, a plan designing step, and an analysis step. In the first acquisition step, the operation history of a washer is acquired. In the second acquisition step, at least the action schedule of a user of the washer is acquired. In the plan designing step, based on the operation history acquired in the first acquisition step, an operation plan for the future regarding the normal operation of the washer periodically performed by the user is designed. In the analysis step, based on the action schedule acquired in the second acquisition step, whether the operation of the washer is the normal operation is analyzed. In the plan designing step, the operation plan is updated based on the result of the analysis in analysis step.