Patent Grant
11866869
The present subject matter relates generally to washing machine appliances, or more specifically, to systems and methods for using image recognition processes to estimate load sizes for washing machines.
Washing machine appliances generally include a tub for containing water or wash fluid, e.g., water and detergent, bleach, or other wash additives. A basket is rotatably mounted within the tub and defines a wash chamber for receipt of articles for washing. During normal operation of such washing machine appliances, the wash fluid is directed into the tub and onto articles within the wash chamber of the basket. The basket or an agitation element can rotate at various speeds to agitate articles within the wash chamber, to wring wash fluid from articles within the wash chamber, etc. During a spin or drain cycle, a drain pump assembly may operate to discharge water from within sump.
A common concern during operation of washing machine appliances is an accurate evaluation of the load size for articles loaded within the wash basket of the washing machine appliance. In some washing machine appliances, the load size is utilized to influence a washing operation and can determine, for instance, basket speed, the volume of wash additive or wash fluid added to the wash basket, etc. If an improper or inaccurate load size is utilized, articles may become damaged or be insufficiently cleaned over the course of the washing operation. However, conventional washing machine appliances require a user to select guess the appropriate load size. However, it may be difficult for a user to accurately determine the proper input or size of a given load.
Attempts have been made to automatically (e.g., without direct user input or estimations) detect certain attributes of a load using sensors or detection assemblies within the washing machine appliance. Unfortunately, though, such systems may increase the expense and complexity of an appliance. Moreover, existing systems for automatically determining a load size (e.g., without a specific user-specified input or determination) may be require resource-intensive steps or calculations, which can increase the cost or required time for washing each load.
Accordingly, improved methods and systems for determining a load size in washing machine appliances are desired. In particular, methods and systems that provide for an accurate determination for a specific load to be washed would be advantageous, especially if such systems or methods could be achieved without requiring additional or dedicated sensing assemblies to be installed on the washing machine appliance. Additionally or alternatively, it may be beneficial to provide a system or method to quickly or easily estimate load size without increasing the time or resources required for washing each load.
Aspects and advantages of the invention will be set forth in part in the following description, or may be obvious from the description, or may be learned through practice of the invention.
In one exemplary aspect of the present disclosure, a method of operating a washing machine appliance is provided. The method may include obtaining one or more images of an external basket spaced apart from the washing machine appliance from a camera assembly and analyzing an obtained image of the one or more images using an image recognition process to estimate a fill level of a load of clothes within the external basket. The method may further include matching the estimated fill level to an estimated load size and directing a wash cycle within the washing machine appliance based on the estimated load size.
In another exemplary aspect of the present disclosure, a method of operating a washing machine appliance is provided. The method may include obtaining one or more images of an external basket spaced apart from the washing machine appliance from a camera assembly. Obtaining one or more images may include receiving a video signal from the camera assembly. The method may also include analyzing an obtained image of the one or more images using an image recognition process to estimate a fill level of a load of clothes within the external basket. The method may further include detecting transfer of the load of clothes to the wash basket matching the estimated fill level to an estimated load size following detecting transfer of the load of clothes. The method may still further include directing a wash cycle within the washing machine appliance based on the estimated load size.
These and other features, aspects and advantages of the present invention will become better understood with reference to the following description and appended claims. The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and, together with the description, serve to explain the principles of the invention.
A full and enabling disclosure of the present invention, including the best mode thereof, directed to one of ordinary skill in the art, is set forth in the specification, which makes reference to the appended figures.
Repeat use of reference characters in the present specification and drawings is intended to represent the same or analogous features or elements of the present invention.
Reference now will be made in detail to embodiments of the invention, one or more examples of which are illustrated in the drawings. Each example is provided by way of explanation of the invention, not limitation of the invention. In fact, it will be apparent to those skilled in the art that various modifications and variations can be made in the present invention without departing from the scope of the invention. For instance, features illustrated or described as part of one embodiment can be used with another embodiment to yield a still further embodiment. Thus, it is intended that the present invention covers such modifications and variations as come within the scope of the appended claims and their equivalents.
As used herein, the terms “first,” “second,” and “third” may be used interchangeably to distinguish one component from another and are not intended to signify location or importance of the individual components. The terms “includes” and “including” are intended to be inclusive in a manner similar to the term “comprising.” Similarly, the term “or” is generally intended to be inclusive (i.e., “A or B” is intended to mean “A or B or both”). In addition, here and throughout the specification and claims, range limitations may be combined or interchanged. Such ranges are identified and include all the sub-ranges contained therein unless context or language indicates otherwise. For example, all ranges disclosed herein are inclusive of the endpoints, and the endpoints are independently combinable with each other. The singular forms “a,” “an,” and “the” include plural references unless the context clearly dictates otherwise.
Approximating language, as used herein throughout the specification and claims, may be applied to modify any quantitative representation that could permissibly vary without resulting in a change in the basic function to which it is related. Accordingly, a value modified by a term or terms, such as “generally,” “about,” “approximately,” and “substantially,” are not to be limited to the precise value specified. In at least some instances, the approximating language may correspond to the precision of an instrument for measuring the value, or the precision of the methods or machines for constructing or manufacturing the components or systems. For example, the approximating language may refer to being within a 10 percent margin, i.e., including values within ten percent greater or less than the stated value. In this regard, for example, when used in the context of an angle or direction, such terms include within ten degrees greater or less than the stated angle or direction, e.g., “generally vertical” includes forming an angle of up to ten degrees in any direction, e.g., clockwise or counterclockwise, with the vertical direction V.
The word “exemplary” is used herein to mean “serving as an example, instance, or illustration.” In addition, references to “an embodiment” or “one embodiment” does not necessarily refer to the same embodiment, although it may. Any implementation described herein as “exemplary” or “an embodiment” is not necessarily to be construed as preferred or advantageous over other implementations. Moreover, each example is provided by way of explanation of the invention, not limitation of the invention. In fact, it will be apparent to those skilled in the art that various modifications and variations can be made in the present invention without departing from the scope of the invention. For instance, features illustrated or described as part of one embodiment can be used with another embodiment to yield a still further embodiment. Thus, it is intended that the present invention covers such modifications and variations as come within the scope of the appended claims and their equivalents.
Referring now to the figures, an exemplary laundry appliance that may be used to implement aspects of the present subject matter will be described. Specifically,
According to exemplary embodiments, washing machine appliance 100 includes a cabinet 102 that is generally configured for containing or supporting various components of washing machine appliance 100 and which may also define one or more internal chambers or compartments of washing machine appliance 100. In this regard, as used herein, the terms “cabinet,” “housing,” and the like are generally intended to refer to an outer frame or support structure for washing machine appliance 100, e.g., including any suitable number, type, and configuration of support structures formed from any suitable materials, such as a system of elongated support members, a plurality of interconnected panels, or some combination thereof. It should be appreciated that cabinet 102 does not necessarily require an enclosure and may simply include open structure supporting various elements of washing machine appliance 100. By contrast, cabinet 102 may enclose some or all portions of an interior of cabinet 102. It should be appreciated that cabinet 102 may have any suitable size, shape, and configuration while remaining within the scope of the present subject matter.
As illustrated, cabinet 102 generally extends between a top 104 and a bottom 106 along the vertical direction V, between a first side 108 (e.g., the left side when viewed from the front as in
Referring to
Wash basket 120 may define one or more agitator features that extend into wash chamber 126 to assist in agitation and cleaning articles disposed within wash chamber 126 during operation of washing machine appliance 100. For example, as illustrated in
According to exemplary embodiments, wash tub 124 may be generally suspended within cabinet 102 by one or more suspension assemblies 129, e.g., as shown for example in
Referring generally to
A window 136 in door 134 permits viewing of wash basket 120 when door 134 is in the closed position, e.g., during operation of washing machine appliance 100. In optional embodiments, window 136 includes a discrete inner window 174 and outer window 176. Door 134 also includes a handle (not shown) that, e.g., a user may pull when opening and closing door 134. Further, although door 134 is illustrated as mounted to front panel 130, it should be appreciated that door 134 may be mounted to another side of cabinet 102 or any other suitable support according to alternative embodiments. Washing machine appliance 100 may further include a latch assembly 138 (see
Referring again to
A drain pump assembly 144 is located beneath wash tub 124 and is in fluid communication with sump 142 for periodically discharging soiled wash fluid from washing machine appliance 100. Drain pump assembly 144 may generally include a drain pump 146 which is in fluid communication with sump 142 and with an external drain 148 through a drain hose 150. During a drain cycle, drain pump 146 urges a flow of wash fluid from sump 142, through drain hose 150, and to external drain 148. More specifically, drain pump 146 includes a motor (not shown) which is energized during a drain cycle such that drain pump 146 draws wash fluid from sump 142 and urges it through drain hose 150 to external drain 148.
Washing machine appliance 100 may further include a wash fluid dispenser that is generally configured for dispensing a flow of water, wash fluid, etc. into wash tub 124. For example, a spout 152 is configured for directing a flow of fluid into wash tub 124. For example, spout 152 may be in fluid communication with a water supply 155 (
As illustrated in
In addition, a water supply valve 158 may provide a flow of water from a water supply source (such as a municipal water supply 155) into detergent dispenser 156 and into wash tub 124. In this manner, water supply valve 158 may generally be operable to supply water into detergent dispenser 156 to generate a wash fluid, e.g., for use in a wash cycle, or a flow of fresh water, e.g., for a rinse cycle. It should be appreciated that water supply valve 158 may be positioned at any other suitable location within cabinet 102. In addition, although water supply valve 158 is described herein as regulating the flow of “wash fluid,” it should be appreciated that this term includes, water, detergent, other additives, or some mixture thereof.
During operation of washing machine appliance 100, laundry items are loaded into wash basket 120 through opening 132, and washing operation is initiated through operator manipulation of one or more input selectors or using a remote device 182 (see below). Wash tub 124 is filled with water, detergent, or other fluid additives, e.g., via spout 152 or detergent drawer 156. One or more valves (e.g., water supply valve 158) can be controlled by washing machine appliance 100 to provide for filling wash basket 120 to the appropriate level for the amount of articles being washed or rinsed. By way of example for a wash mode, once wash basket 120 is properly filled with fluid, the contents of wash basket 120 can be agitated (e.g., with ribs 128) for washing of laundry items in wash basket 120.
After the agitation phase of the wash cycle is completed, wash tub 124 can be drained. Laundry articles can then be rinsed by again adding fluid to wash tub 124, depending on the particulars of the cleaning cycle selected by a user. Ribs 128 may again provide agitation within wash basket 120. One or more spin cycles may also be used. In particular, a spin cycle may be applied after the wash cycle or after the rinse cycle in order to wring wash fluid from the articles being washed. During a final spin cycle, basket 120 is rotated at relatively high speeds and drain assembly 144 may discharge wash fluid from sump 142. After articles disposed in wash basket 120 are cleaned, washed, or rinsed, the user can remove the articles from wash basket 120, e.g., by opening door 134 and reaching into wash basket 120 through opening 132.
Referring again to
Washing machine appliance 100 may further include or be in operative communication with a processing device or a controller 166 that may be generally configured to facilitate appliance operation. In this regard, control panel 160, user input devices 162, and display 164 may be in communication with controller 166 such that controller 166 may receive control inputs from user input devices 162, may display information using display 164, and may otherwise regulate operation of washing machine appliance 100. For example, signals generated by controller 166 may operate washing machine appliance 100, including any or all system components, subsystems, or interconnected devices, in response to the position of user input devices 162 and other control commands. Control panel 160 and other components of washing machine appliance 100, such as motor assembly 122 and machine measurement device 168 (discussed herein), may be in communication with controller 166 via one or more signal lines or shared communication busses. In this manner, Input/Output (“I/O”) signals may be routed between controller 166 and various operational components of washing machine appliance 100. Optionally, machine measurement device 168 may be included with controller 166. Moreover, machine measurement devices 168 may include a microprocessor that performs the calculations specific to the measurement of motion with the calculation results being used by controller 166.
As used herein, the terms “processing device,” “computing device,” “controller,” or the like may generally refer to any suitable processing device, such as a general or special purpose microprocessor, a microcontroller, an integrated circuit, an application specific integrated circuit (ASIC), a digital signal processor (DSP), a field-programmable gate array (FPGA), a logic device, one or more central processing units (CPUs), a graphics processing units (GPUs), processing units performing other specialized calculations, semiconductor devices, etc. In addition, these “controllers” are not necessarily restricted to a single element but may include any suitable number, type, and configuration of processing devices integrated in any suitable manner to facilitate appliance operation. Alternatively, controller 166 may be constructed without using a microprocessor, e.g., using a combination of discrete analog or digital logic circuitry (such as switches, amplifiers, integrators, comparators, flip-flops, OR gates, and the like) to perform control functionality instead of relying upon software.
Controller 166 may include, or be associated with, one or more memory elements or non-transitory computer-readable storage mediums, such as RAM, ROM, EEPROM, EPROM, flash memory devices, magnetic disks, or other suitable memory devices (including combinations thereof). These memory devices may be a separate component from the processor or may be included onboard within the processor. In addition, these memory devices can store information or data accessible by the one or more processors, including instructions that can be executed by the one or more processors. It should be appreciated that the instructions can be software written in any suitable programming language or can be implemented in hardware. Additionally, or alternatively, the instructions can be executed logically or virtually using separate threads on one or more processors.
For example, controller 166 may be operable to execute programming instructions or micro-control code associated with an operating cycle of washing machine appliance 100. In this regard, the instructions may be software or any set of instructions that when executed by the processing device, cause the processing device to perform operations, such as running one or more software applications, displaying a user interface, receiving user input, processing user input, etc. Moreover, it should be noted that controller 166 as disclosed herein is capable of and may be operable to perform any methods, method steps, or portions of methods of appliance operation. For example, in some embodiments, these methods may be embodied in programming instructions stored in the memory and executed by controller 166.
The memory devices may also store data that can be retrieved, manipulated, created, or stored by the one or more processors or portions of controller 166. The data can include, for instance, data to facilitate performance of methods described herein. The data can be stored locally (e.g., on controller 166) in one or more databases or may be split up so that the data is stored in multiple locations. In addition, or alternatively, the one or more database(s) can be connected to controller 166 through any suitable network(s), such as through a high bandwidth local area network (LAN) or wide area network (WAN). In this regard, for example, controller 166 may further include a communication module or interface that may be used to communicate with one or more other component(s) of washing machine appliance 100, controller 166, an external device 182 (e.g., device controller 188), or any other suitable device, e.g., via any suitable communication lines or network(s) and using any suitable communication protocol. The communication interface can include any suitable components for interfacing with one or more network(s), including for example, transmitters, receivers, ports, controllers, antennas, or other suitable components.
In optional embodiments, one or more machine measurement devices 168 may be provided in the washing machine appliance 100 for measuring movement (e.g., of the tub 124). Machine measurement devices 168 may measure a variety of suitable variables that can be correlated to movement within the washing machine appliance 100, such as at the tub 124. The movement measured by such devices 180 can be utilized to selectively help estimate the load size of articles within tub 124 or the transfer of articles to the wash basket 120.
A machine measurement device 168 in accordance with the present disclosure may include an accelerometer (e.g., fixed to tub 124), which measures translational motion, such as acceleration along one or more directions. Additionally or alternatively, a machine measurement device 168 may include a gyroscope, which measures rotational motion, such as rotational velocity about an axis. A machine measurement device 168 in accordance with the present disclosure is mounted to the tub 124 (e.g., on a sidewall of tub 124) to sense movement of the tub 124 relative to the cabinet 102 or rotation axis A by measuring uniform periodic motion, non-uniform periodic motion, or excursions of the tub 124 during appliance 100 operation. For instance, movement may be measured as discrete identifiable components (e.g., in a predetermined direction).
In exemplary embodiments, a machine measurement device 168 may include at least one gyroscope or at least one accelerometer. The machine measurement device 168, for example, may be a printed circuit board that includes the gyroscope and accelerometer thereon. The machine measurement device 168 may be mounted to the tub 124 (e.g., via a suitable mechanical fastener, adhesive, etc.) and may be oriented such that the various sub-components (e.g., the gyroscope and accelerometer) are oriented to measure movement along or about particular directions as discussed herein. Notably, the gyroscope and accelerometer in exemplary embodiments are mounted to the tub 124 at a single location (e.g., the location of the printed circuit board or other component of the machine measurement device 168 on which the gyroscope and accelerometer are grouped). Alternatively, however, the gyroscope and accelerometer need not be mounted at a single location. For example, a gyroscope located at one location on tub 124 can measure the rotation of an accelerometer located at a different location on tub 124, because rotation about a given axis is the same everywhere on a solid object such as tub 124.
Additionally or alternatively, the machine measurement device 168 may include another suitable sensor or device for measuring movement of the tub 124. For instance, the machine measurement device 168 may be provided as or include an optical sensor, an inductive sensor, an ultrasonic sensor, etc.
Referring again to
For example, external communication system 180 permits controller 166 of washing machine appliance 100 to communicate with a separate device external to washing machine appliance 100, referred to generally herein as a remote or external device 182. As described in more detail below, these communications may be facilitated using a wired or wireless connection, such as via a network 184. In general, external device 182 may be any suitable device separate from washing machine appliance 100 that is configured to provide or receive communications, information, data, or commands from a user. In this regard, external device 182 may be, for example, a personal phone, a smartphone, a tablet, a laptop or personal computer, a wearable device, a smart home system, or another mobile or remote device. In turn, external device 182 may include a monitor or screen 190 configured to display digital two-dimensional images, as would be understood.
In some embodiments, remote user device 182 includes a camera or camera module 178. Camera 178 may be any type of device suitable for capturing a two-dimensional picture or image. As an example, camera 178 may be a video camera or a digital camera with an electronic image sensor [e.g., a charge coupled device (CCD) or a CMOS sensor]. When assembled, camera 178 is generally mounted or fixed to a body of remote user device 182 and is in communication (e.g., electric or wireless communication) with a controller 188 of the remote user device 182 such that the controller may receive a signal from camera 178 corresponding to the image captured by camera 178.
Generally, external device 182 may include a controller 188 (e.g., including one or more suitable processing devices, such as a general or special purpose microprocessor, a microcontroller, an integrated circuit, an application specific integrated circuit (ASIC), a digital signal processor (DSP), a field-programmable gate array (FPGA), a logic device, one or more central processing units (CPUs), a graphics processing units (GPUs), processing units performing other specialized calculations, semiconductor devices, etc. Controller 188 may include, or be associated with, one or more memory elements or non-transitory computer-readable storage mediums, such as RAM, ROM, EEPROM, EPROM, flash memory devices, magnetic disks, or other suitable memory devices (including combinations thereof). These memory devices may be a separate component from the processor of controller 188 or may be included onboard within such processor. In addition, these memory devices can store information or data accessible by the one or more processors of the controller 188, including instructions that can be executed by the one or more processors. It should be appreciated that the instructions can be software written in any suitable programming language or can be implemented in hardware. Additionally, or alternatively, the instructions can be executed logically or virtually using separate threads on one or more processors.
For example, controller 188 may be operable to execute programming instructions or micro-control code associated with operation of or engagement with washing machine appliance 100. In this regard, the instructions may be software or any set of instructions that when executed by the processing device, cause the processing device to perform operations, such as running one or more software applications, displaying or directing a user interface, receiving user input, processing user input, etc. Moreover, it should be noted that controller 188 as disclosed herein is capable of and may be operable to perform one or more methods, method steps, or portions of methods of appliance operation. For example, in some embodiments, these methods may be embodied in programming instructions stored in the memory and executed by controller 188.
The memory devices of controller 188 may also store data that can be retrieved, manipulated, created, or stored by the one or more processors or portions of controller 166. The data can include, for instance, data to facilitate performance of methods described herein. store data that can be retrieved, manipulated, created, or stored by the one or more processors or portions of controller 188. The data can include, for instance, data to facilitate performance of methods described herein. As an example, and turning briefly to
In certain embodiments, a remote measurement device 192 may be included with or connected to controller 188 on external device 182. Moreover, remote measurement devices 192 may include a microprocessor that performs the calculations specific to the measurement of position or movement with the calculation results being used by controller 188. Generally, remote measurement device 192 may detect a plurality of angle readings. For instance, multiple angle readings may be detected simultaneously to track multiple (e.g., mutually orthogonal) axes of the external device 182, such as an X-axis, Y-axis, and Z-axis shown in
A remote measurement device 192 in accordance with the present disclosure can be mounted on or within the external device 182, as required to sense movement or position of external device 182 relative to the cabinet 102 of appliance 100. Optionally, remote measurement device 192 may include at least one gyroscope or at least one accelerometer. The remote measurement device 192, for example, may be a printed circuit board which includes the gyroscope and accelerometer thereon.
Returning generally to
Separate from or in addition to external device 182, a remote server 186 may be in communication with washing machine appliance 100 or external device 182 through network 184. In this regard, for example, remote server 186 may be a cloud-based server 186, and is thus located at a distant location, such as in a separate state, country, etc. According to an exemplary embodiment, external device 182 may communicate with a remote server 186 over network 184, such as the Internet, to transmit/receive data or information, provide user inputs, receive user notifications or instructions, interact with or control washing machine appliance 100, etc. In addition, external device 182 and remote server 186 may communicate with washing machine appliance 100 to communicate similar information.
In general, communication between washing machine appliance 100, external device 182, remote server 186, or other user devices or appliances may be carried using any type of wired or wireless connection and using any suitable type of communication network, non-limiting examples of which are provided below. For example, external device 182 may be in direct or indirect communication with washing machine appliance 100 through any suitable wired or wireless communication connections or interfaces, such as network 184. For example, network 184 may include one or more of a local area network (LAN), a wide area network (WAN), a personal area network (PAN), the Internet, a cellular network, any other suitable short- or long-range wireless networks, etc. In addition, communications may be transmitted using any suitable communications devices or protocols, such as via Wi-Fi®, Bluetooth®, Zigbee®, wireless radio, laser, infrared, Ethernet type devices and interfaces, etc. In addition, such communication may use a variety of communication protocols (e.g., TCP/IP, HTTP, SMTP, FTP), encodings or formats (e.g., HTML, XML), or protection schemes (e.g., VPN, secure HTTP, SSL).
External communication system 180 is described herein according to an exemplary embodiment of the present subject matter. However, it should be appreciated that the exemplary functions and configurations of external communication system 180 provided herein are used only as examples to facilitate description of aspects of the present subject matter. System configurations may vary, other communication devices may be used to communicate directly or indirectly with one or more associated appliances, other communication protocols and steps may be implemented, etc. These variations and modifications are contemplated as within the scope of the present subject matter.
Now that the construction of washing machine appliance 100 and system 180 according to exemplary embodiments have been presented, exemplary methods (e.g., methods 500 and 600) of operating a washing machine appliance will be described. Although the discussion below refers to the exemplary methods 500 and 600 of operating washing machine appliance 100, one skilled in the art will appreciate that the exemplary methods 500 and 600 are applicable to the operation of a variety of other washing machine appliances, such as vertical axis washing machine appliances. In exemplary embodiments, the various method steps as disclosed herein may be performed (e.g., in whole or part) by controller 188, controller 166, or another, separate controller (e.g., on remote server 186).
Advantageously, methods in accordance with the present disclosure may permit the size of a load of clothes to be automatically and accurately determined. Additionally or alternatively, a user may be advantageously guided to ensure consistent and accurate images are gathered to, in turn, ensure accuracy of any further determinations. Further additionally or alternatively, the cumulative run time and resources expended by the washing machine appliance (e.g., over time or multiple discrete washing operations) may be reduced while still providing an accurate estimation of load size without requiring any guessing or estimations to be made by a user.
Referring now to
It should be appreciated that obtaining the images may include obtaining more than one image, a series of frames, a video, or any other suitable visual representation of the load of clothes using the camera assembly. Thus, 510 may include receiving a video signal from the camera assembly. Separate from or in addition to the video signal, the images obtained by the camera assembly may vary in number, frequency, angle, resolution, detail, etc. in order to improve the clarity of a load of clothes (e.g., held within the external basket to be transferred to the wash basket). In addition, the obtained images may also be cropped in any suitable manner for improved focus on desired portions of the load of clothes.
In optional embodiments, the obtained images can be presented or displayed as a real-time feed of the camera assembly at the remote device (e.g., according to the received video signal). For instant, a constant or regularly refreshing set of live images from the camera assembly may be presented on the monitor or screen of the remote device. Thus, a user viewing the remote device may be able to see the field of view being captured by the camera assembly (e.g., without having to repeatedly freeze the frame or provide any active input by a user on the remote device).
The one or more images may be obtained using the camera assembly at any suitable time prior to transferring the load of clothes to the wash chamber or initiating the wash cycle. For example, as best illustrated in
At 520, the method 500 includes receiving one or more position signals corresponding to the position or orientation of the external device.
In some embodiments, 520 includes receiving a plurality of angle readings from the remote device. Such angle readings may generally indicate the position of the remote device (e.g., in multiple dimensions, such as three) relative to a fixed direction, axis, or point. For instance, the multiple readings may be detected for the remote device relative to gravity. The angle readings may be received following or in tandem with 510. In some embodiments, the angle readings may be received from a measurement device of the remote device (e.g., as described above). In particular, the measurement device may include an accelerometer configured to detect the tilt or angle of the remote device, as would be understood. Moreover, 520 may include determining a position (e.g., tilt or angular position) of the remote device (e.g., relative to the external basket or gravity). For instance, the angle readings may indicate how the remote device (and thus camera assembly) is oriented in space.
In additional or alternative embodiments, 520 includes receiving feedback signals related to an opening perimeter or fiducial reference on the external basket. Thus, 520 may include detecting a fiducial reference on the external basket within the one or more images. For instance, from the obtained images, the controller may identify the region corresponding to a predetermined portion of the external basket (e.g., the perimeter opening through which clothing is received, as indicated in
As is understood, recognizing or identifying such fiducial references or portions of the external basket, may be performed by one or more image processing techniques or algorithms (e.g., executed at the controller of the remote device, remote server, or appliance). According to exemplary embodiments, image processing may include blur detection algorithms that are generally intended to compute, measure, or otherwise determine the amount of blur in an image. For example, these blur detection algorithms may rely on focus measure operators, the Fast Fourier Transform along with examination of the frequency distributions, determining the variance of a Laplacian operator, or any other methods of blur detection known by those having ordinary skill in the art. In addition, or alternatively, the image processing algorithms may use other suitable techniques for recognizing or identifying items or objects, such as edge matching or detection, divide-and-conquer searching, greyscale matching, histograms of receptive field responses, or another suitable routine (e.g., executed at the controller of the remote device, remote server, or appliance based on one or more captured images from one or more cameras). Other image processing techniques are possible and within the scope of the present subject matter. The processing algorithm may further include measures for isolating or eliminating noise in the image comparison, e.g., due to image resolution, data transmission errors, inconsistent lighting, or other imaging errors. By eliminating such noise, the image processing algorithms may improve accurate object detection, avoid erroneous object detection, and isolate the important object, region, or pattern within an image.
Optionally, 520 may include comparing the detected fiducial reference to a two-dimensional reference shape in an obtained image of the one or more images. As would be understood, the two-dimensional geometry of a fiducial reference captured in an obtained image will vary depending on the angle of the camera when the image is obtained. The two-dimensional reference shape may correspond to the geometry of the fiducial reference in a set or predetermined camera angle (e.g., in which images to accurately analyze the load within the wash chamber may be obtained). As an example, the two-dimensional reference shape may be a rectangle, such as may correspond to an intended geometry of the opening of the external basket in a set angle of the camera. From the comparison, it may be determined if the fiducial reference matches the two-dimensional reference shape (e.g., the fiducial reference within the obtained image has dimensions that are within a set tolerance or range of the two-dimensional reference shape, such as 10%). For instance, the size or eccentricity of the fiducial reference within the obtained image may be calculated and compared to the size or eccentricity programmed for the two-dimensional reference shape.
In certain embodiments, the two-dimensional reference shape may be overlaid on the real-time feed (e.g., presented on the remote device). Thus, as would be understood, a representation of the two-dimensional reference shape may be overlaid onto the real-time feed of the camera and appears as a fixed object in front of the digital representation (i.e., video) of the external basket on the monitor of the remote device. The position of the two-dimensional reference shape that is displayed or overlaid may be constant, even as the camera angle and obtained images change. Thus, a user may be guided to move the camera such that the fiducial reference aligns to (e.g., beneath) the overlaid two-dimensional reference shape. Separate from or in addition to the two-dimensional reference shape, the method 500 may provide for displaying movement guidance (e.g., in the form of pictorial or textual instructions, such as arrows or written messages) with the real-time feed (e.g., to help a user move the camera to align the two-dimensional reference shape with the fiducial reference).
At 530, the method 500 includes determining a set camera angle (e.g., horizontal angle) for a camera assembly is met based on the received position signals (i.e., subsequent to 520). As an example, the determined position of the remote device may be determined to match the set camera angle (e.g., within a set tolerance or range, such as 10%). As an additional or alternative example, it may be determined that, within an obtained image, the fiducial reference matches or is aligned with the two-dimensional reference shape. Specifically, it may be determined that the fiducial reference in the obtained image has dimensions that correspond in size, curve, or location (e.g., within a set tolerance or range, such as 10%) to the dimensions of the two-dimensional reference shape.
In optional embodiments, a feedback signal is generated (e.g., at the remote device) in response to 530. Such a feedback signal may prompt a feedback action (e.g., visual alert on the monitor, haptic movement at the remote device, audio tone, etc.) corresponding to the set camera angle being met such that a user can know further movement of the camera or remote device is unnecessary.
At least one obtained image may be selected or recorded in response to determining the set camera angle is met. For instance, the obtained image may be automatically selected or recorded in direct response to meeting the set camera angle or, alternatively, a user may be prompted to select or record the obtained image once or while the set camera angle is met. Thus, at least one obtained image may capture a view of the external basket while the camera or remote device is at the set camera angle (e.g., directed downward).
At 540, the method 500 includes analyzing an obtained image (e.g., the above-described obtained image from the one or more images). Specifically, the obtained image may be analyzed using a (e.g., machine learning) image recognition process to estimate a fill level of the load of clothes within the external basket. Variations in the fill level may generally change the visible height or volume of clothes within the external basket (e.g., as illustrated in
As used herein, the terms image recognition, object detection, and similar terms may be used generally to refer to any suitable method of observation, analysis, image decomposition, feature extraction, image classification, etc. of one or more image or videos taken of an external basket holding a load of clothes outside of a washing machine appliance. It should be appreciated that any suitable image recognition software or process may be used to analyze images taken by the camera assembly and a controller may be programmed to perform such processes and take corrective action.
In certain embodiments, the image analysis may include utilizing artificial intelligence (“AI”), such as a machine learning image recognition process, a neural network classification module, any other suitable artificial intelligence (AI) technique, or any other suitable image analysis techniques, examples of which will be described in more detail below. Moreover, each of the exemplary image analysis or evaluation processes described below may be used independently, collectively, or interchangeably to extract detailed information regarding the images being analyzed to facilitate performance of one or more methods described herein or to otherwise improve appliance operation. According to exemplary embodiments, any suitable number and combination of image processing, image recognition, or other image analysis techniques may be used to obtain an accurate analysis of the obtained images.
In this regard, the image recognition process may use any suitable artificial intelligence technique, for example, any suitable machine learning technique, or for example, any suitable deep learning technique. According to an exemplary embodiment, controller may implement a form of image recognition called region based convolutional neural network (“R-CNN”) image recognition. Generally speaking, R-CNN may include taking an input image and extracting region proposals that include a potential object, such as an item of clothing (e.g., jeans, socks, etc.) or an undesirable article (e.g., a belt, a wallet, etc.). In this regard, a “region proposal” may be regions in an image that could belong to a particular object. A convolutional neural network is then used to compute features from the regions proposals and the extracted features will then be used to determine a classification for each particular region.
According to still other embodiments, an image segmentation process may be used along with the R-CNN image recognition. In general, image segmentation creates a pixel-based mask for each object in an image and provides a more detailed or granular understanding of the various objects within a given image. In this regard, instead of processing an entire image—i.e., a large collection of pixels, many of which might not contain useful information—image segmentation may involve dividing an image into segments (e.g., into groups of pixels containing similar attributes) that may be analyzed independently or in parallel to obtain a more detailed representation of the object or objects in an image. This may be referred to herein as “mask R-CNN” and the like. It should be appreciated that any other suitable image recognition process may be used while remaining within the scope of the present subject matter.
According to still other embodiments, the image recognition process may use any other suitable neural network process. For example, 540 may include using Mask R-CNN instead of a regular R-CNN architecture. In this regard, Mask R-CNN is based on Fast R-CNN which is slightly different than R-CNN. For example, R-CNN first applies CNN and then allocates it to zone recommendations on the covn5 property map instead of the initially split into zone recommendations. In addition, according to exemplary embodiments standard CNN may be used to analyze the image and estimate fill level of the load within the wash basket.
According to exemplary embodiments the image recognition process may further include the implementation of Vision Transformer (ViT) techniques or models. In this regard, ViT is generally intended to refer to the use of a vision model based on the Transformer architecture originally designed and commonly used for natural language processing or other text-based tasks. For example, ViT represents an input image as a sequence of image patches and directly predicts class labels for the image. This process may be similar to the sequence of word embeddings used when applying the Transformer architecture to text. The ViT model and other image recognition models described herein may be trained using any suitable source of image data in any suitable quantity. Notably, ViT techniques have been demonstrated to outperform many state-of-the-art neural network or artificial intelligence image recognition processes.
According to still other embodiments, the image recognition process may use any other suitable neural network process while remaining within the scope of the present subject matter. For example, the step of analyzing the one or more images may include using a deep belief network (“DBN”) image recognition process. A DBN image recognition process may generally include stacking many individual unsupervised networks that use each network's hidden layer as the input for the next layer. According to still other embodiments, the step of analyzing one or more images may include the implementation of a deep neural network (“DNN”) image recognition process, which generally includes the use of a neural network (computing systems inspired by the biological neural networks) with multiple layers between input and output. Other suitable image recognition processes, neural network processes, artificial intelligence analysis techniques, and combinations of the above described or other known methods may be used while remaining within the scope of the present subject matter.
In addition, it should be appreciated that various transfer techniques may be used but use of such techniques is not required. If using transfer techniques learning, a neural network architecture may be pretrained such as VGG16/VGG19/ResNet50 with a public dataset then the last layer may be retrained with an appliance specific dataset. In addition, or alternatively, the image recognition process may include detection of certain conditions based on comparison of initial conditions, may rely on image subtraction techniques, image stacking techniques, image concatenation, etc. For example, the subtracted image may be used to train a neural network with multiple classes for future comparison and image classification.
It should be appreciated that the machine learning image recognition models may be actively trained by the appliance with new images, may be supplied with training data from the manufacturer or from another remote source, or may be trained in any other suitable manner. For example, according to exemplary embodiments, this image recognition process relies at least in part on a neural network trained with a plurality of images of the appliance in different configurations, experiencing different conditions, or being interacted with in different manners. This training data may be stored locally or remotely and may be communicated to a remote server for training other appliances and models. According to exemplary embodiments, it should be appreciated that the machine learning models may include supervised or unsupervised models and methods. In this regard, for example, supervised machine learning methods (e.g., such as targeted machine learning) may help identify problems, anomalies, or other occurrences which have been identified and trained into the model. By contrast, unsupervised machine learning methods may be used to detect clusters of potential failures, similarities among data, event patterns, abnormal concentrations of a phenomenon, etc.
It should be appreciated that image processing and machine learning image recognition processes may be used together to facilitate improved image analysis, object detection, color detection, or to extract other useful qualitative or quantitative data or information from the one or more images that may be used to improve the operation or performance of the appliance. Indeed, the methods described herein may use any or all of these techniques interchangeably to improve image analysis process and facilitate improved appliance performance and consumer satisfaction. The image processing algorithms and machine learning image recognition processes described herein are only exemplary and are not intended to limit the scope of the present subject matter in any manner.
At 550, the method 500 includes detecting transfer of the load of clothes to the wash basket (e.g., following 510, 520, 530, or 540). As an example, once the fill level is estimated at 540, a detection may be made that the clothing within the external basket (and captured in the obtained image) is moved to the wash basket. In some embodiments, such a detection corresponds to movement of or within the washing machine appliance. For instance, 550 may include receiving one or more movement signals from the machine measurement device, such as an accelerometer signal indicating movement of the wash tub (e.g., above a set threshold). In additional or alternative embodiments, detection of the transfer may correspond to the door to the washing machine appliance being moved to a closed position (e.g., from an open position). For instance, 550 may include determining the door of the washing machine appliance is closed within the predetermined time period (e.g., following 510). Such as determination may be based on a signal from the latch assembly, or another suitable sensor for detecting the door in the closed position.
At 560, the method 500 includes matching the estimated fill level to an estimated load size. Optionally, the estimated fill level may be applied to a programmed look-up table, chart, graph, or formula in which estimated fill level categorizations or values are correlated to estimated load sizes. For instance, previous determinations may be stored in which a discrete fill level is confirmed to correlate to (e.g., be the equivalent of) a discrete estimated load size. In turn, the “new” estimated fill level from 540 may be found to be near to (e.g., within a set range from) the previously confirmed fill level, which correlates to a discrete estimated load size (e.g., out of a plurality of programmed load sizes). Thus, and in detail, 560 may include determining the estimated fill level of 540 is within the set range of a previously confirmed fill level. The discrete estimated load size may then match the estimated fill level of 540.
In additional or alternative embodiments, 560 includes directing a load estimation sequence within the washing machine appliance to generate the estimated load size. In other words, once the load of clothing is received within the wash basket, the washing machine appliance may execute a load estimation sequence to determine the load size. Such sequences are generally known and may include, for instance, detecting movement of the wash tub, resistance to rotation of the wash basket, or measuring the weight of articles within the wash basket to generate a “new measured load size.” Once determined, the new measured load size may be correlated to the estimated fill level of 540. In turn, future operations in which a similar fill level is detected, the similar fill level may be quickly and notably matched to the new measured load size (e.g., without again having to perform a load estimation sequence within the washing machine appliance).
At 570, the method 500 includes directing a wash cycle within the washing machine appliance based on the estimated load size. Such direction may require adjusting one or more operating parameters of the washing machine appliance (e.g., as part of the wash cycle, which may then be initiated). Thus, 570 may include selecting an operating cycle parameter, adjusting a water or detergent fill amount, or providing a user notification. As used herein, an “operating parameter” of the washing machine appliance is any cycle setting, operating time, component setting, spin speed, part configuration, or other operating characteristic that may affect the performance of the washing machine appliance. In turn, references to operating parameter adjustments or “adjusting at least one operating parameter” are intended to refer to control actions intended to improve system performance based on the load size. For example, adjusting an operating parameter may include adjusting an agitation time or an agitation profile, adjusting a water level, limiting a spin speed of the wash basket, etc. Other operating parameter adjustments are possible and within the scope of the present subject matter.
For example, according to an exemplary embodiment, the mask R-CNN image recognition process may be used on one or more images obtained at 510 to estimate a “small” load size. As a result, it may further be determined that the agitation profile should be gentle, or that the total wash time should be decreased. One or more of the corresponding controllers may automatically detect and implement such a wash cycle without requiring user input. By contrast, if it is estimated that a “large” load size is provided, a large volume of hot water may be used with more detergent or an aggressive agitation profile. It should be appreciated that the exemplary load characteristics and the exemplary operating parameters described herein are only exemplary and not intended to limit the scope of the present subject matter in any manner.
In addition, adjusting the at least one operating parameter may include providing a user notification when a predetermined load attribute exists. For example, if 560 results in the estimation of an excessively large load size, the wash cycle may be restricted (e.g., stopped or otherwise prevented) and a user notification may be provided, e.g., via an indicator on the remote device or the control panel of the appliance. Thus, for example, if a user provides a load that is too large for the washing machine appliance safely wash, the user may be instructed to remove articles before the wash cycle commences or continues.
In some embodiments, the start of the wash cycle at 570 may be contingent on one or more predetermined conditions. As an example, it may be required that a door shuts within a predetermined time period (e.g., less than one minute, such as a period less than or equal to 30 seconds, 15 seconds, or 5 seconds) following 510 or 540 (e.g., measured in response to 510 or 540). For instance, the method 500 may include determining the door of the washing machine appliance is closed within the predetermined time period (e.g., at 550). In turn, 570 may be in response to determining the door is closed within the predetermined time period. If the door is not determined to close within the predetermined time period (e.g., determination of the door being closed within the predetermined time period fails), a user may be required to manually input a start signal (e.g., by pressing a button) at the control panel of the washing machine appliance in order to prompt 570.
Turning now to
The obtained images are then presented or displayed as a real-time feed of the camera assembly at the remote device (e.g., according to the received video signal). For instant, a constant or regularly refreshing set of live images from the camera assembly may be presented on the monitor or display of the remote device. Thus, a user viewing the remote device may be able to see the field of view being captured by the camera assembly (e.g., without having to repeatedly freeze the frame or provide any active input by a user on the remote device).
The images may be obtained using the camera assembly at any suitable time prior to initiating the wash cycle. For example, as best illustrated in
At 612, the method 600 includes guiding orientation of the camera assembly to a set camera angle.
Optionally, 612 may include detecting a fiducial reference on the external basket within the images of the real-time video feed. For instance, from the obtained images, the controller may identify the region corresponding to a predetermined portion of the external basket, which serves as the fiducial reference. Any suitable portion of the external basket may serve as the fiducial marker. In some embodiments, the fiducial reference is a top opening or surface of the external basket (e.g., as indicated in
As is understood, recognizing or identifying such fiducial references or portions of the external basket, may be performed by one or more image processing techniques or algorithms (e.g., executed at the controller of the remote device, remote server, or appliance). According to exemplary embodiments, image processing may include blur detection algorithms that are generally intended to compute, measure, or otherwise determine the amount of blur in an image. For example, these blur detection algorithms may rely on focus measure operators, the Fast Fourier Transform along with examination of the frequency distributions, determining the variance of a Laplacian operator, or any other methods of blur detection known by those having ordinary skill in the art. In addition, or alternatively, the image processing algorithms may use other suitable techniques for recognizing or identifying items or objects, such as edge matching or detection, divide-and-conquer searching, greyscale matching, histograms of receptive field responses, or another suitable routine (e.g., executed at the controller of the remote device, remote server, or appliance based on one or more captured images from one or more cameras). Other image processing techniques are possible and within the scope of the present subject matter. The processing algorithm may further include measures for isolating or eliminating noise in the image comparison, e.g., due to image resolution, data transmission errors, inconsistent lighting, or other imaging errors. By eliminating such noise, the image processing algorithms may improve accurate object detection, avoid erroneous object detection, and isolate the important object, region, or pattern within an image.
Additionally or alternatively, 612 may include overlaying a two-dimensional reference shape over the real-time video feed. The two-dimensional reference shape may generally correspond to the geometry of a portion of the external basket to which the camera assembly or field of view is intended to be aligned (e.g., the fiducial reference). As an example, the two-dimensional reference shape may be a rectangle, such as may correspond to an intended geometry of the opening of the external basket in a set angle of the camera. The position of the two-dimensional reference shape that is displayed or overlaid may be constant, even as the camera angle and obtained images change. Thus, a user may be guided to move the camera such that the fiducial reference aligns to (e.g., beneath) the overlaid two-dimensional reference shape. Separate from or in addition to the two-dimensional reference shape, the method 612 may provide for displaying movement guidance (e.g., in the form of pictorial or textual instructions, such as arrows or written messages) with the real-time feed (e.g., to help a user move the camera to align the two-dimensional reference shape with the fiducial reference).
Further additionally or alternatively, 612 may include receiving a plurality of angle readings may (e.g., from a measurement device of the remote device) to determine the position of the remote device (e.g., relative to the external basket or a fixed reference direction, axis, or point). Subsequently, determined position of the remote device may be determined to match the set camera angle, or at least a portion thereof (e.g., within a set tolerance or range, such as 10%).
If the set angle is met, such as may be indicated by using the plurality of angle readings or comparing the fiducial reference the two-dimensional reference shape, the method 600 may capture (e.g., select or record) at least one obtained image and proceed to 614.
At 614, the method 600 includes analyzing an obtained image (e.g., the above-described at least one obtained image that is captured from the one or more images). Specifically, the obtained image may be analyzed using a (e.g., machine learning) image recognition process to estimate a fill level of the load of clothes within the external basket. Variations in the fill level may generally change the visible height or volume of clothes within the external basket (e.g., as illustrated in
Various exemplary forms of image recognition are recited above (e.g., within the context of 540) and need not be repeated here, though one of ordinary skill would recognize one or more may be used at 614.
At 616, the method 600 includes selecting a wash cycle. The wash cycle may be selected, for instance, based on one or more default load settings (e.g., default load sizes), estimated load attributes, or inputs received from a user (e.g., at the control panel or remote device). Thus, 616 may include selecting an operating cycle parameter, adjusting a water or detergent fill amount, or providing a user notification. As used herein, an “operating parameter” of the washing machine appliance is any cycle setting, operating time, component setting, spin speed, part configuration, or other operating characteristic that may affect the performance of the washing machine appliance. In turn, references to operating parameter adjustments or “adjusting at least one operating parameter” are intended to refer to control actions intended to improve system performance based on the load characteristics. For example, adjusting an operating parameter may include adjusting an agitation time or an agitation profile, adjusting a water level, limiting a spin speed of the wash basket, etc. Other operating parameter adjustments are possible and within the scope of the present subject matter.
At 618, the method 600 includes evaluating a loading condition. Specifically, 618 may including monitoring one or more sensors or elements to determine if movement corresponding to loading clothing articles is detected (e.g., within a predetermined timeout period). For instance, 618 may include determining if one or more movement signals is received from the machine measurement device, such as an accelerometer signal indicating movement of the wash tub (e.g., above a set threshold). The predetermined timeout period may be counted, for instance, from any one of the prior steps 610 through 616, or from another suitable trigger event prior to 618. If a movement signal is received within the predetermined timeout period of 618, the method 600 may proceed to 620. By contrast, if no movement signal is received to indicate loading prior to expiration of the predetermined timeout period of 618, the method 600 may proceed to 622 wherein the washing machine is placed in an idle state (e.g., requiring direct user intervention before any further action is taken).
At 620, the method 600 includes evaluating a door condition. Specifically, 620 may include monitoring one or more sensor or elements to determine if the door to the washing machine appliance is closed (e.g., within a predetermined timeout period). For instance, 620 may include determining the door of the washing machine appliance is closed based on a signal from the latch assembly. The predetermined timeout period may be counted, for instance, from any one of the prior steps 610 through 616, from 618, or from another suitable trigger event prior to 620. If the door is detected as closed within the predetermined timeout period of 620, the method 600 may proceed to 624. If the door is not detected as closed prior to expiration of the predetermined timeout period of 620, the method 600 may proceed to 622 wherein the washing machine is placed in an idle state (e.g., requiring direct user intervention before any further action is taken).
At 624, the method 600 includes initiating the selected wash cycle. For instance, an initial volume of water may be directed to the wash tub (e.g., to wet the articles of clothing within the wash basket). The initial volume of water, the water temperature at which the initial volume of water is supplied, the inclusion of wash additive (if any), or any other applicable operating parameter at 624 may correspond to the selected wash cycle.
At 626, the method 600 includes evaluating the estimated fill level. Specifically, it may be determined if the estimated fill level has been matched to a specific estimated load size. For instance, it may be determined if the estimated fill level is within a set range of previous determination stored within a programmed look-up table, chart, graph, or formula. In such determinations, one or more estimated fill level categorizations or values are correlated to estimated load sizes. For instance, previous determinations may be stored in which a discrete fill level is confirmed to correlate to (e.g., be the equivalent of) a discrete estimated load size. In turn, the “new” estimated fill level from 614 may be found to be near to (e.g., within a set range from) the previously confirmed fill level, which correlates to a discrete estimated load size (e.g., out of a plurality of programmed load sizes). Thus, and in detail, 626 may include determining the estimated fill level of 614 is within the set range of a previously confirmed fill level. The discrete estimated load size may then match the estimated fill level of 614. If such a match is made, the method 600 may proceed directly to 630. If no match has been made, the method 600 may proceed to 628.
At 628, the method 600 includes directing a load estimation sequence within the washing machine appliance to generate an estimated load size. In other words, after the load of clothing is received within the wash basket (e.g., as confirmed at 618 and 620), the washing machine appliance may execute a load estimation sequence to determine the load size. Such sequences are generally known and may include, for instance, detecting movement of the wash tub, resistance to rotation of the wash basket, or measuring the weight of articles within the wash basket to generate a “new measured load size.” Once determined, the new measured load size may be correlated to the estimated fill level of 614. In turn, future operations in which a similar fill level is detected, the similar fill level may be quickly and notably matched to the new measured load size (e.g., without again having to perform a load estimation sequence within the washing machine appliance). Moreover, following the load estimation sequence, the method 600 may proceed to 630.
At 630, the method 600 includes directing the selected wash cycle within the washing machine appliance according on the estimated load size. Such direction may require adjusting one or more operating parameters of the washing machine appliance (e.g., as part of the selected wash cycle). Thus, 630 may include selecting an operating cycle parameter, adjusting a water or detergent fill amount, or providing a user notification. For example, adjusting an operating parameter may include adjusting an agitation time or an agitation profile, adjusting a water level, limiting a spin speed of the wash basket, etc. Other operating parameter adjustments are possible and within the scope of the present subject matter.
Optionally, adjusting the at least one operating parameter may include providing a user notification when a predetermined load attribute exists. For example, if 626 or 628 results in the estimation of an excessively large load size, the wash cycle may be restricted (e.g., stopped or otherwise prevented) and a user notification may be provided, e.g., via an indicator on the remote device or the control panel of the appliance. Thus, for example, if a user provides a load that is too large for the washing machine appliance safely wash, the user may be instructed to remove articles before the wash cycle continues.
This written description uses examples to disclose the invention, including the best mode, and also to enable any person skilled in the art to practice the invention, including making and using any devices or systems and performing any incorporated methods. The patentable scope of the invention is defined by the claims, and may include other examples that occur to those skilled in the art. Such other examples are intended to be within the scope of the claims if they include structural elements that do not differ from the literal language of the claims, or if they include equivalent structural elements with insubstantial differences from the literal languages of the claims.
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