Patent Application
20230343127
The present subject matter relates generally to household appliances, such as one or more laundry appliances, and more particularly to systems and methods for detecting protected users of such appliances.
Household appliances are utilized generally for a variety of tasks by a variety of users. For example, a household may include such appliances as laundry appliances, e.g., a washer and/or dryer, kitchen appliances, e.g., a refrigerator, a dishwasher, etc., along with room air conditioners and other various appliances.
In many situations, operation of a household appliance by certain users may be undesirable. For example, some household appliances may include features which generate high levels of heat, e.g., burners on a cooktop or oven appliance, and/or may include enclosable internal volumes, such as inside of a drum of a dryer appliance, a wash basket of a washing machine appliance, or a food storage compartment in a refrigerator or freezer appliance. Accordingly, many household appliances include components or features which are not desired for certain users, such as children or elderly, etc., to have unlimited access to.
Accordingly, an appliance with improved features for restricting or preventing protected users from operating the appliance unattended would be useful. More particularly, an appliance that is capable of identifying a protected user, and methods of identifying a protected user, would be useful.
Aspects and advantages of the invention will be set forth in part in the following description, or may be apparent from the description, or may be learned through practice of the invention.
In an exemplary embodiment, a method of operating a household appliance is provided. The household appliance includes a camera assembly operable to obtain an image. The method includes downloading a protected user detection software from a remote computing device to the household appliance. The method also includes unlocking a user interface of the household appliance and detecting a user at the household appliance after unlocking the user interface of the household appliance. The user is detected with the camera assembly. The method further includes determining whether the detected user is a protected user. The determination is made using the protected user detection software. The method also includes locking the user interface of the household appliance in response to determining that the detected user is the protected user.
In another exemplary embodiment, a household appliance is provided. The household appliance includes a camera assembly operable to obtain an image and a controller. The controller is operable for downloading a protected user detection software from a remote computing device to the household appliance. The controller is also operable for unlocking a user interface of the household appliance and detecting, with the camera assembly, a user at the household appliance after unlocking the user interface of the household appliance. The controller is further operable for determining, using the protected user detection software, whether the detected user is a protected user and locking the user interface of the household appliance in response to determining that the detected user is the protected user.
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.
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 or spirit 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.
Directional terms such as “left” and “right” are used herein with reference to the perspective of a user standing in front of a household appliance to access the appliance and/or items therein. Terms such as “inner” and “outer” refer to relative directions with respect to the interior and exterior of the appliance. For example, “inner” or “inward” refers to the direction towards the interior of the appliance. Terms such as “left,” “right,” “front,” “back,” “top,” or “bottom” are used with reference to the perspective of a user accessing the appliance. For example, a user stands in front of the appliance to open the door(s) and reaches into the appliance to add, move, or withdraw items therein.
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. As used herein, terms of approximation, such as “generally,” or “about” include values within ten percent greater or less than the stated value. 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. For example, “generally vertical” includes directions within ten degrees of vertical in any direction, e.g., clockwise or counter-clockwise.
Exemplary household appliances are illustrated in
According to various embodiments of the present disclosure, the household appliance 10 may take the form of any of the example laundry appliances described herein, or may be any other household appliance. Thus, it will be understood that the present subject matter is not limited to any particular household appliance.
It should be understood that “household appliance” and/or “appliance” are used herein to describe appliances typically used or intended for common domestic tasks, such as a laundry appliance, e.g., as illustrated in
As may be seen generally throughout
In various embodiments, the user interface panel 100 may represent a general purpose I/O (“GPIO”) device or functional block. In some embodiments, the user interface panel 100 may include or be in operative communication with user input device 102, such as one or more of a variety of digital, analog, electrical, mechanical or electro-mechanical input devices including rotary dials, control knobs, push buttons, and touch pads. The user interface panel 100 may include a display component 104, such as a digital or analog display device designed to provide operational feedback to a user. The display component 104 may also be a touchscreen capable of receiving a user input, such that the display component 104 may also be a user input device in addition to or instead of the user input device 102.
Generally, each appliance may include a controller 210 in operative communication with the user input device 102. The user interface panel 100 and the user input device 102 may be in communication with the controller 210 via, for example, one or more signal lines or shared communication busses. Input/output (“I/O”) signals may be routed between controller 210 and various operational components of the appliance. Operation of the appliance can be regulated by the controller 210 that is operatively coupled to the user interface panel 100. A user interface panel 100 may for example provide selections for user manipulation of the operation of an appliance, e.g., via user input device 102 and/or display 104. In response to user manipulation of the user interface panel 100 and/or user input device 102, the controller 210 may operate various components of the appliance. Controller 210 may include a memory and one or more microprocessors, CPUs or the like, such as general or special purpose microprocessors operable to execute programming instructions or micro-control code associated with operation of the appliance. The memory may represent random access memory such as DRAM, or read only memory such as ROM or FLASH. In one embodiment, the processor executes programming instructions stored in memory. The memory may be a separate component from the processor or may be included onboard within the processor. Alternatively, a controller 210 may be constructed without using a microprocessor, e.g., using a combination of discrete analog and/or digital logic circuitry (such as switches, amplifiers, integrators, comparators, flip-flops, AND gates, and the like) to perform control functionality instead of relying upon software.
The controller 210 may be programmed to operate the appliance by executing instructions stored in memory. For example, the instructions may be software or any set of instructions that when executed by the processing device, cause the processing device to perform operations. Controller 210 can include one or more processor(s) and associated memory device(s) configured to perform a variety of computer-implemented functions and/or instructions (e.g. performing the methods, steps, calculations and the like and storing relevant data as disclosed herein). It should be noted that controllers 210 as disclosed herein are capable of and may be operable to perform any methods and associated method steps as disclosed herein.
As generally seen throughout
Additional exemplary details of each laundry appliance are illustrated in
Referring again to
Wash basket 120 may define one or more agitator features that extend into wash chamber 126 to assist in agitation and cleaning of articles disposed within wash chamber 126 during operation of washing machine appliance 10. For example, as illustrated 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 10. 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 12 or any other suitable support according to alternative embodiments.
Referring again to
A spout 150 is configured for directing a flow of fluid into wash tub 124. For example, spout 150 may be in fluid communication with a water supply (not shown) in order to direct fluid (e.g., clean water) into wash tub 124. Spout 150 may also be in fluid communication with the sump 142. For example, pump assembly 144 may direct wash fluid disposed in sump 142 to spout 150 in order to circulate wash fluid in wash tub 124.
As illustrated in
Additionally, a bulk reservoir 154 is disposed within cabinet 12. Bulk reservoir 154 is also configured for receipt of fluid additive for use during operation of washing machine appliance 10. Bulk reservoir 154 is sized such that a volume of fluid additive sufficient for a plurality or multitude of wash cycles of washing machine appliance 10 (e.g., five, ten, twenty, fifty, or any other suitable number of wash cycles) may fill bulk reservoir 154. Thus, for example, a user can fill bulk reservoir 154 with fluid additive and operate washing machine appliance 10 for a plurality of wash cycles without refilling bulk reservoir 154 with fluid additive. A reservoir pump 156 is configured for selective delivery of the fluid additive from bulk reservoir 154 to wash tub 124.
During operation of washing machine appliance 10, e.g., during a wash cycle of the washing machine appliance 10, a laundry items are loaded into wash basket 120 through opening 132, and washing operation is initiated through operator manipulation of input selectors 102. Wash tub 124 is filled with water, detergent, and/or other fluid additives, e.g., via spout 150 and/or detergent drawer 152. One or more valves (not shown) can be controlled by washing machine appliance 10 to provide for filling wash basket 120 to the appropriate level for the amount of articles being washed and/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 and/or after the rinse cycle in order to wring wash fluid from the articles being washed. During a spin cycle, basket 120 is rotated at relatively high speeds. After articles disposed in wash basket 120 are cleaned and/or washed, 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.
While described in the context of a specific embodiment of horizontal axis washing machine appliance 10, using the teachings disclosed herein it will be understood that horizontal axis washing machine appliance 10 is provided by way of example only. It should be appreciated that the present subject matter is not limited to any particular style, model, or configuration of washing machine appliance. Other washing machine appliances having different configurations, different appearances, and/or different features may also be utilized with the present subject matter as well, e.g., vertical axis washing machine appliances.
Cabinet 12 includes a front side 22 and a rear side 24 spaced apart from each other along the transverse direction T. Within cabinet 12, an interior volume 29 is defined. A drum or container 26 is mounted for rotation about a substantially horizontal axis within the interior volume 29. Drum 26 defines a chamber 25 for receipt of articles of clothing for tumbling and/or drying. Drum 26 extends between a front portion 37 and a back portion 38. Drum 26 also includes a back or rear wall 34, e.g., at back portion 38 of drum 26. A supply duct 41 may be mounted to rear wall 34 and receives heated air that has been heated by a heating assembly or system 40.
As used herein, the terms “clothing” or “articles” includes but need not be limited to fabrics, textiles, garments, linens, papers, or other items from which the extraction of moisture is desirable. Furthermore, the term “load” or “laundry load” refers to the combination of clothing or articles that may be washed together in a washing machine or dried together in a dryer appliance 11 (e.g., clothes dryer) and may include a mixture of different or similar articles of clothing of different or similar types and kinds of fabrics, textiles, garments and linens within a particular laundering process.
A motor 31 is provided in some embodiments to rotate drum 26 about the horizontal axis, e.g., via a pulley and a belt (not pictured). Drum 26 is generally cylindrical in shape, having an outer cylindrical wall 28 and a front flange or wall 30 that defines an opening 32 of drum 26, e.g., at front portion 37 of drum 26, for loading and unloading of articles into and out of chamber 25 of drum 26. A plurality of lifters or baffles 27 are provided within chamber 25 of drum 26 to lift articles therein and then allow such articles to tumble back to a bottom of drum 26 as drum 26 rotates. Baffles 27 may be mounted to drum 26 such that baffles 27 rotate with drum 26 during operation of dryer appliance 11.
The rear wall 34 of drum 26 may be rotatably supported within the cabinet 12 by a suitable fixed bearing. Rear wall 34 can be fixed or can be rotatable. Rear wall 34 may include, for instance, a plurality of holes that receive hot air that has been heated by heating system 40. The heating system 40 may include, e.g., a heat pump, an electric heating element, and/or a gas heating element (e.g., gas burner). Moisture laden, heated air is drawn from drum 26 by an air handler, such as blower fan 48, which generates a negative air pressure within drum 26. The moisture laden heated air passes through a duct 44 enclosing screen filter 46, which traps lint particles. As the air passes from blower fan 48, it enters a duct 50 and then is passed into heating system 40. In some embodiments, the dryer appliance 11 may be a conventional dryer appliance, e.g., the heating system 40 may be or include an electric heating element, e.g., a resistive heating element, or a gas-powered heating element, e.g., a gas burner. In other embodiments, the dryer appliance may be a condensation dryer, such as a heat pump dryer. In such embodiments, heating system 40 may be or include a heat pump including a sealed refrigerant circuit. Heated air (with a lower moisture content than was received from drum 26), exits heating system 40 and returns to drum 26 by duct 41. After the clothing articles have been dried, they are removed from the drum 26 via opening 32. A door (
In some embodiments, one or more selector inputs 102, such as knobs, buttons, touchscreen interfaces, etc., may be provided or mounted on the cabinet 12 (e.g., on a backsplash 71) and are in operable communication (e.g., electrically coupled or coupled through a wireless network band) with the processing device or controller 210. Controller 210 may also be provided in operable communication with components of the dryer appliance 11 including motor 31, blower 48, or heating system 40. In turn, signals generated in controller 210 direct operation of motor 31, blower 48, or heating system 40 in response to the position of inputs 102. As used herein, “processing device” or “controller” may refer to one or more microprocessors, microcontroller, ASICS, or semiconductor devices and is not restricted necessarily to a single element. The controller 210 may be programmed to operate dryer appliance 11 by executing instructions stored in memory (e.g., non-transitory media). The controller 56 may include, or be associated with, one or more memory elements such as RAM, ROM, or electrically erasable, programmable read only memory (EEPROM). For example, the instructions may be software or any set of instructions that when executed by the processing device, cause the processing device to perform operations. It should be noted that controllers as disclosed herein are capable of and may be operable to perform any methods and associated method steps as disclosed herein. For example, in some embodiments, methods disclosed herein may be embodied in programming instructions stored in the memory and executed by the controller 210.
User interface panel 100, including user input device 102 and display 104 thereon, provides a user interface, e.g., a means for users to communicate with and operate the household appliance 10. It will be appreciated that other components or devices that provide for communication with household appliance 10 for operating household appliance 10 may also be included in the user interface. For example, the user interface may include a speaker, a microphone, a camera (still or video) or motion detection camera for detecting a user’s proximity to household appliance 10 or for picking up certain motions, and/or other user interface elements in various combinations.
As will be described in more detail below, household appliance 10 may further include features that are generally configured to detect the presence and identity of a user, in particular of a protected user, such as one of a group of protected users. More specifically, such features may include one or more sensors, e.g., cameras 192 (see, e.g.,
As shown schematically in
Although a single camera 192 is illustrated in
In some embodiments, it may be desirable to activate the camera or cameras 192 for limited time durations and only in response to certain triggers. For example, a proximity sensor, such as an infrared (IR) camera, may be provided such that the camera 192 is only activated after the proximity sensor detects motion at the front of the household appliance 10. In additional embodiments, the activation of the camera 192 may be in response to an interaction with the household appliance, such as a door opening, such as detecting that a door of the household appliance 10, such as the door 134 of the washing machine appliance or the door of the dryer appliance, was opened using a door switch, or an interaction with the user interface, such as pressing a button or touching a touchscreen control, etc. In this manner, privacy concerns related to obtaining images of the user of the household appliance 10 may be mitigated. According to exemplary embodiments, camera assembly 190 may be used to facilitate a user detection and/or identification process for the household appliance 10. As such, each camera 192 may be positioned and oriented to monitor one or more areas of the household appliance 10 and adjoining areas, such as while a user is accessing or attempting to access the household appliance 10.
It should be appreciated that according to alternative embodiments, camera assembly 190 may include any suitable number, type, size, and configuration of camera(s) 192 for obtaining images of any suitable areas or regions within or around household appliance 10. In addition, it should be appreciated that each camera 192 may include features for adjusting the field of view and/or orientation.
It should be appreciated that the images obtained by camera assembly 190 may vary in number, frequency, angle, resolution, detail, etc. in order to improve the clarity of the particular regions surrounding or within household appliance 10. In addition, according to exemplary embodiments, controller 210 may be configured for illuminating the household appliance 10 and/or surrounding areas using one or more light sources prior to obtaining images. Notably, controller 210 of household appliance 10 (or any other suitable dedicated controller) may be communicatively coupled to camera assembly 190 and may be programmed or configured for analyzing the images obtained by camera assembly 190, e.g., in order to detect and/or identify a user proximate to the household appliance 10, as described in more detail below.
In general, controller 210 may be operably coupled to camera assembly 190 for analyzing one or more images obtained by camera assembly 190 to extract useful information regarding objects or people within the field of view of the one or more cameras 192. In this regard, for example, images obtained by camera assembly 190 may be used to extract a facial image or other identifying information related to one or more users. Notably, this analysis may be performed locally (e.g., on controller 210) or may be transmitted to a remote server (e.g., in the “cloud,” as those of ordinary skill in the art will recognize as referring to a remote server or database in a distributed computing environment including at least one remote computing device) for analysis. Such analysis is intended to facilitate user detection, e.g., by identifying a user accessing the household appliance, such as a user who may be operating, e.g., activating or adjusting, one or more components of the household appliance 10 or otherwise accessing the household appliance 10. As will be described in more detail below, such identification may also include determining whether the user is a protected user such as a child, an elderly or infirm person, a disabled person, etc. Additionally, it should be noted that the protected user or users are not necessarily only people, for example, animals such as a dog or cat may also be included in the group of protected users.
Specifically, according to an exemplary embodiment as illustrated in
Notably, camera assembly 190 may obtain images upon any suitable trigger, such as a time-based imaging schedule where camera assembly 190 periodically images and monitors the field of view, e.g., in and/or in front of the household appliance 10. According to still other embodiments, camera assembly 190 may periodically take low-resolution images until motion (such as approaching the household appliance 10, opening a door thereof, or reaching for one of the controls or user inputs thereof) is detected (e.g., via image differentiation of low-resolution images), at which time one or more high-resolution images may be obtained. According to still other embodiments, household appliance 10 may include one or more motion sensors (e.g., optical, acoustic, electromagnetic, etc.) that are triggered when an object or user moves into or through the area in front of the household appliance 10, and camera assembly 190 may be operably coupled to such motion sensors to obtain images of the object during such movement. In some embodiments, the camera assembly 190 may only obtain images when the household appliance is activated, as will be understood by those of ordinary skill in the art. Thus, for example, when the household appliance 10 is operating, the camera assembly 190 may then continuously or periodically obtain images, or may apply the time-based imaging schedule, motion detection based imaging, or other imaging routines/schedules throughout the time that the household appliance 10 is operating.
It should be appreciated that the images obtained by camera assembly 190 may vary in number, frequency, angle, resolution, detail, etc. in order to improve the clarity thereof. In addition, according to exemplary embodiments, controller 210 may be configured for illuminating a light (not shown) while obtaining the image or images. Other suitable imaging triggers are possible and within the scope of the present subject matter.
Turning now to
The household appliance 10 may be in communication with the remote user interface device 1000 device through various possible communication connections and interfaces. The household appliance 10 and the remote user interface device 1000 may be matched in wireless communication, e.g., connected to the same wireless network. The household appliance 10 may communicate with the remote user interface device 1000 via short-range radio such as BLUETOOTH® or any other suitable wireless network having a layer protocol architecture. As used herein, “short-range” may include ranges less than about ten meters and up to about one hundred meters. For example, the wireless network may be adapted for short-wavelength ultra-high frequency (UHF) communications in a band between 2.4 GHz and 2.485 GHz (e.g., according to the IEEE 802.15.1 standard). In particular, BLUETOOTH® Low Energy, e.g., BLUETOOTH® Version 4.0 or higher, may advantageously provide short-range wireless communication between the household appliance 10 and the remote user interface device 1000. For example, BLUETOOTH® Low Energy may advantageously minimize the power consumed by the exemplary methods and devices described herein due to the low power networking protocol of BLUETOOTH® Low Energy.
The remote user interface device 1000 is “remote” at least in that it is spaced apart from and not physically connected to the household appliance 10, e.g., the remote user interface device 1000 is a separate, stand-alone device from the household appliance 10 which communicates with the household appliance 10 wirelessly. Any suitable device separate from the household appliance 10 that is configured to provide and/or receive communications, information, data, or commands from a user may serve as the remote user interface device 1000, such as a smartphone (e.g., as illustrated in
The remote user interface device 1000 may include a memory for storing and retrieving programming instructions. Thus, the remote user interface device 1000 may provide a remote user interface which may be an additional user interface to the user interface panel 100. For example, the remote user interface device 1000 may be a smartphone operable to store and run applications, also known as “apps,” and the additional user interface may be provided as a smartphone app.
As mentioned above, the household appliance 10 may also be configured to communicate wirelessly with a network 1100. The network 1100 may be, e.g., a cloud-based data storage system including one or more remote computing devices such as remote databases and/or remote servers, which may be collectively referred to as “the cloud.” For example, the household appliance 10 may communicate with the cloud 1100 over the Internet, which the household appliance 10 may access via WI-FI®, such as from a WI-FI® access point in a user’s home.
Exemplary methods for operating a household appliance, such as a laundry appliance, as described above, are provided. In this regard, for example, a controller of the household appliance, e.g., controller 210, may be configured for implementing some or all steps of one or more of the following exemplary methods. However, it should be appreciated that the exemplary methods are discussed herein only to describe exemplary aspects of the present subject matter, and are not intended to be limiting.
An exemplary method 300 of operating a household appliance is illustrated in
As illustrated in
The database of face images may then be used to build protected user detection software as indicated at 306 in
As illustrated at steps 310, 312, and 314 in
When the user interface of the household appliance 10 is unlocked, e.g., after the unlocking at step 310 in
As long as at least one authorized user is present, e.g., when the detection at step 312 is positive, the user interface may remain unlocked, e.g., method 300 may loop back to step 310 as illustrated in
When an authorized user is not detected at step 312, the method 300 may continue to a step 314 of detecting one or more protected users, e.g., people. If a protected user, e.g., person, is not detected at step 314, the method 300 may continue to iterate and continue to monitor for users as long as the user interface is unlocked, e.g., when the determination at step 314 in
When a protected user is detected, e.g., when the determination at step 330 in
When a protected user is detected, and an authorized user is not also detected, e.g., when the outcome of step 312 is negative and the outcome of step 314 is positive as illustrated in
For example, the alarm may be a local alarm, e.g., on the household appliance. In particular, the local alarm may deter or repel the protected users from touching the household appliance, and/or may encourage the protected users to move away from the household appliance.
When the number of protected users detected is less than or equal to the threshold, such as when only one protected user is detected, method 300 may then bypass the alarm step 322 and proceed to a verification step 324.
Still referring to
If, however, the protected user detection was not correct, e.g., when the determination at step 324 (such as the response to the confirmation request) is negative, then method 300 may include improving and/or updating the protected user detection software to reduce or avoid future false positives. An incorrect detection may include, for example, identifying an unprotected user as a protected user or other false positive at the protected user detection step 314 (where an unprotected user may be an authorized user or an unidentified user whose face has not been scanned and entered into the database as described above with respect to steps 304 and 306). The incorrect detection may be a learning opportunity, e.g., as described in the following, after being notified of the incorrect detection, the method may include updating or rebuilding the protected user detection software with data corresponding to the false positive, such that the household appliance learns from the incorrect detection and improves the protected user detection after the incorrect detection. For example, when the result of step 324 is negative, the method 300 may then proceed to a step 326 of rebuilding or updating the protected user detection software, e.g., by a remote computing device such as in the cloud. For example, the image or images obtained at steps 312 and/or 314 may be transmitted from the household appliance to the remote computing device at step 326, and such transmitted image or images may be used to rebuild the protected user detection software in response to the negative response received at the verification/confirmation step 324. For example, rebuilding the user detection software may include re-training a machine learning image recognition model (e.g., neural network), or otherwise updating and/or replacing an image processing, image analysis, and/or image recognition algorithm, examples of which are described in more detail below.
After rebuilding the protected user detection software, the new detection software, such as a new or updated version of the protected user detection software, may be downloaded to the household appliance, e.g., as indicated at step 328 in
In some embodiments, method 300 may further include steps 330 and 332 of unlocking the household appliance by or in response to the authorized user. For example, the user interface of household appliance may remain locked until unlocked by a verified authorized user. The authorized user may be verified by biometric data from the authorized user, such as the voice of the authorized user recorded at step 302. The authorized user may also or instead be verified by the remote user interface device (see, e.g., remote user interface device 1000 of
Turning now to
As shown in
Method 400 may also include a step 420 of unlocking a user interface of the household appliance. For example, unlocking the user interface may include permitting the household appliance, e.g., the controller 210 thereof, to activate one or more mechanical components of the household appliance in response to a user input received at the user interface panel 100.
Still referring to
Method 400 may further include a step 440 of determining that the detected user is a protected user. Such determination may be made using the protected user detection software. When the detected user is a protected user, method 400 may then include a step 450 of locking the user interface of the household appliance, such as by disabling user inputs, e.g., on the user interface panel 100 of the household appliance 10, whereby the household appliance, such as mechanical components thereof (e.g., one or more heating elements, pumps, and/or motors) will not be activated in response to inputs or manipulation (e.g., button pressing) of the user input devices or user interface.
For example, steps 430 and 440 may include, and/or the household appliance may be configured for, detecting or identifying one or more users, e.g., based on one or more images. In some embodiments, detection of the user(s) may be accomplished with the camera assembly 190. For example, the household appliance may include a camera, and the method 400 may include, and/or the household appliance may be configured for, capturing an image with the camera and detecting the user(s) based on the image captured by the camera. The structure and operation of cameras are understood by those of ordinary skill in the art and, as such, the camera is not illustrated or described in further detail herein for the sake of brevity and clarity. In such embodiments, the controller 210 of the household appliance 10 may be configured for image-based processing, e.g., to detect a user and identify the user, e.g., determine whether the user is an authorized user, a protected user, or an unidentified user (e.g., neither an authorized user nor a protected user, such as not in the facial recognition database described above) based on an image of the user, e.g., a photograph taken with the camera(s) 192 of the camera assembly 190. For example, the controller 210 may be configured to identify the user by comparison of the image to a stored image of a known or previously-identified user. For example, controller 210 of household appliance 10 (or any other suitable dedicated controller) may be communicatively coupled to camera assembly 190 and may be programmed or configured for analyzing the images obtained by camera assembly 190, e.g., in order to detect a user accessing or proximate to household appliance 10 and to identify the user, e.g., determine whether the user is an authorized user or a protected user.
In some exemplary embodiments, the method 400 may include analyzing one or more images to detect and identify a user. It should be appreciated that this analysis may utilize any suitable image analysis techniques, image decomposition, image segmentation, image processing, etc. This analysis may be performed entirely by controller 210, may be offloaded to a remote server (e.g., in the cloud 1100) for analysis, may be analyzed with user assistance (e.g., via user interface panel 100), or may be analyzed in any other suitable manner. According to exemplary embodiments of the present subject matter, the analysis may include a machine learning image recognition process.
According to exemplary embodiments, this image analysis may use any suitable image processing technique, image recognition process, etc. As used herein, the terms “image analysis” and the like may be used generally to refer to any suitable method of observation, analysis, image decomposition, feature extraction, image classification, etc. of one or more images, videos, or other visual representations of an object. As explained in more detail below, this image analysis may include the implementation of image processing techniques, image recognition techniques, or any suitable combination thereof. In this regard, the image analysis may use any suitable image analysis software or algorithm to constantly or periodically monitor household appliance 10 and/or a proximate and contiguous area in front of the household appliance 10. It should be appreciated that this image analysis or processing may be performed locally (e.g., by controller 210) or remotely (e.g., by offloading image data to a remote server or network, e.g., in the cloud).
Specifically, the analysis of the one or more images may include implementation of an image processing algorithm. As used herein, the terms “image processing” and the like are generally intended to refer to any suitable methods or algorithms for analyzing images that do not rely on artificial intelligence or machine learning techniques (e.g., in contrast to the machine learning image recognition processes described below). For example, the image processing algorithm may rely on image differentiation, e.g., such as a pixel-by-pixel comparison of two sequential images. This comparison may help identify substantial differences between the sequentially obtained images, e.g., to identify movement, the presence of a particular object, the existence of a certain condition, etc. For example, one or more reference images may be obtained when a particular condition exists, and these references images may be stored for future comparison with images obtained during appliance operation. In a particular example, the reference images may be images of the face or faces of one or more authorized users and of one or more protected users, e.g., in a database as described above, such that the extant particular condition in the reference images is the presence of an authorized user and/or of a protected user. Similarities and/or differences between the reference image and the obtained image may be used to extract useful information for improving appliance performance. For example, image differentiation may be used to determine when a pixel level motion metric passes a predetermined motion threshold.
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 (the term “object” is used broadly herein to include humans, e.g., users of the household appliance and authorized or protected users in particular). In addition, or alternatively, the image processing algorithms may use other suitable techniques for recognizing or identifying particular items or objects, such as edge matching, divide-and-conquer searching, greyscale matching, histograms of receptive field responses, or another suitable routine (e.g., executed at the controller 210 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.
In addition to the image processing techniques described above, 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, and/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, the image recognition process may include the implementation of 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 or region of an image. In this regard, a “region proposal” may be one or more regions in an image that could belong to a particular object (e.g., a human or animal face, such as the face of an authorized user and/or of a protected user) or may include adjacent regions that share common pixel characteristics. A convolutional neural network is then used to compute features from the region 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, as opposed to a regular R-CNN architecture. For example, mask R-CNN may be based on fast R-CNN which is slightly different than R-CNN. For example, R-CNN first applies a convolutional neural network (“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 obtain, identify, or detect any other qualitative or quantitative data related to one or more objects or regions within the one or more images. In addition, a K-means algorithm may be used.
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 steps of detecting and identifying a user may include analyzing the one or more images 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 methods 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, such as by different users. This training data may be stored locally or remotely and may be communicated to a remote server for training other appliances and models.
It should be appreciated that image processing and machine learning image recognition processes may be used together to facilitate improved image analysis, object 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.
In some embodiments, the analysis of the image and user identification, e.g., the determination of whether the detected user is a protected user (or authorized user or unidentified user), may be performed using a protected user detection software. The protected user detection software may be built by a remote server, e.g., in the cloud, and may further be updated and/or re-built with additional inputs at subsequent user detections. For example, the protected user detection software may be trained using one or more user inputs. Thus, in some embodiments, e.g., at initial or prior user detection events, the determination that the detected user is a protected user may include receiving a user input that indicates the detected user is a protected user. Such user input may include a user confirmation provided in response to the notification, such as a confirmation or verification that the protected user was identified correctly.
When the household appliance receives such user input(s) and thus determines that the user is a protected user, the household appliance may then gather data, e.g., obtain images with one or more cameras. The household appliance may also or instead gather such data in response to an incorrect detection. The gathered data may be used to rebuild or update the protected user detection software. For example, the protected user detection software may be built by a remote server, e.g., in the cloud, and downloaded by the household appliance, such as transmitted from the remote server and received by the household appliance. Then, at a subsequent protected user detections (which may be determined automatically, e.g., by analyzing sensor input such as camera images, and/or based on manual user input) additional data may be gathered and such additional data may be sent to the cloud, such as transmitted from the household appliance and received by the remote server. The remote server may then use the additional data to update and/or rebuild the protected user detection software. The updated protected user detection software may then be transmitted to, e.g., re-downloaded by, the household appliance. Accordingly, the protected user detection software may be continuously updated and the accuracy of the protected user detection software may be continuously improved with additional data. In particular, the remote server may be in communication with numerous household appliances, may receive data from multiple of the household appliances, and may update the protected user detection software based on all the data from the multiple household appliances.
Thus, in some embodiments, method 400 may also include transmitting the input obtained from the camera at step 430 to a remote server from the household appliance after receiving the user input. In such embodiments, method 400 may further include building a protected user detection software by the remote server based on the input obtained from the camera. The protected user detection software may then be transmitted from the remote server to the household appliance. For example, method 400 may include steps of receiving biometric data for one or more authorized users and receiving biometric data for one or more protected users. The biometric data for both classes of users may include facial recognition images. The biometric data for at least the authorized users may also include a voice print or voice recognition data. The biometric data for both classes of users may then be transmitted to the cloud, e.g., one or more remote computing devices. For example, method 400 may also include transmitting the received biometric data for the one or more authorized users and the received biometric data for the one or more protected users to the remote computing device. Method 400 may also, in some embodiments, include building, by the remote computing device, the protected user detection software, such as using the transmitted biometric data to build (or re-build or update) the protected user detection software.
Further embodiments may include both initially downloading the protected user detection software from the remote server prior to detecting the protected user, followed by uploading the input obtained at step 430, e.g., transmitting the images obtained from the camera, to the remote server from the household appliance after correctly or incorrectly identifying the protected user (by analyzing the input locally and/or by receiving a user input indicating that the detected user is a protected user). Thus, the protected user detection software may then be updated or rebuilt by the remote server, and the updated or rebuilt protected user detection software may be downloaded by the household appliance for use in a subsequent operation.
In some exemplary embodiments, method 400 may further include sending a notification to a remote user interface device after determining that the detected user is a protected user. The method 400 may then include receiving a response to the notification. In particular, when the response is a negative response, e.g., when the response is or includes an incorrect detection input (such as a selection of “NO” in response to a prompt), method 400 may include, after receiving the incorrect decision input, transmitting the one or more images to the remote computing device from the household appliance. In such embodiments or instances, the method 400 may then include updating the protected user detection software by the remote computing device based on the one or more transmitted (e.g., uploaded) images and then downloading the updated protected user detection software from the remote computing device to the household appliance.
In some exemplary embodiments, method 400 may also include an alarm step in response to more than one protected user. For example, the user detected at step 430 as described above may be a first user and may further be a first protected user, e.g., as determined at step 440 as described above. In such instances, method 400 may further include detecting a second user at the household appliance after unlocking the user interface of the household appliance. The second user may be detected with the camera assembly, e.g., in the same or similar manner as the first user as described above. When the second user is detected, the method 400 may also include determining that the second detected user is a second protected user, e.g., using the protected user detection software to identify the second user as a protected user. When more than one protected user is detected, e.g., after determining that the second detected user is the second protected user, the method 400 may include activating an alarm.
In some embodiments, the household appliance may remain unlocked as long as at least one authorized user is present, e.g., it may be presumed that the authorized user is supervising or monitoring any protected users that may also be present. Thus, for example, the step 430 of detecting the user may include detecting more than one user. In such embodiments, the step 440 of determining whether the detected user is the protected user may include determining whether each detected user is a protected user. In such embodiments, the step 450 of locking the user interface may only be performed when every detected user is a protected user, such as when two users are detected and both detected users are protected users. As a contrasting example, when two or more users are detected and at least one detected user is an authorized user, then the user interface of the household appliance may remain unlocked, for example when three users are detected and at least one is an authorized user, even when the other two (or more) detected users are protected users.
In some embodiments, method 400 may also include secure unlocking of the user interface after locking the user interface at step 450. For example, method 400 may further include receiving an unlock command. The unlock command may be, for example, a voice command or may be a mobile command (e.g., received from a remote user interface device), as described above with reference to step 330 in
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.
