Patent Description:
Consumers are increasingly focused on health and health-related products. We focus on our weight, what we eat, how we stand, and so on. Thus, a need exists for a multi-use apparatus that generates health-related information of a user. The present disclosure is directed to addressing these needs and solving other problems.

According to some implementations of the present disclosure, a method for determining a normalized weight of a non-static item is disclosed. Weight data associated with the non-static item is received from a plurality of load cells. A load cell weight for the non-static item is determined based at least in part on the weight data. The load cell weight for the non-static item is received as an input for a machine learning algorithm. The normalized weight for the non-static item is generated as an output for the machine learning algorithm.

In some implementations, the machine learning algorithm further receives, as the input, a category of the non-static item. In some implementations, the category of the non-static item includes a person, an animal, an inanimate object, or any combination thereof.

In some implementations, the category of the non-static item further includes a cat, a dog, a horse, a hamster, a guinea pig, a rabbit, a chinchilla, a mouse, a rat, a parrot, a hermit crab, a ferret, a reptile, a fish, a sea monkey, or any combination thereof.

In some implementations, historical data associated with the non-static item is received. The historical data includes historical load cell weight data and historical normalized weight data. The machine learning algorithm is trained with the historical data. In some implementations, the historical data is associated with other non-static items of a same category. In some implementations, the historical data is associated with the non-static item of a smart scale system, which includes the plurality of load cells.

In some implementations, the plurality of load cells is configured to generate the weight data in response to the non-static item engaging a smart scale system, which includes the plurality of load cells. In some implementations, the non-static item engaging the smart scale system includes (i) the non-static item standing on a cover layer of the smart scale system, (ii) the non-static item moving across the cover layer of the smart scale system, or (iii) both.

According to the invention, pressure data associated with the non-static item is received from an array of pressure sensors. The array of pressure sensors is configured to generate the pressure data in response to the non-static item engaging a smart scale system, which includes the array of pressure sensors.

According to the invention, a pressure heat map associated with the non-static item is generated based at least in part on the pressure data. The pressure heat map is representative of a pressure gradient associated with feet or paws of the non-static item and indicative of a weight distribution of the non-static item.

In some implementations, the array of pressure sensors is coupled to a mattress of the non-static item. The pressure data during a sleep session of the non-static item is received from the array of pressure sensors. Based at least in part on the pressure data during the sleep session of the non-static item, a sleep status for the non-static item is determined. In some implementations, the sleep status for the non-static item includes (i) whether the non-static item has a sleep disorder, (ii) a sleep quality of the non-static item, or (iii) both.

In some implementations, the weight data during the sleep session of the non-static item is received from the plurality of load cells. Based at least in part on the weight data during the sleep session of the non-static item, a change in weight for the non-static item during the sleep session is determined.

According to some implementations of the present disclosure, a smart scale system includes a plurality of load cells, a control system, and a memory. The plurality of load cells is coupled to a first side of a substrate. The plurality of load cells is configured to generate weight data associated with a non-static item. The control system includes one or more processors. The memory stores thereon machine readable instructions. The control system is coupled to the memory. In some implementations, the memory and the control system are coupled to the first side of the substrate. Any combination of the methods above is implemented when the machine executable instructions in the memory are executed by at least one of the one or more processors of the control system.

In some implementations, the smart scale system further includes a cover layer. In some implementations, the cover layer includes a sheet of fabric. In some implementations, the sheet of fabric includes at least two electrically conductive fabric portions spaced from each other. In some implementations, the at least two electrically conductive fabric portions are spaced from each other at least <NUM> inches.

In some implementations, the substrate is one or more pieces of glass. In some implementations, the substrate includes two pieces of glass coupled together via one or more hinges. In some implementations, the memory and the control system are coupled to the first side of the substrate.

In some implementations, the smart scale system further includes a plurality of rigid feet. In some implementations, each of the plurality of rigid feet is directly coupled to a respective one of the plurality of load cells.

In some implementations, the smart scale system further includes a base cover. The base cover is coupled to the substrate such that the plurality of load cells, the memory, and the control system are at least partially positioned between the base cover and the substrate. In some implementations, the base cover includes a plurality of apertures, and each of the plurality of rigid feet protrudes at least partially through at least one of the plurality of apertures.

In some implementations, the plurality of load cells includes a four-by-four array of load cells. The four-by-four array of load cells being is to an analog to digital converter. In some implementations, the plurality of load cells includes at least four single load cells, where each of the four single load cells is coupled to a respective analog to digital converter.

In some implementations, the smart scale system further includes an array of pressure sensors coupled to a second opposing side of the substrate. The array of pressure sensors is configured to generate pressure data associated with the non-static item.

In some implementations, the array of pressure sensors includes a first sheet and a second sheet. In some implementations, the first sheet includes a pressure sensitive sheet that is positioned adjacent to the second sheet. In some implementations, the pressure sensitive sheet includes a piezoresistive sheet that is configured to change its electrical resistance in response to pressure being applied thereto.

In some implementations, the second sheet includes a plurality of electrically conductive trace patterns. In some implementations, each of the plurality of electrically conductive trace patterns defines a pressure sensor of the array of pressure sensors. In some implementations, each of the plurality of electrically conductive trace patterns includes an inner disk and an outer ring. In some implementations, the outer ring is an equilateral polygon or a perfect circle.

According to some implementations of the present disclosure, a system for determining a normalized weight of a non-static item is disclosed. The system includes a control system configured to implement the method of any one of claims <NUM> to <NUM>.

According to some implementations of the present disclosure, a computer program product includes instructions which, when executed by a computer, cause the computer to carry out the method of any one of claims <NUM> to <NUM>. In some implementations, the computer program product is a non-transitory computer readable medium.

The foregoing and additional aspects and implementations of the present disclosure will be apparent to those of ordinary skill in the art in view of the detailed description of various embodiments and/or implementations, which is made with reference to the drawings, a brief description of which is provided next.

Relevant prior art documents for the present invention are <CIT>, <CIT> and <CIT>.

None of these prior art documents discloses that pressure data associated with the non-static item is received from an array of pressure sensors and that based at least in part on the pressure data, a pressure heat map associated with the non-static item is generated, wherein the pressure heat map is representative of a pressure gradient associated with feet or paws of the non-static item and indicative of a weight distribution of the non-static item.

The foregoing and other advantages of the present disclosure will become apparent upon reading the following detailed description and upon reference to the drawings.

While the present disclosure is susceptible to various modifications and alternative forms, specific embodiments have been shown by way of example in the drawings and will be described in detail herein. It should be understood, however, that the present disclosure is not intended to be limited to the particular forms disclosed. Rather, the present disclosure is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the present disclosure as defined by the appended claims.

According to some implementations of the present disclosure, a smart scale for a user to stand on can determine the user's posture, pressure points, weight, and more. At least two different types of pressure sensors can be used: a CMOS sensor and a sensor comprising a thin layer of liquid.

The present disclosure is described with reference to the attached figures, where like reference numerals are used throughout the figures to designate similar or equivalent elements. The figures are not drawn to scale, and are provided merely to illustrate the instant disclosure. Several aspects of the disclosure are described below with reference to example applications for illustration. It should be understood that numerous specific details, relationships, and methods are set forth to provide a full understanding of the disclosure. One having ordinary skill in the relevant art, however, will readily recognize that the disclosure can be practiced without one or more of the specific details, or with other methods. In other instances, well-known structures or operations are not shown in detail to avoid obscuring the disclosure. The present disclosure is not limited by the illustrated ordering of acts or events, as some acts may occur in different orders and/or concurrently with other acts or events. Furthermore, not all illustrated acts or events are required to implement a methodology in accordance with the present disclosure.

The term "coupled" is defined as connected, whether directly or indirectly through intervening components, and is not necessarily limited to physical connections. The connection can be such that the objects are permanently connected or releasably connected. The term "substantially" is defined to be essentially conforming to the particular dimension, shape or other word that substantially modifies, such that the component need not be exact. For example, substantially cylindrical means that the object resembles a cylinder, but can have one or more deviations from a true cylinder. The term "comprising" means "including, but not necessarily limited to"; it specifically indicates open-ended inclusion or membership in a so-described combination, group, series and the like.

Aspects of the present disclosure can be implemented using one or more suitable processing device, such as general purpose computer systems. microprocessors, digital signal processors, micro-controllers, application specific integrated circuits (ASIC), programmable logic devices (PLD), field programmable logic devices (FPLD), field programmable gate arrays (FPGA), mobile devices such as a mobile telephone or personal digital assistants (PDA), a local server, a remote server, wearable computers, tablet computers, or the like.

Memory storage devices of the one or more processing devices can include a machine-readable medium on which is stored one or more sets of instructions (e.g., software) embodying any one or more of the methodologies or functions described herein. The instructions can further be transmitted or received over a network via a network transmitter receiver. While the machine-readable medium can be a single medium, the term "machine-readable medium" should be taken to include a single medium or multiple media (e.g., a centralized or distributed database, and/or associated caches and servers) that store the one or more sets of instructions. The term "machine-readable medium" can also be taken to include any medium that is capable of storing, encoding, or carrying a set of instructions for execution by the machine and that cause the machine to perform any one or more of the methodologies of the various implementations, or that is capable of storing, encoding, or carrying data structures utilized by or associated with such a set of instructions. The term "machine-readable medium" can accordingly be taken to include, but not be limited to, solid-state memories, optical media, and magnetic media. A variety of different types of memory storage devices, such as a random access memory (RAM) or a read only memory (ROM) in the system or a floppy disk, hard disk, CD ROM, DVD ROM, flash, or other computer readable medium that is read from and/or written to by a magnetic, optical, or other reading and/or writing system that is coupled to the processing device, can be used for the memory or memories.

Referring generally to <FIG>, a smart scale system <NUM> for determining a user profile for a user can include a mat <NUM>, an imaging device <NUM> (such as a camera, a video recorder, or the like), a display device <NUM>, a processor <NUM>, and a memory device <NUM>. The mat <NUM> includes a first sensor <NUM> configured to output pressure data. The imaging device <NUM> can be configured to generate image data reproducible as one or more images of a user. The memory device <NUM> can be configured to receive and store therein the pressure data from the first sensor <NUM> and the image data from the imaging device <NUM>. The memory device <NUM> can store machine-readable instructions that are configured to cause the processor <NUM> to determine that a portion of the user is in contact with the mat <NUM> based on the pressure data, the image data, or both. The processor <NUM> can be further caused to determine a user profile for the user based on the pressure data, the image data, or both. The user profile includes a posture of the user. The posture of the user can be determined by comparing the pressure data, the image data, or both, to one or more predetermined postures stored in the memory device <NUM>. The predetermined postures may be stored in a database <NUM> of the memory device <NUM>. The processor <NUM> can also be caused to display, on the display device <NUM>, information associated with the determined user profile. Optionally, the smart scale system <NUM> includes a power source <NUM> and a user interface <NUM>.

In some implementations, the memory device <NUM> can be configured to cause the processor <NUM> to determine an identity of the user based on the pressure data, the image data, or both. The determining process can be carried out by, for example, a machine learning algorithm. As an example, the user profile includes a shape of the portion of the user (e.g., a foot, a hand, or the like), a dimension of the portion of the user, or the like, or any combination thereof. In such example, the displayed information associated with the determined user profile includes a first indicium indicative of the weight of the user, a second indicium indicative of the posture of the user, a third indicium indicative of the shape of the portion of the user, a fourth indicium indicative of the dimension of the portion of the user, or the like, or any combination thereof.

In some implementations, the mat <NUM> includes a second sensor (not shown) configured to output temperature data. In such implementations, the memory device <NUM> can be further configured to cause the processor <NUM> to determine that the portion of the user is in contact with the mat <NUM> based on the pressure data, the image data, the temperature data, or any combination thereof. The first sensor <NUM> may be the same as, or different from, the second sensor.

In some implementations, the smart scale system <NUM> includes a user interface <NUM>. For example, the user interface <NUM> can be coupled to the display device <NUM>. The user interface <NUM> can be configured to receive input data associated with the user. As an example, the input data includes age or gender of the user.

The memory device <NUM> of the smart scale system <NUM> can be further configured to cause the processor <NUM> to determine a wellness plan for the user based on the determined user profile, and the displayed information associated with the determined user profile can then include an indicium indicative of wellness of the user. For example, the wellness plan is an exercise schedule.

The memory device <NUM> of the smart scale system <NUM> can also be configured to cause the processor <NUM> to determine a posture score based on the comparing the pressure data, the image data, or both, to the one or more predetermined postures store in the database <NUM> of the memory device <NUM>. For example, the posture score can be indicative of poor posture of the user. In some implementations, the memory device <NUM> can be configured to cause the processor <NUM> to determine a posture correction plan associated with the user based on the comparing the pressure data, the image data, or both, to the one or more predetermined postures.

In some implementations, the display device <NUM> is coupled to the mat <NUM>. For example, the mat <NUM> includes one or more LED lights. The processor <NUM> can be configured to cause the display device <NUM> to display a shape indicative of a position for the user to place his or her hands or feet on the mat <NUM>. This can be useful in various situations, such as in the instance where the mat <NUM> is a yoga mat, and the smart scale system <NUM> is configured to display yoga postures suggested to the user by recommending placement for the user's hands and/or feet.

In some implementations, the memory device <NUM> of the smart scale system <NUM> can be configured to cause the processor <NUM> to determine an active period based on the determining that the portion of the user is in contact with the mat <NUM>. In some such implementations, the smart scale system <NUM> can further include a virtual reality device (not shown) configured to receive the pressure data from the first sensor <NUM> and the image data from the camera <NUM> during the active period and display digital information based on the received data. As an example, the display device <NUM> can be coupled to the virtual reality device.

In some implementations, one or more components of the smart scale system <NUM> includes, be a part of, or be used in conjunction of, an augmented reality system. The augmented reality system can be configured to show how the user should correct his or her posture via, for example, an augmented reality display device (e.g., the user sees himself or herself in an outline showing a corrective posture and then the user can try to align his or her spine to the outline).

In some implementations, the mat <NUM> of the smart scale system <NUM> is configured to pair with a mobile phone. For example, the display device <NUM> is coupled to the mobile phone. The mat <NUM> can be paired with one, two, three, or any other number of mobile phones. The mat <NUM> can also be paired with one or more different devices. In some other implementations, the mat <NUM> of the smart scale system <NUM> works in a standalone mode (e.g., without a mobile device).

In some implementations, one or more components of the smart scale system <NUM> includes, be a part of, or be used in conjunction of, an artificial intelligence system. For example, the artificial intelligence system can be stored in the cloud, at the edge (e.g., IoT Edge), or in any combination thereof.

While the smart scale system <NUM> is shown in <FIG> as including a mat <NUM>, one or more sensors <NUM>, an imaging device <NUM>, a display device <NUM>, one or more processors <NUM>, one or more memory devices <NUM>, a database <NUM>, a power source <NUM>, and a user interface <NUM>, alternative systems that are the same as, or similar to, the smart scale system <NUM> can be constructed with more or less components. For example, a first alternative system (not shown) includes a mat, a pressure sensor, a temperature sensor, a camera, a display device, a processor, and a memory device.

<FIG> illustrates an exemplary implementation of the present disclosure, a smart scale system <NUM> is the same as, or similar to, the smart scale system <NUM>, except that the various components can be coupled to different devices. For example, the smart scale system <NUM> includes the mat <NUM>, the imaging device <NUM>, and a mobile device <NUM>. The mat <NUM> includes the sensor <NUM>, a power source <NUM>, and a communications module <NUM>. The mobile device <NUM> includes the processor <NUM>, the memory device <NUM>, the display device <NUM>, the user interface <NUM>, and a communications module <NUM>. The mat <NUM> can be communicatively coupled to the mobile device <NUM> via the communications modules <NUM> and <NUM>. Similarly, the imaging device <NUM> can be communicatively coupled to the mobile device <NUM>. Additionally or alternatively, the imaging device <NUM> can be directly coupled to the mobile device <NUM>. As another example, the devices can be coupled to one another via Bluetooth or BLE.

For example, the power source <NUM> includes a battery and an energy harvesting element configured to harvest energy for charging the battery. The energy harvesting element can be a transducer configured to convert thermal energy into electrical energy for charging the battery. In some instances, the transducer can be coupled to a second sensor configured to output temperature data (such as the one described above). Alternatively or additionally, the energy harvesting element can be a transducer configured to convert mechanical energy (e.g., vibrations from someone standing on the mat or exercising on the mat) into electrical energy for charging the battery.

<FIG> illustrate a smart scale system <NUM> including the mat <NUM> and the camera <NUM>. The mat <NUM> includes the sensor <NUM> configured to sense pressure data associated with a user <NUM>. The camera <NUM> has a field of view α configured to cover at least a portion of the user <NUM> and the mat <NUM>. The user <NUM> can be instructed to turn at an angle towards the camera <NUM> in order to get various image data for the camera <NUM> at different angles.

Various components of the smart scale system <NUM> can be coupled to various devices. In addition to the implementation such as that of the smart scale system <NUM>, the processor <NUM> and the memory device <NUM> can be coupled to the mat <NUM> (<FIG>). The display device <NUM> can be coupled to a mirror (<FIG>, <FIG>), a carpet, the mat <NUM>, or the mobile device <NUM> (<FIG>).

<FIG> illustrates a smart scale system <NUM> including the mat <NUM> and a smart mirror <NUM>. The camera <NUM> can be coupled to the smart mirror <NUM>. The camera <NUM> of the smart mirror <NUM> has a field of view β configured to cover at least a portion of the user <NUM> and the mat <NUM>.

<FIG> illustrates a front elevation view of the smart mirror <NUM> of the smart scale system <NUM>, while <FIG> illustrates a side elevation view of the smart mirror <NUM>. As can be seen in <FIG>, the frame surrounds the mirror <NUM>, while portions of the display device <NUM> that are activated are visible through the mirror <NUM>. <FIG> also shows the two-dimensional grid that can be formed by the mirror sensors in the frame that is used to detect the user's face, head, or other body part. This two dimensional grid is generally not visible to the user during operation.

<FIG> shows the arrangement of the frame with the mirror sensors, the mirror <NUM>, the display device <NUM>, and the camera. In an embodiment, the processor <NUM> and the memory device <NUM> can be mounted behind the display device <NUM>. In other embodiments, the processor <NUM> and the memory may be located at other portions within the smart mirror <NUM>, or can be located external to the smart mirror <NUM> entirely. The smart mirror <NUM> generally also includes housing components <NUM> and <NUM> that form a housing that contains and protects the display device <NUM>, the camera, and the processor <NUM>.

In some implementations, the sensor <NUM> of the mat <NUM> can be configured to sense pressure data, as illustrated in <FIG> showing a pressure map <NUM> of the feet <NUM> and <NUM> of the user. The pressure map <NUM> is representative of a pressure gradient associated with the feet <NUM>, <NUM> of the user. Further, the pressure map <NUM> is indicative of a weight distribution of the user.

Used in conjunction with the pressure map <NUM> or without the pressure map <NUM>, the pressure data can be used to generate additional information associated with the user. As a first example, in some implementations, a length of a foot of the user can be calculated. Additionally, in some implementations, based at least in part on the calculated length of the foot, a shoe size for the user can be estimated. As a second example, in some implementations, a foot profile for the user can be determined. The foot profile can include a selection among a high arc, a low arc, and a medium arc. As a third example, in some implementations, an insole profile for the user can be determined. Additionally, in some implementations, based at least in part on the insole profile, a custom insole may be created for the user (e.g., using 3D printing).

As shown, the pressure map <NUM> includes pressure points 167A1, 167A2, 167A3 for the foot <NUM>. The pressure map <NUM> further includes pressure points 167B1, 167B2, 167B3 for the foot <NUM>. In some implementations, the pressure map <NUM> can aid in detecting whether the user has diabetic foot. A diabetic foot often has fluid buildup and/or lost nerves, which can be detected using electrodes (<FIG>) and/or a pressure sensor. As a first example, the area of the diabetic foot can go up over time, resulting in an enlargement of the pressure map <NUM> over time. As a second example, the pressure distribution differs over time (e.g., the pressure points <NUM>'s move over time and/or enlarge over time). In some implementations, instead of analyzing the pressure data over time, pressure data of a current user can be compared with pressure data of other users (some with diabetic foot and some without). In some implementations, the pressure map <NUM> can aid in detecting other ailments and/or illnesses, such as joint diseases.

In some implementations, the sensor <NUM> of the mat <NUM> can be configured to sense temperature data, as illustrated in <FIG> showing a temperature map <NUM> of the feet <NUM> and <NUM> of the user. The heat from the user can be used to power the battery of the power source <NUM>, such as the one illustrated in <FIG> of the present disclosure.

Referring to <FIG>, the mat <NUM> can be flexible, or stretchable, or both, as shown in the smart scale system <NUM>. For example, the mat <NUM> can be configured to move between a generally planar configuration (<FIG>) to a generally cylindrical configuration (<FIG>). For example, the power source <NUM>, the processor <NUM>, and the memory device <NUM> can be coupled to a side portion <NUM> of the mat <NUM>, such that when the mat <NUM> is rolled into a cylindrical configuration, the side portion <NUM> of the mat <NUM> remains relatively flat.

In some implementations, the first sensor <NUM> can be a CMOS integrated silicone pressure sensor, or a piezoelectric sensor. In some implementations, the first sensor <NUM> includes an embedded layer of liquid capable of sensing pressure. For example, the first sensor <NUM> can be a layer stacked pressure sensor comprising a liquid metal-embedded elastomer. As best shown in <FIG>, the smart scale system <NUM> includes a top layer <NUM>, a bottom layer <NUM>, and a middle layer (e.g., the sensor <NUM>) between the top layer <NUM> and the bottom layer <NUM>. The top layer <NUM> can be made of fabric, rubber, or any other suitable material. Similarly, the bottom layer <NUM> can be made of fabric, rubber, or any other suitable material.

Referring now to <FIG>, a smart scale system <NUM> is illustrated, according to some implementations of the present disclosure. The smart scale system <NUM> is the same as, similar to, or used in conjunction with the smart scale systems of <FIG>, where like reference numbers are used to designate similar or equivalent components. In some implementations, one or more components of the smart scale system <NUM> form a smart mat, such as a bath mat or a yoga mat.

The smart scale system <NUM> is used to determine a normalized weight of a user, among other uses. The smart scale system <NUM> includes a control system <NUM>, a memory device <NUM>, one or more processors <NUM>, a weight system <NUM>, and a pressure sensing system <NUM>. In some implementations, the smart scale system <NUM> further includes a bio-impedance system <NUM>. In some implementations, the smart scale system <NUM> further includes a communications network <NUM>.

As shown in <FIG>, the control system <NUM> includes the one or more processors <NUM> (hereinafter, processor <NUM>). The control system <NUM> is generally used to control (e.g., actuate) the various components of the system <NUM> and/or analyze data obtained and/or generated by the components of the system <NUM>. The processor <NUM> can be a general or special purpose processor or microprocessor. While one processor <NUM> is shown in <FIG>, the control system <NUM> can include any suitable number of processors (e.g., one processor, two processors, five processors, ten processors, etc.) that can be in a single housing, or located remotely from each other. The control system <NUM> can be coupled to and/or positioned within a mat of the system <NUM>, within a housing of one or more load cells <NUM> of the weight system <NUM>, within a housing of one or more of the sensors <NUM> of the sensing system <NUM>, or any combination thereof. The control system <NUM> can be centralized (within one such housing) or decentralized (within two or more of such housings, which are physically distinct). In such implementations including two or more housings containing the control system <NUM>, such housings can be located proximately and/or remotely from each other.

The memory device <NUM> stores machine-readable instructions that are executable by the processor <NUM> of the control system <NUM>. The memory device <NUM> can be any suitable computer readable storage device or media, such as, for example, a random or serial access memory device, a hard drive, a solid state drive, a flash memory device, etc. While one memory device <NUM> is shown in <FIG>, the system <NUM> can include any suitable number of memory devices <NUM> (e.g., one memory device, two memory devices, five memory devices, ten memory devices, etc.). The memory device <NUM> can be coupled to and/or positioned within a mat of the system <NUM>, within a housing of one or more load cells <NUM> of the weight system <NUM>, within a housing of one or more of the sensors <NUM> of the sensing system <NUM>, within a housing of a user interface (e.g., a mobile phone, a smart mirror), or any combination thereof. Like the control system <NUM>, the memory device <NUM> can be centralized (within one such housing) or decentralized (within two or more of such housings, which are physically distinct.

In some implementations, the system <NUM> further includes an electronic interface (such as the user interface <NUM> of <FIG>). The electronic interface is configured to receive data (e.g., user input data) such that the data can be stored in the memory device <NUM> and/or analyzed by the processor <NUM> of the control system <NUM>. The electronic interface can communicate one or more components of the system <NUM> using a wired connection or a wireless connection (e.g., using an RF communication protocol, a WiFi communication protocol, a Bluetooth communication protocol, over a cellular network, etc.). The electronic interface can include an antenna, a receiver (e.g., an RF receiver), a transmitter (e.g., an RF transmitter), a transceiver, or any combination thereof. The electronic interface can also include one more processors and/or one more memory devices that are the same as, or similar to, the processor <NUM> and the memory device <NUM> described herein. In other implementations, the electronic interface is coupled to or integrated (e.g., in a housing) with the control system <NUM> and/or the memory device <NUM>.

In some implementations, the weight system <NUM> of the smart scale system <NUM> includes a plurality of load cells <NUM>. For example, as shown in <FIG>, the weight system <NUM> includes four of four-by-four arrays of load cells: 922a, 922b, 922c, and 922d. Each of the four-by-four arrays of load cells is coupled to a respective analog to digital converter (ADC). For example, the array of load cells 922a is coupled to the ADC 924a; the array of load cells 922b is coupled to the ADC 924b; the array of load cells 922c is coupled to the ADC 924c; and the array of load cells 922d is coupled to the ADC 924d.

In some implementations, the pressure sensing system <NUM> of the smart scale system <NUM> includes an array of pressure sensors. In some such implementations, the array of pressure sensors includes a matrix of pressure sensors of any suitable number. As an example, as shown in <FIG>, the array of pressure sensors includes a 3x2 matrix of pressure sensors: 912a-912f. As another example, the array of pressure sensors includes a 100x70 matrix of pressure sensors. Additional details and/or alternative implementations of the pressure sensing system <NUM> is discussed with regard to <FIG>, <FIG>, and their corresponding description. Further, an example output (e.g., a heat map) of the pressure sensing system <NUM> is shown in <FIG>.

In some implementations, the control system <NUM> is configured to receive weight data from the weight system <NUM>, and to receive pressure data from the pressure sensing system <NUM>. Every user has a unique pressure map (e.g., like a finger print), as generated by the pressure data associated with the user. Based at least in part on the pressure data (received from the pressure sensing system <NUM>) and registered user data (stored on the memory device <NUM> and/or transmitted from the communications network <NUM>), the control system <NUM> is configured to determine whether that the user is a registered user or a non-registered user of the smart scale system <NUM>.

If the user is a non-registered user of the smart scale system, in some implementations, based at least in part on a determination that a portion of the user is in contact with the smart scale system, a camera is activated to generate image data of the user. The determination can be made based at least in part on the weight data, the pressure data, or both. The generated image data is then compared with the registered user data, thereby verifying that the user is a non-registered user of the smart scale system.

In some implementations, the plurality of load cells in the weight system <NUM> is configured to only measure weight up to a certain amount. The pressure sensing system <NUM> is configured to pick up the task of measuring weight, if the weight of the user exceeds the amount measurable by the weight system <NUM>. Therefore, in some implementations, a load cell weight for the user is determined based on the received weight data. If the load cell weight does not exceed a predetermined threshold, the load cell weight is displayed on a display device as an actual weight for the user. If the load cell weight exceeds the predetermined threshold, (i) a pressure sensor weight is estimated for the user based at least in part on the received pressured data, and (ii) the pressure sensor weight is displayed on the display device as the actual weight for the user.

In some implementations, the weight for the user, as measured by the weight system, is not accurate enough to reflect the true weight of the user, and the smart scale system <NUM> can normalize it. First, a load cell weight for the user is determined based on the received weight data. The determined load cell weight is received as a first input for a machine learning algorithm. In addition, a reason for adjustment is received as a second input for the machine learning algorithm. The machine learning algorithm then generates an output, which is a normalized weight of the user. Additionally, in some implementations, the load cell weight for the user, and/or the normalized weight for the user, and/or the reason for adjustment are displayed on a display device.

The reason for adjustment can include (i) a state of the user being dressed or undressed (e.g., clothes may add weight, different types of clothes may add various amounts of weight), (ii) a status of the user's recent use of bathroom (e.g., lack of bowel movement may add weight), (iii) a time when the user last ate and/or drank (e.g., recent consumption of food or beverage may add more weight), (iv) a type of food of the user's last meal (e.g., consuming carbohydrates and/or sodium may increase water retention), (v) a shower status (e.g., having wet hair may add weight); or (vi) any combination thereof.

Further, the machine learning algorithm can be trained with historical data. For example, in some implementations, the historical data includes historical load cell weight data and historical normalized weight data. The historical data can be associated with other users (e.g., registered user data of other users), and/or the current user of the smart scale system (e.g., from a third party activity tracker associated with the current user, or other activity tracking databases). In some implementations, the machine learning algorithm can be trained with sensor data measured by other sensors of the smart scale system. For example, in some such implementations, the data given by the plurality of electrodes <NUM> can be used to determine whether the user is wet or not.

If the user is a registered user of the smart scale system <NUM>, a prompt is displayed on a display device for the user to input information to be associated with the received weight data. User information is then received in response to the prompt. Based at least in part on the received user information, the weight data is modified to output a normalized weight. Additionally, in some implementations, the modified weight data is displayed on the displayed device. The user information can include the same, or similar information as the reason for adjustment discussed herein. Similarly, the machine learning algorithm can be used if the user is a registered user. The machine learning algorithm can be further or alternatively trained using user specific data that is generated, over a period of time, by the user of the smart scale system <NUM>.

In some implementations, the bio-impedance system <NUM> of the smart scale system <NUM> is configured to generate bioelectrical impedance data associated with the user. The bioelectrical impedance system <NUM> includes a plurality of electrodes <NUM> configured to conductively contact the user and form one or more closed circuits with the user. For example, as shown in <FIG>, a first pair of electrodes 944a, 944b forms a first closed circuit with the user (e.g., with a first foot of the user), and is configured to generate current data to be transmitted to a bioelectrical impedance module <NUM>. A second pair of electrodes 944c, 944d forms a second closed circuit with the user (e.g., with a second foot of the user), and is configured to generate voltage data to be transmitted to the bioelectrical impedance module <NUM>. Based at least in part on the bioelectrical impedance data (including the current data and the voltage data), the control system <NUM> is configured to determine an estimated body composition of the user, such as a body fat and a muscle mass of the user.

In some implementations, the communications network <NUM> of the smart scale system <NUM> includes a wireless communications module <NUM> and a pairing button <NUM>. The wireless communications module <NUM> an include a BLE module, and/or a Wi-Fi module. The pairing button <NUM> can be physical or virtual. In some implementations, actuation of the pairing button <NUM> enables the control system to transmit data to and/or from the wireless communications module <NUM>. In some implementations, the pairing button <NUM> is a wireless button. For example, in some such implementations, the pairing button <NUM> includes a Near Field Communication (NFC) button.

While the smart scale system <NUM> in <FIG> is shown to include the control system <NUM>, the memory device <NUM>, the weight system <NUM>, the pressure sensing system <NUM>, the bio-impedance system <NUM>, and the communications network <NUM>, a smart scale system can include more or fewer components. As an example, in some implementations, a first alternative smart scale system can include a control system, a weight system, and a sensing system. As another example, in some implementations, a second alternative smart scale system can include a control system, a memory device, a weight system, a pressure sensing system, and a display device. As yet another example, in some implementations, a third alternative smart scale system can include a control system, a memory device, a weight system, a pressure sensing system, a bio-impedance system, and a user input module.

Referring now to <FIG>, an illustrative block diagram of the pressure sensing system <NUM> is shown, according to some implementations of the present disclosure. In some implementations, a shift register is configured to multiply the number of outputs available; a SPDT MUX is a digitally controlled switch, such as a multiplexer where two possible output is chosen for each input; an AO is an operational amplifier; and a multiplexer CD4051 is a multiplexer where one output is chosen for each input. A main goal of the AO and the SPDT MUX is to avoid an electrical issue common to matrixes of sensors, such as cross-talk. A main goal of the shift register and the simple MUX is to make the reading simpler (fewer inputs and outputs of the control system are needed thanks to them).

In some implementations, the components of the pressure sensing system <NUM> are coupled to a PCB, which is in turn connected to the control system <NUM>. In some implementations, the PCB is wired to a copper row sheet (e.g., the first sheet <NUM> of <FIG>) and a copper column sheet (e.g., the third sheet <NUM> of <FIG>). In some such implementations, the PCB is positioned between the substrate <NUM> and the base cover <NUM> (<FIG>).

Referring now to <FIG>, a smart scale system <NUM> is illustrated. The smart scale system <NUM> is the same as, or similar to, the smart scale system <NUM>, where like reference numbers are designated for similar or equivalent elements. More specifically, <FIG> is an illustrative partial exploded diagram of a smart scale system, according to some implementations of the present disclosure; and <FIG> is a reversed partial exploded diagram of the smart scale system of <FIG>, according to some implementations of the present disclosure.

The smart scale system <NUM> includes a cover layer <NUM> (e.g., a bath mat cover, a top layer), a generally opaque layer <NUM>, an array of pressure sensors (including a first sheet <NUM>, a second sheet <NUM>, and a third sheet <NUM>), a substrate <NUM>, a plurality of load cells (including the load cell <NUM>), a plurality of load feet (including the load foot <NUM>), and a base cover <NUM>. As shown, the plurality of load cells is coupled to a first side of the substrate <NUM>. The array of pressure sensors is coupled to a second opposing side of the substrate <NUM>. In some implementations, the substrate <NUM> is one or more pieces of glass, such as two pieces, four pieces, eight pieces, etc. In some implementations, the substrate <NUM> includes two pieces of glass coupled together via one or more hinges, so that the smart scale system <NUM> can be folded in half for easy transportation.

In some implementations, the cover layer includes a sheet of fabric. As shown, the cover layer <NUM> includes two electrically conductive fabric portions 1078A, 1078B spaced from each other. In some implementations, the two electrically conductive fabric portions 1078A, 1078B are spaced from each other by a suitable distance, such as one inch, two inches, three inches, four inches, five inches, six inches, and up to a width of the cover layer <NUM>. In some implementations, the two electrically conductive fabric portions 1078A, 1078B are spaced from each other at least three inches. In some implementations, the two electrically conductive fabric portions 1078A, 1078B are spaced from each other at a distance that a user's feet are typically spaced apart.

In some implementations, a plurality of electrodes 1044A, 1044B is positioned between the opaque layer <NUM> and the cover layer <NUM>. Two electrodes 1044A, 1044B are shown in <FIG>. Each of the electrodes 1044A, 1044B is positioned directly below a respective one of the conductive fabric portions 1078A, 1078B. , even though the electrodes 1044A, 1044B are not exposed to the user, the electrodes 1044A, 1044B are still conductive via the conductive fabric portions 1078A, 1078B. In other words, in some implementations, the conductive fabric portions are configured to be in electrical physical connection with the electrodes. In some implementations, beneath the cover layer <NUM>, the opaque layer <NUM> is positioned above the various components so that the various components underneath are not visible to human eye.

The array of pressure sensors is configured to generate pressure data associated with the user. In some implementations, the array of pressure sensors is configured to generate the pressure data in response to the user engaging the system (e.g., standing on the cover layer <NUM>). In some implementations, the array of pressure sensors includes the first sheet <NUM>, the second sheet <NUM>, and the third sheet <NUM>. In some implementations, the first sheet <NUM> is a copper rows layer, and includes a plurality of electrically conductive rows <NUM>. In some implementations, the third sheet <NUM> is a copper columns layer, and includes a plurality of electrically conductive columns <NUM>. In some implementations, the second sheet <NUM> is a pressure sensitive sheet, and includes a piezoresistive sheet that is positioned between the first sheet <NUM> and the third sheet <NUM>. The piezoresistive sheet is configured to change its electrical resistance in response to pressure being applied thereto. In some such implementations, the intersection of each of the plurality of electrically conductive rows <NUM> with each of the plurality of electrically conductive columns <NUM> forms and/or defines a pressure sensor (e.g., the pressure sensor <NUM> of <FIG>) of the array of pressure sensors.

The plurality of load cells being is to generate weight data associated with a user. In some implementations, the plurality of load cells is configured to generate the weight data in response to the user engaging the smart scale system (e.g., standing on the cover layer <NUM>). In some implementations, each of the plurality of load feet is rigid, and is directly coupled to a respective one of the plurality of load cells. For example, as shown in <FIG>, the rigid load foot <NUM> is directly coupled to the load cell <NUM>. The base cover <NUM> is coupled to the substrate <NUM> such that the plurality of load cells <NUM>, the memory, and the control system are at least partially positioned between the base cover <NUM> and the substrate <NUM>. In some implementations, the base cover <NUM> includes a plurality of apertures. Each of the plurality of rigid load feet protrudes at least partially through at least one of the plurality of apertures. For example, the load foot <NUM> protrudes partially through the aperture <NUM> of the base cover <NUM>. As such, while the load cell <NUM> is not exposed to the ground, the load cell <NUM> is stabilized via its contact with the load foot <NUM>, and is effectively stabilized on the ground.

In some implementations, one or more components of the smart scale system <NUM> form a smart mat, for example, a bath mat, a yoga mat, a doormat, an anti-fatigue mat, a chair cushion, a body pillow, a shoe insole, a portion of a carpet, one or more pieces of tile, one or more pieces of hardwood flooring, part of a mattress, part of a shower (e.g., coupled to or embedded in a shower pan or a bath tub), or the like. This is advantageous because some people and/or animals can have weight anxiety. Hiding the smart scale system in everyday items can also encourage continual monitoring of the weight, body fat distribution, and/or any health changes of the user. Furthermore, energy harvesting can be included in some of the above-referenced implementations, for example, using heat of the feet and/or dynamic pressure with a piezoelectric collector.

In some implementations, the smart mat includes all of the components shown in <FIG>. The smart mat can be of any suitable dimensions. For example, a length of the smart mat is between about <NUM> to about <NUM>, preferably between about <NUM> to about <NUM>, and most preferably about <NUM>. A width of the smart mat is between about <NUM> to about <NUM> centimeters, preferably between about <NUM> to about <NUM>, and most preferably about <NUM>. A thickness of the smart mat is between about <NUM> to about <NUM>, preferably between about <NUM> to about <NUM>, and most preferably about <NUM>. Additionally or alternatively, in some implementations, the load foot <NUM> extends from the base cover <NUM> by about <NUM> to about <NUM>.

Referring to <FIG>, a partial perspective view of a smart scale system <NUM> is shown. The smart scale system <NUM> is the same as, or similar to, the smart scale system <NUM>, where like reference numbers refer to like elements and/or components. However, the array of pressure sensors of the smart scale system <NUM> includes two sheets (e.g., a first sheet <NUM> and a second sheet <NUM> in <FIG>) instead of three (e.g., the first sheet <NUM>, the second sheet <NUM>, and the third sheet <NUM> in <FIG>).

As shown, the first sheet <NUM> of the smart scale system <NUM> is the same as, or similar to, the second sheet <NUM> of the smart scale system <NUM>. In some implementations, the first sheet <NUM> is a pressure sensitive sheet, such as a piezoresistive sheet. In some implementations, the first sheet <NUM> is flexible. The second sheet <NUM> of the smart scale system <NUM> replaces the first sheet <NUM> and the third sheet <NUM> of the smart scale system <NUM> at once. In some implementations, the second sheet <NUM> includes a printed circuit board (PCB) having a plurality of electrically conductive trace patterns (e.g., 1112A-1112E) thereon. In some such implementations, each of the plurality of electrically conductive trace patterns forms and/or defines a pressure sensor (e.g., the pressure sensor <NUM> of <FIG>) of the array of pressure sensors.

Referring now to <FIG>, an example pressure sensor formed and/or defined by an electrically conductive trace pattern <NUM> is illustrated, according to some implementations of the present disclosure. As shown, the electrically conductive trace pattern <NUM> includes an inner disk <NUM> and an outer ring <NUM>. The inner disk <NUM> is spaced radially apart from the outer ring <NUM> by a first distance d. Examples of the first distance d include: <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, and <NUM>. In some implementations, the first distance d is <NUM>, which is <NUM>.

The outer ring <NUM> is formed around the inner disk <NUM> for a second distance θ. Examples of the second distance θ include <NUM>°, <NUM>°, <NUM>°, <NUM>°, <NUM>°, <NUM>°, and <NUM>°. As shown in <FIG>, the electrically conductive trace pattern 1112A has an outer ring that forms a perfect circle around the inner disk, where the second distance θ is <NUM>°. Alternatively, in some implementations, the outer ring <NUM> does not form all the way around the inner disk <NUM>, and therefore has a second distance θ smaller than <NUM>° (best shown in <FIG>).

Referring now to <FIG>, in some implementations, the outer ring of the electrically conductive trace pattern is an equilateral polygon. For example, the electrically conductive trace pattern 1112A is positioned adjacent to the electrically conductive trace pattern 1112B. The electrically conductive trace pattern 1112A includes the inner disk 1105A and the outer ring 1103A, where the outer ring 1103A is an equilateral hexagon. The equilateral hexagon of the outer ring 1103A has six sides, and one side of the outer ring 1103A in the electrically conductive trace pattern 1112A is adjacent to and parallel to one side of the outer ring in the electrically conductive trace pattern 1112B, and so forth. The length for each side is between about <NUM> to about <NUM>, such as <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, or <NUM>.

Turning now to <FIG> illustrates a first layout 1200A of load cells in a smart scale system (e.g., the smart scale system <NUM>, the smart scale system <NUM>, the smart scale system <NUM>, the smart scale system <NUM>, the smart scale system <NUM>, the smart scale system <NUM>, the smart scale system <NUM>, the smart scale system <NUM>, and the smart scale system <NUM>). In this layout 1200A, the plurality of load cells includes four single load cells <NUM>. In some implementations, in this layout 1200A, the plurality of load cells includes one four-by-four array of load cells described above.

<FIG> illustrates a second layout 1200B of load cells in a smart scale system (e.g., the smart scale system <NUM>, the smart scale system <NUM>, the smart scale system <NUM>, the smart scale system <NUM>, the smart scale system <NUM>, the smart scale system <NUM>, the smart scale system <NUM>, the smart scale system <NUM>, and the smart scale system <NUM>). In this layout 1200B, the plurality of load cells includes six (<NUM>) of the load cells <NUM> as those shown in <FIG>. In some implementations, in this layout 1200B, the plurality of load cells includes one two-by-three array of load cells.

<FIG> illustrates a third layout 1200C of load cells in a smart scale system (e.g., the smart scale system <NUM>, the smart scale system <NUM>, the smart scale system <NUM>, the smart scale system <NUM>, the smart scale system <NUM>, the smart scale system <NUM>, the smart scale system <NUM>, the smart scale system <NUM>, and the smart scale system <NUM>). In this layout 1200C, the plurality of load cells includes nine (<NUM>) of the load cells <NUM>. In some implementations, in this layout 1200C, the plurality of load cells includes one three-by-three array of load cells.

<FIG> illustrates a fourth layout 1200D of load cells of load cells in a smart scale system (e.g., the smart scale system <NUM>, the smart scale system <NUM>, the smart scale system <NUM>, the smart scale system <NUM>, the smart scale system <NUM>, the smart scale system <NUM>, the smart scale system <NUM>, the smart scale system <NUM>, and the smart scale system <NUM>). In this layout 1200D, the plurality of load cells includes eight (<NUM>) single load cells <NUM>. In some implementations, in this layout 1200D, the plurality of load cells includes two of the four-by-four arrays of load cells described above. In some implementations, the fourth layout 1200D includes two smart scale systems having the first layout 1200A coupled to each other via one or more hinges <NUM>.

According to some implementations of the present disclosure, the array of pressure sensors can include a pressure sensing sheet having a material used with its thickness (e.g., a <NUM>-dimensional sensor), and/or with its surface (e.g., a coplanar sensor). In some implementations, this material is an isolating polymer charged in conductive particles. A low current can pass through tunnel effect from a conductive particle to another. In some implementations, this material is deformable. In addition, distances between the conductive particles change with the applied vertical pressure (cf.

<FIG> illustrates a first scheme 2000A in a pressure sensing sheet <NUM> prior to vertically applied pressure, according to some implementations of the present disclosure. The current 2050A travels through the conductive particles <NUM>. <FIG> illustrates a second scheme 2000B in the pressure sensing sheet <NUM> after vertically applied pressure, according to some implementations of the present disclosure. The current 2050B travels through the conductive particles <NUM> and the adjacent conductive particles <NUM>.

The conductive particles are closer when the material is under pressure (<FIG>), where the contribution of tunnel effect to the current circulation raises. The relationship between the impedance and the applied force can be written as: <MAT> where p is the resistivity of the contact surfaces; F is the normal force to the surface; and K is a function depending, inter alia, on the elasticity of the material.

However, K is not a constant, and the relationship between pressure and resistivity is not linear. Otherwise, crushing the material is only possible if the material itself can freely lengthen in the two other dimensions, which is not the case here: the deformation can only be local.

Thus, in some implementations, the value to measure is a resistance. The resistance here is not linear to the pressure. Further, the pressure variation is not easily measurable on the required pressure slot.

In some implementations, a <NUM>-dimensional sensor is not optimal because its variation of the resistance with the pressure is not enough under high pressure. Thus, the industrialization of the array of pressure sensors is more complex, and it is difficult to have a sensor geometry that satisfies the need of multiple sensors in a small area (e.g., four sensors per square centimeter). Indeed, in such conditions, an individual sensor can only take space up to <NUM> x <NUM><NUM>, which would highly reduce the resistance of the individual sensor.

For a coplanar sensor, the measurable magnitude is its resistance. To limit the consumption of the array of pressure sensors and/or to limit cross-talk between the adjacent pressure sensors, the value of resistance must be as large as possible. In some implementations, the variation in resistance is significant over the range of use of the array of pressure sensors, such that the slope of the characteristic is steep enough on the whole scale to have a correct definition. Accordingly, a preferred geometry allows efficient paving of the plane.

Because the value to measure is resistance, one solution is to use a transimpedance amplifier (TIA). <FIG> illustrates a circuit diagram <NUM> in the transimpedance amplifier, according to some implementations of the present disclosure. The circuit is powered using an asymmetric power supply. The transimpedance amplifier is equipped of a virtual stable ground, where the internal impedance is as low as possible.

The relationship between the input current i and the output voltage VMES<NUM> is given in first approximation by the equation: <MAT>.

However, the array of pressure sensors cannot settle with this first approximation. If ε is the differential voltage, such as: <MAT>.

With this new notation, the relationship between the measured current and the output voltage becomes: <MAT>.

Yet the value to be measured is the sensor resistance, which will allow us to calculate the current i thanks to the characteristic of the operational amplifier forcing. When in the linear mode, the voltage is V-. With the resistance of the sensor Rc, the scheme becomes is illustrated in <FIG>, which shows a circuit diagram <NUM> in a transimpedance amplifier, according to some implementations of the present disclosure.

The current in <FIG> circulates in the same direction as that in <FIG>. The output voltage is now given by the equation: <MAT>.

In some implementations, the power supply uses a supply Vcc of <NUM> V, and requires the use of amplifiers having rail to rail in outputs. Nevertheless, the limitation of this supply voltage force a constraint on the choice of the resistance Rf. As such, the limits condition is given by the equation: <MAT>.

In some implementations, f is very low, and can be close to <NUM>. Therefore, by setting f as equal to <NUM>, we have: <MAT>.

The resistance of the sensor Rc highly varies. In order to avoid the amplifier saturation, a resistance Rf lower or equal to the minimum impedance of the array of pressure sensors is implemented. In order to limit the consumption of the amplifier, a resistance Rf is chosen to be the resistance of the array of pressure sensors when put under maximum pressure, relatively to the specifications.

Furthermore, a coplanar sensor alone can only measure an average pressure on the surface that is between its two electrodes. It cannot measure a precise image of the applied pressure. In order to obtain an image that is more precise, a juxtaposition of sensors is used, which raises the question of the disposition of those sensors on a plane.

To solve the above problems, the present disclosure provides the optimal geography of planar paving, such as what is illustrated in the second sheet <NUM> of the smart scale system <NUM> in <FIG>. The distance between adjacent sensors (e.g., between the electrically conductive trace pattern 1112A and the electrically conductive trace pattern 1112B) must be sufficient to avoid crosstalk, especially if the outer ring (e.g., the outer ring <NUM> shown in <FIG>) was not complete all-around (e.g., where the second distance θ is less than <NUM>°). Indeed, the resistance of the sensor is proportional to the perimeter of the outer ring, and to the distance between two electrodes. In some implementations, the perimeter of the outer ring is limited (e.g., the outer ring is not a complete ring, and θ is less than <NUM>°) in order to raise the resistance of the sensor. In other words, if there is less contact (e.g., having an unfinished ring), there is more resistance.

Referring to <FIG>, a smart scale system <NUM> is the same as, or similar to, the smart scale system <NUM> or the smart scale system <NUM>, except that the smart scale system <NUM> is modified for pets, such as a cat, a dog, a horse, a hamster, a guinea pig, a rabbit, a chinchilla, a mouse, a rat, a parrot, a hermit crab, a ferret, a reptile, a fish, a sea monkey, or any combination thereof. In some implementations, the smart scale system is customized for any non-static item that moves, walks, crawls, rolls across the weighing plane. For example, the smart scale system <NUM> calculates an average of the weight measured, ignoring the landing part and the leaving part.

As shown in <FIG>, a dog <NUM> is using the smart scale system <NUM>. For example, the dog <NUM> steps on the smart scale system <NUM>, the measured weight goes from <NUM> to <NUM> and then goes around <NUM> for a number seconds. Then the dog <NUM> leaves, and the measured weight goes down. The smart scale system <NUM> then averages every value measured during the number seconds, and thus determines the weight for the dog <NUM> is <NUM>. In other words, in some implementations, the weight for a non-static item can be estimated from the moment it oscillates around a specific value, which is indicative of the actual weight of the non-static item.

Depending on the type of the pet, the layout and/or properties of the load cells differ. For example, a smart scale system customized for dogs includes a weight range of between about <NUM> kilograms to about <NUM> kilograms. As disclosed herein, the array of pressure sensors in the smart scale system can be configured to sense pressure data. <FIG> illustrates a pressure map <NUM> of two paws <NUM> and <NUM> of the dog (or other types of pets) of the smart scale system, according to some implementations of the present disclosure. In some implementations, pressure data of all four paws of the dog are measured, and included in the pressure map <NUM>. The pressure map <NUM> is representative of a pressure gradient associated with the two paws <NUM> and <NUM>. Further, the pressure map <NUM> can be indicative of a weight distribution of the dog.

Used in conjunction with the pressure map <NUM> or without the pressure map <NUM>, the pressure data can be used to generate additional information associated with the dog (or other types of pets), in the same or similar manner as what is illustrated in <FIG> and described accordingly. Further, the pressure map <NUM> can include pressure points for the paws. As such, the pressure map <NUM> can aid in detecting various ailments and/or illnesses of the dog (or other types of pets), instantaneously and/or over time, in the same or similar manner as what is illustrated in <FIG> and described accordingly.

In some implementations, the array of pressure sensors in the smart scale system can be configured to sense temperature data, in the same or similar manner as what is illustrated in <FIG> and described accordingly. For example, <FIG> illustrates a temperature map <NUM> of two paws <NUM> and <NUM> of the dog (or other types of pets) of the smart scale system, according to some implementations of the present disclosure. In some implementations, temperature data of all four paws of the dog are measured, and included in the temperature map <NUM>. The heat from the dog can be used to power the battery of the power source of the smart mat system, such as the one illustrated in <FIG> of the present disclosure.

In some implementations, the weight data and/or the pressure data can be used to determine the type and/or category of the non-static item (e.g., based on a weight range, based on the footprint, based on the heat map, based on the temperature map, or any combination thereof). Alternatively or additionally, the weight data and/or the pressure data can be used to identify the user, regardless of the user being a human being or an animal.

Referring now to <FIG>, an illustrative block diagram <NUM> of a smart scale system <NUM> is shown, according to some implementations of the present disclosure. The smart scale system <NUM> includes one or more sensors <NUM>, which can include one or more load cells as disclosed herein. In some implementations, the smart scale system <NUM> is modified specifically to detect animals smaller than a typical human being, such as cats and dogs. In some implementations the smart scale system <NUM> may further include an interface <NUM>, which can include a display LED and/or a button. The smart scale system <NUM> also includes an electronic system, which has a power supply <NUM>, a signal conditioning processing module <NUM>, a central module <NUM>, a communication module <NUM>, or any combination thereof. The communication module <NUM> is wirelessly (e.g., via Bluetooth) coupled to a mobile device, such as a mobile phone <NUM>. Alternatively or additionally, the communication module <NUM> is coupled to a smart plug, which in turn is wirelessly coupled to the mobile device.

<FIG> illustrates a smart scale system <NUM> adapted for using in a bed, according to some implementations of the present disclosure. In some implementations, the smart scale system <NUM> is adapted for using in a baby crib, which doubles as a baby monitoring system. The smart scale system <NUM> is the same as, or similar to, the smart scale system <NUM>, the smart scale system <NUM>, or the smart scale system <NUM>. However, the load cells <NUM> of the smart scale system <NUM> are located, and sometimes hidden, within the legs of the bed, whereas the array of pressure sensors <NUM> is coupled to and/or embedded in the mattress. In some implementations, the smart scale system <NUM> is further configured to (i) analyze the sleep quality of the user, (ii) detect sleep disorders (e.g., sleep apneas), (iii) detect sleep positions, (iv) determine changes in sleep behavior, or (v) any combination thereof.

<FIG> illustrates a smart scale system <NUM> adapted for using in a shower, according to some implementations of the present disclosure. The smart scale system <NUM> is the same as, or similar to, the smart scale system <NUM>, the smart scale system <NUM>, or the smart scale system <NUM>. However, the smart scale system <NUM> is coupled to a shower pan <NUM>. In some implementations, the shower pan <NUM> is made of flexi-glass, fiberglass, plastic, ceramic, or any combination thereof. The smart scale system <NUM> is therefore configured to measure the weight of a user <NUM> while she is showering. In some implementations, the shower pan <NUM> is positioned above the smart scale system <NUM>, such that the shower pan <NUM> prevents at least a portion of the smart scale system <NUM> from getting wet.

In some implementations, the smart scale system <NUM> includes load cells <NUM> under the shower pan <NUM>, and an energy harvesting device using the water pressure and/or a turbine in the water supply. The load cells <NUM> are configured to measure the vertical displacement of the shower pan. The seal on the sides of the shower pan <NUM> will take off a little bit of the weight of the user <NUM>, because the seal will stop the shower pan to slightly go down. As such, the smart scale system <NUM> is configured to adjust its estimation and/or calculation to take that off-weight into account, and add it back into the measured weight.

In some implementations, the smart scale system <NUM> is powered using (i) the supplied hot and/or cold water flowing in the pipe(s) to the shower head, (ii) the drain water (e.g., with a filter to remove the hair, where the drain pipe is narrowed to increase pressure and flow, and thus increase power generation from the drain water), or (iii) both. Additionally or alternatively, the smart scale system <NUM> includes a weighing system disguised as a tile, which can be embedded in the shower pan <NUM>, and eventually powered by a turbine that uses the water evacuation and/or supply.

It should initially be understood that the disclosure herein may be implemented with any type of hardware and/or software, and may be a pre-programmed general purpose computing device. For example, the system may be implemented using a server, a personal computer, a portable computer, a thin client, or any suitable device or devices. The disclosure and/or components thereof may be a single device at a single location, or multiple devices at a single, or multiple, locations that are connected together using any appropriate communication protocols over any communication medium such as electric cable, fiber optic cable, or in a wireless manner.

It should also be noted that the disclosure is illustrated and discussed herein as having a plurality of modules which perform particular functions. It should be understood that these modules are merely schematically illustrated based on their function for clarity purposes only, and do not necessary represent specific hardware or software. In this regard, these modules may be hardware and/or software implemented to substantially perform the particular functions discussed. Moreover, the modules may be combined together within the disclosure, or divided into additional modules based on the particular function desired. Thus, the disclosure should not be construed to limit the present disclosure, but merely be understood to illustrate one example implementation thereof.

In some implementations, a server transmits data (e.g., an HTML page) to a client device (e.g., for purposes of displaying data to and receiving user input from a user interacting with the client device).

Implementations of the subject matter described in this specification can be implemented in a computing system that includes a back end component, e.g., as a data server, or that includes a middleware component, e.g., an application server, or that includes a front end component, e.g., a client computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the subject matter described in this specification, or any combination of one or more such back end, middleware, or front end components. Examples of communication networks include a local area network ("LAN") and a wide area network ("WAN"), an inter-network (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer to-peer networks).

Implementations of the subject matter and the operations described in this specification can be implemented in digital electronic circuitry, or in computer software, firmware, or hardware, including the structures disclosed in this specification and their structural equivalents, or in combinations of one or more of them. Implementations of the subject matter described in this specification can be implemented as one or more computer programs, i.e., one or more modules of computer program instructions, encoded on computer storage medium for execution by, or to control the operation of, data processing apparatus. Alternatively or in addition, the program instructions can be encoded on an artificially generated propagated signal, e.g., a machine-generated electrical, optical, or electromagnetic signal that is generated to encode information for transmission to suitable receiver apparatus for execution by a data processing apparatus. Moreover, while a computer storage medium is not a propagated signal, a computer storage medium can be a source or destination of computer program instructions encoded in an artificially generated propagated signal.

The operations described in this specification can be implemented as operations performed by a "data processing apparatus" on data stored on one or more computer-readable storage devices or received from other sources.

The term "data processing apparatus" encompasses all kinds of apparatus, devices, and machines for processing data, including by way of example a programmable processor, a computer, a system on a chip, or multiple ones, or combinations, of the foregoing The apparatus can include special purpose logic circuitry, e.g., an FPGA (field programmable gate array) or an ASIC (application specific integrated circuit).

A computer program (also known as a program, software, software application, script, or code) can be written in any form of programming language, including compiled or interpreted languages, declarative or procedural languages, and it can be deployed in any form, including as a standalone program or as a module, component, subroutine, object, or other unit suitable for use in a computing environment.

Devices suitable for storing computer program instructions and data include all forms of non-volatile memory, media and memory devices, including by way of example semiconductor memory devices, e.g., EPROM, EEPROM, and flash memory devices; magnetic disks, e.g., internal hard disks or removable disks; magneto optical disks; and CD ROM and DVD-ROM disks.

According to some implementations of the present disclosure, a system for determining a user profile for a user includes a mat, a camera, a display device, a processor, and a memory device. The mat includes a first sensor configured to output pressure data. The camera is configured to generate image data reproducible as one or more images of a user. The memory device is configured to receive and store therein the pressure data from the first sensor and the image data from the camera. The memory device stores machine-readable instructions that are configured to cause the processor to determine that a portion of the user is in contact with the mat based on the pressure data, the image data, or both. The processor is further caused to determine a user profile for the user based on the pressure data, the image data, or both. The user profile includes a posture of the user. The posture of the user is determined by comparing the pressure data, the image data, or both, to one or more predetermined postures stored in the memory device. The processor is also caused to display, on the display device, information associated with the determined user profile.

According to some implementations of the present disclosure, a system for determining a user profile for a user includes a mat, a camera, a display device, a processor, and a memory device. The mat includes a first sensor configured to output pressure data. The camera is configured to generate image data reproducible as one or more images of a user. The memory device is configured to receive and store therein the pressure data from the first sensor and the image data from the camera. The memory device stores machine-readable instructions that are configured to cause the processor to determine that a portion of the user is in contact with the mat based on the pressure data, the image data, or both. The processor is further caused to determine a user profile for the user based on the pressure data and the image data. The user profile includes a posture of the user. The posture of the user is determined by comparing the pressure data and the image data to one or more predetermined postures stored in the memory device. The processor is also caused to display, on the display device, information associated with the determined user profile.

According to some implementations of the present disclosure, a system for determining a user profile for a user includes a mat, a display device, a processor, and a memory. The mat includes a battery, a first sensor configured to output pressure data, and a second sensor configured to output temperature data. The second sensor includes a transducer configured to convert thermal energy into electrical energy for charging the battery. The memory device is configured to receive and store therein the pressure data from the first sensor and the temperature data from the second sensor. The memory device stores machine-readable instructions configured to cause the processor to determine that a portion of the user is in contact with the mat based on the pressure data, the temperature data, or both. The processor is further configured to determine a user profile for the user based on the pressure data and the temperature data. The user profile includes a posture of the user. The posture of the user is determined by comparing the pressure data and the temperature data to one or more predetermined postures stored in the memory device. The processor is also caused to display, on the display device, information associated with the determined user profile.

According to some implementations of the present disclosure, a method for determining a posture of a user includes receiving pressure data and image data from a smart scale system. The smart scale system includes a mat, a camera, and a display device. The mat has a first sensor configured to output pressure data. The camera is configured to generate image data reproducible as one or more images of a user. The method further includes: storing, on a memory device, the received pressure data and the received image data; determining that a portion of the user is in contact with the mat based on the received pressure data, the received image data, or both; comparing the received pressure data, the received image data, or both, to one or more predetermined postures stored in the memory device; based at least in part on the comparing, determining a user profile for the user, the user profile including a posture of the user; and displaying, on the display device, information associated with the determined user profile.

According to some implementations of the present disclosure, a smart scale system includes a substrate, a plurality of load cells coupled to a first side of the substrate, an array of pressure sensors coupled to a second opposing side of the substrate, a memory, and a control system. The plurality of load cells is configured to generate weight data associated with a user. The array of pressure sensors is configured to generate pressure data associated with the user. The memory stores registered user data and machine-readable instructions. The control system is coupled to the memory and arranged to provide control signals to one or more processors configured to execute the machine-readable instructions. The weight data is received from the plurality of load cells. The pressure data is received from the array of pressure sensors. Based at least in part on the pressure data and the registered user data, the user is a non-registered user of the smart scale system is determined. A prompt is displayed, on a display device, for the user to register as a registered user of the smart scale system.

According to some implementations of the present disclosure, a smart scale system includes a substrate, a plurality of load cells coupled to a first side of the substrate, an array of pressure sensors coupled to a second opposing side of the substrate, a memory, and a control system. The plurality of load cells is configured to generate weight data associated with a user. The array of pressure sensors is configured to generate pressure data associated with the user. The memory stores registered user data and machine-readable instructions. The control system is coupled to the memory and arranged to provide control signals to one or more processors configured to execute the machine-readable instructions. The weight data is received from the plurality of load cells. The pressure data is received from the array of pressure sensors. Based at least in part on the pressure data and the registered user data, the user is a registered user of the smart scale system is determined. A prompt is displayed, on a display device, for the user to input information to be associated with the received weight data.

According to some implementations of the present disclosure, a method for determining a normalized weight of a user is disclosed. Registered user data is received from a memory of a smart scale system. The registered user data includes historical weight data, historical user information, and historical normalized weight data. A machine learning algorithm is trained with the historical weight data, the historical user information, and the historical normalized weight data. Pressure data associated with the user is received from an array of pressure sensors of the smart scale system. Current weight data associated with the user is received from a plurality of load cells of the smart scale system. Based at least in part on the pressure data and the registered user data, it is determined that the user is a registered user of the smart scale system. A prompt is displayed, on a display device, for the user to input information to be associated with the current weight data. In response to the prompt, current user information is received. The current weight data associated with the user and the current user information are received as an input for the machine learning algorithm. The normalized weight for the user is generated as an output for the machine learning algorithm.

The various operations of exemplary methods described herein may be performed, at least partially, by an algorithm. The algorithm may be comprised in program codes or instructions stored in a memory (e.g., a non-transitory computer-readable storage medium described above). Such algorithm may comprise a machine learning algorithm. In some embodiments, a machine learning algorithm may not explicitly program computers to perform a function, but can learn from training data to make a predictions model that performs the function.

The various operations of exemplary methods described herein may be performed, at least partially, by one or more processors that are temporarily configured (e.g., by software) or permanently configured to perform the relevant operations. Whether temporarily or permanently configured, such processors may constitute processor-implemented engines that operate to perform one or more operations or functions described herein.

Similarly, the methods described herein may be at least partially processor-implemented, with a particular processor or processors being an example of hardware. For example, at least some of the operations of a method may be performed by one or more processors or processor-implemented engines. For example, at least some of the operations may be performed by a group of computers (as examples of machines including processors), with these operations being accessible via a network (e.g., the Internet) and via one or more appropriate interfaces (e.g., an Application Program Interface (API)).

Claim 1:
A method for determining a normalized weight of a non-static item, comprising:
receiving, from a plurality of load cells (<NUM>), weight data associated with the non-static item;
determining a load cell weight for the non-static item based at least in part on the weight data;
receiving, as an input for a machine learning algorithm, the load cell weight for the non-static item;
receiving, from an array of pressure sensors (<NUM>), pressure data associated with the non-static item;
generating, based at least in part on the pressure data, a pressure heat map associated with the non-static item, wherein the pressure heat map is representative of a pressure gradient associated with feet or paws of the non-static item and indicative of a weight distribution of the non-static item; and
generating, as an output for the machine learning algorithm, the normalized weight for the non-static item.