Scale zeroing for self-checkout system

Abstract

To describe a method and device for performing zeroing of a scale of a self-checkout system.SOLUTION: An exemplary method includes steps of: detecting a weight on a scale of a self-checkout system; and determining that there is no object located on the scale. In response to detecting the weight on the scale and determining that there is no object located on the scale, zeroing operation of the scale is performed.SELECTED DRAWING: Figure 2

Description

[Background technology]

[0001]

[0001] Self-checkout systems are often sold as modular systems that can be configured in different ways. For example, a self-checkout system may include shelves for holding shopping baskets, a scanner for scanning items that customers wish to purchase, a bagging area for bagging the items after they are scanned, and a point-of-sale (POS) device for processing payments for the items scanned by the customer. Furthermore, the self-checkout system may include a security scale (also known as a bagging scale) in the bagging area for verifying that items placed in the bagging area have been scanned by the customer. [Brief explanation of the drawings]

[0002] [Figure 1] FIG. 1 is a diagram illustrating an exemplary self-checkout system according to one embodiment. [Figure 2]

[0003] FIG. 2 is a flowchart diagram of a method for zeroing a scale of a self-checkout system according to one embodiment. [Figure 3A]

[0004] FIG. 3A is a diagram illustrating an example scenario for zeroing a scale of a self-checkout system according to one embodiment. [Figure 3B] FIG. 3B is a diagram illustrating an exemplary scenario for zeroing the scale of a self-checkout system, according to one embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0003]

[0005] The self-checkout system may use a security scale to verify that an item placed in a bagging area has been scanned by a customer. For example, the self-checkout system may use the security scale to determine whether the weight of an item placed on the scale falls within an acceptable weight range for that item. One potential problem with security scales is that in some cases, the security scale may be prone to falsely detecting the weight on the scale, which may be caused by, as illustrative, non-limiting examples, malfunctioning or defective load sensor(s), environmental factors, low battery, or uneven surfaces.

[0004]

[0006] A false weight detection on the scale can cause the self-checkout system to erroneously conclude that an unscanned item has been placed in the bagging area. In such an instance, the self-checkout system may require the customer to remove the "absent" item(s) from the bagging area and may not allow the customer to scan the item(s) until the "absent" item(s) are removed from the bagging area. As a result, the self-checkout system may be taken out of service by store personnel, reducing the efficiency of the checkout process at the retail store and degrading the customer experience.

[0005]

[0007] Furthermore, in some cases, it may take a significant amount of resources for a retailer to resolve a malfunctioning self-checkout system due to a security scale falsely detecting a non-existent item. In some situations, for example, store personnel may have to take the self-checkout system out of service and manually intervene in the self-checkout system to resolve the issue. Furthermore, in situations where store personnel are unable to resolve the issue, the store personnel may have to manually open a support ticket requesting a technician to diagnose and resolve the issue. In these situations, the retailer may incur significant costs associated with dispatching a field technician to the retail store to resolve the issue.

[0006]

[0008] Embodiments herein describe techniques for detecting when a scale in a self-checkout system is falsely detecting a "not present" item on the scale and performing a zeroing operation on the scale to return the scale to zero when there are no item(s) on the scale. As described herein, the zeroing operation may involve determining the falsely detected weight on the scale, setting a "temporary zero weight" parameter of the scale equal to the falsely detected weight, and resetting the weight of the scale to the difference between the temporary zero weight parameter and the falsely detected weight.

[0007]

[0009] In some embodiments, a self-checkout system may use one or more camera devices to detect whether an item(s) is / are on the scale of the self-checkout system. The camera device(s) may include a camera device(s) of the self-checkout system, a camera device(s) of the environment in which the self-checkout system is located, a camera device(s) coupled to (or associated with) another computing system in the environment, or a combination thereof. In some embodiments, the camera device(s) may include an artificial intelligence (AI) / machine learning (ML) engine configured to perform object detection within the field of view (FOV) of the camera device(s). The camera device(s) may use the AI / ML engine to provide an indication (display, instruction, suggestion, etc.) to the self-checkout system of whether an item(s) is / are on the scale of the self-checkout system.

[0008]

[0010] In some embodiments, the self-checkout system may receive an indication of whether there is an item or items on the scale of the self-checkout system from another computing system that includes an AI / ML engine configured to perform object detection. In such embodiments, the computing system may be located on-premise or in a cloud computing environment.

[0009]

[0011] In some embodiments, the self-checkout system may perform a zeroing operation on the scale of the self-checkout system when it determines that the scale is detecting an item on the scale that is not present. Such a determination may be based on obtaining (i) an indication that there is no item on the scale and (ii) an indication that a weight is detected on the scale. In addition to performing a zeroing operation to return the scale to zero, the self-checkout system may generate and send an indication of the false-detected weight scenario to store personnel. For example, the self-checkout system may open a support ticket for the self-checkout system to cause store personnel and / or other technicians to check the self-checkout system.

[0010]

[0012] In some embodiments, the self-checkout system may refrain from performing a zeroing operation on the scale of the self-checkout system when the scale determines that the scale does not detect a non-existent item on the scale. For example, the self-checkout system may receive an indication that an object or item is present on the scale.

[0011]

[0013] Advantageously, the embodiments described herein can reduce the occurrence of self-checkout systems being taken out of service due to false positive detection of an item(s) on the scale of the self-checkout system. Thus, the embodiments can significantly increase the efficiency of the checkout process at a retail store and improve the customer experience.

[0012]

[0014] It should be noted that the techniques described herein for performing scale zeroing operations for a self-checkout system may be incorporated into (e.g., implemented within or performed by) various wired or wireless devices. In some implementations, the device may provide connectivity to a network (e.g., a wide area network (WAN) such as the Internet or a cellular network) via a wired or wireless communication link. In some implementations, the device may include a self-checkout system.

[0013]

[0015] It should be noted that although particular embodiments are described performing zeroing operations for a security scale (or bagging scale) of a self-checkout system, the techniques described herein may be applied to other scales of a self-checkout system, such as, as an illustrative, non-limiting example, a sales scale.

[0014] [Advantages of scale zeroing for self-checkout systems]

[0016] A self-checkout system may include one or more scales to process customer transactions. In some cases, the scale(s) of a self-checkout system may falsely detect a weight on the scale, causing the self-checkout system to prevent the customer from continuing with the transaction and taking the self-checkout system out of service. Embodiments herein provide an automatic method, e.g., without human intervention, for zeroing the scale after detecting that no items are detected on the scale using image processing. Doing so prevents the self-checkout system from unnecessarily pausing a customer transaction due to a false detected weight on the scale and reduces the occurrence of the self-checkout system being taken out of service.

[0015]

[0017] 1 illustrates a self-checkout system 100 according to one embodiment. The self-checkout system 100 may be located in an environment 190, such as a retail environment (e.g., a grocery store, a clothing store, an electronics store, etc.).

[0016]

[0018] Self-checkout system 100 includes a shelf 105 disposed on one side of a housing 170 and a bagging area 140 disposed on another (opposite) side of housing 170. In one embodiment, housing 170, shelf 105, and bagging area 140 are modular components, e.g., not permanently connected to one another. As shown, with respect to a customer facing self-checkout system 100, shelf 105 is disposed on the customer's left-hand side and bagging area 140 is disposed on the customer's right-hand side. Because these components are modular, the locations of bagging area 140 and shelf 105 can be swapped such that bagging area 140 is disposed on the left-hand side of housing 170 and shelf 105 is disposed on the right-hand side of housing 170. In some examples, shelf 105 and bagging area 140 can be connected (e.g., using fasteners or some other means). In other examples, the shelves 105 and bagging area 140 may be connected to something else in the enclosure 170 (eg, the floor or frame) to hold these modular components in a fixed position.

[0017]

[0019] The shelves 105 may be used to hold shopping baskets 110 containing items 115 that a customer desires to purchase using the self-checkout system 100. For example, a customer may place the shopping basket 110 on the shelves 105, allowing the customer to easily retrieve and scan the items 115.

[0018]

[0020] In other situations, a customer may have placed items 115 in a shopping cart (not shown) and may use shelves 105 to hold the items. For example, a customer may first remove items 115 from a shopping cart onto shelves 105 to make them more accessible during the checkout process.

[0019]

[0021] Display 120, camera 175, scanner 130, and camera 180 are mounted on or within housing 170. For example, display 120 may include a display screen that allows self-checkout system 100 to communicate with customers. Display 120 may output pricing information, shopping lists, scanning instructions 125, troubleshooting instructions, and the like. In one embodiment, display 120 is a touchscreen that allows a user to interact with the functionality of self-checkout system 100 (including POS application 165). For example, a user may use the touchscreen to select produce, cancel a scan, call for help, and select a payment method for the checkout process, as illustrative, non-limiting examples.

[0020]

[0022] Scanner 130 is located on the top surface of housing 170 and provides an area where a customer can move or slide an item 115 to read its barcode. In at least some embodiments, scanner 130 also includes an integrated scale (referred to herein as a "sales scale") for weighing items such as produce. Embodiments herein may be used for any type of scanning technology and any number of scanners.

[0021]

[0023] The camera 175 is positioned in a location on the housing such that its FOV includes the scanner 130. In that way, the camera 175 can capture images (and video) of a customer moving or holding an item over the scanner 130. In one embodiment, the camera 175 has a dual purpose. One purpose may include capturing images (and video) of items, such as produce, that do not have barcodes. AI / ML models can be used to perform image recognition to identify the produce. This saves the customer from having to manually identify the produce for the self-checkout system 100 (e.g., selecting the produce from a menu on the display 120 or entering a code).

[0022]

[0024] In some cases, images (and / or video) captured by camera 175 may be used to determine whether an item 115 has been placed on the integrated scale (e.g., a "sales scale") of scanner 130. For example, images (and / or video) captured by camera 175 may be analyzed using an AI / ML model (configured to perform object detection) to determine whether an item(s) has been placed on the integrated scale of scanner 130. As described in more detail below, in some embodiments, self-checkout system 100 may use information indicating whether an item 115 has been placed on the integrated scale of scanner 130 to determine whether a zeroing operation of the integrated scale should be performed. For example, in certain scenarios, the integrated scale of scanner 130 may be falsely detecting the weight on the scale due to various factors, including, by way of illustrative, non-limiting examples, malfunctioning or defective load sensor(s), environmental factors, a low battery, and an uneven surface. In such cases, the self-checkout system 100 may be configured to automatically perform an integrated scale zeroing operation to return the scale to zero upon determining that there are no items 115 (or other objects) on the scale.

[0023]

[0025] Bagging area 140 includes bags 135 placed on a sling. After scanning an item 115, the customer can place the scanned item 115 into bag 135. Bagging area 140 also includes a scale 145. Scale 145 weighs the item after it is placed into bag 135 (or into the customer's own bag, if the customer brought their own) to ensure that the weight of the item matches the expected weight of the scanned item, e.g., for loss prevention.

[0024]

[0026] 1 also illustrates the mounting of overhead camera 180 to housing 170. In one embodiment, self-checkout system 100 may have one of cameras 175 or 180, while in other embodiments, it may have both cameras or more than two cameras. In some embodiments, environment 190 may include one or more cameras 195 located external to self-checkout system 100 (e.g., camera(s) 195 may be part of a monitoring system for environment 190). Camera(s) 195 may be communicatively coupled to self-checkout system 100 and / or a computing system communicatively coupled to self-checkout system 100.

[0025]

[0027] In some cases, camera 180, camera(s) 195, or a combination thereof may be used for loss prevention. For example, images (and / or video) captured by camera(s) 195, camera 180, or a combination thereof may be analyzed to determine whether a customer (accidentally or fraudulently) moved an item 115 into bagging area 140 without first scanning the item 115 using scanner 130. To do so, the FOV of camera(s) 195 and / or camera 180 may include bagging area 140 and scanner 130, as well as other areas of self-checkout system 100.

[0026]

[0028] In some embodiments, captured images (and / or video) from camera(s) 195, camera 180, or a combination thereof may be analyzed (e.g., using an AI / ML model configured to perform object detection) to determine whether an item 115 (or another object) has been placed on a scale 145 in the bagging area 140. As described in more detail below, in some embodiments, self-checkout system 100 may use information indicating whether an item 115 has been placed on scale 145 to determine whether a zeroing operation of scale 145 should be performed. For example, in certain scenarios, scale 145 may be falsely detecting a weight on scale 145 due to various factors, including, by way of illustrative, non-limiting examples, malfunctioning or defective load sensor(s), environmental factors, a low battery, and an uneven surface. In such cases, upon determining that no item 115 (or other object) is on scale 145, self-checkout system 100 may be configured to automatically perform a zeroing operation of scale 145 to return scale 145 to zero.

[0027]

[0029] Self-checkout system 100 also includes computing system 150. Computing system 150 may be integrated within housing 170 (e.g., as part of display 120) or may be located external to self-checkout system 100 but communicatively coupled to self-checkout system 100 using, for example, an Ethernet cable. Computer system 150 may represent any number of computing devices. For example, computer system 150 may be implemented by a computing device located within housing 170, a server located elsewhere in environment 190, one or more computing devices located in a cloud computing environment, or a combination thereof.

[0028]

[0030] Computing system 150 includes processor 155 and memory 160. Processor 155 represents one or more processing elements, each of which may include one or more processing cores. Memory 160 may be volatile memory, non-volatile memory, or a combination thereof. Memory 160 includes various instructions executable by processor 155 to perform one or more techniques described herein. Here, memory 160 includes a POS application 165 (e.g., a software application) that controls the operation of self-checkout system 100. For example, POS application 165 may include any number of software modules (or suites of software applications) that communicate with scanner 130 (and any integrated scale therein), cameras 175, 180, and 195, display 120, scale 145, POS devices integrated with or located proximate self-checkout system 100, and other components within self-checkout system 100. POS application 165 may receive inputs from and send commands to these components.

[0029]

[0031] The POS application 165 can determine whether the item 115 (or other object) is located on a scale (e.g., the integrated scale of the scanner 130 and / or the scale 145) of the self-checkout system 100. In one embodiment, the POS application 165 can analyze images (and / or video) captured by one or more cameras (e.g., camera 175, camera 180, camera(s) 195) using an AI / ML model configured to perform object detection to determine whether the item 115 (or other object) is located on the scale. In another embodiment, the POS application 165 can receive an indication of whether the item 115 (or other object) is located on the scale. For example, the camera(s) and / or another computing system can analyze the captured images (and / or video) and provide the POS application 165 with an indication of whether the item 115 (or other object) is located on the scale.

[0030]

[0032] If the POS application 165 detects that a scale of the self-checkout system 100 (e.g., the integrated scale of the scanner 130 or the scale 145) is measuring weight and determines that there are no items on the scale, the POS application 165 may perform a scale zeroing operation to return the scale to zero. As mentioned, the zeroing operation may involve determining the falsely detected weight on the scale, setting the scale's "temporary zero weight" parameter equal to the falsely detected weight, and resetting the scale's weight to the difference between the temporary zero weight parameter and the falsely detected weight. In addition to performing the zeroing operation, the POS application 165 may generate a log entry to alert and cause store personnel or technicians to perform maintenance on the scale, for example, to resolve the underlying factors that caused the scale to falsely detect the weight.

[0031]

[0033] If the POS application 165 detects that the scale is measuring weight and determines that there is an item on the scale, the POS application 165 may refrain from performing a zeroing operation on the scale. In such a case, the POS application 165 may display, as part of the scan instructions 125, a request for the customer to remove the item 115 or object from the bagging area 140.

[0032]

[0034] It should be noted that FIG. 1 illustrates an exemplary reference configuration of a self-checkout system 100 capable of implementing the techniques presented herein, and that other configurations of a self-checkout system consistent with the functionality described herein are contemplated.

[0033]

[0035] 2 is a flowchart of a method 200 for zeroing a scale of a self-checkout system, according to one embodiment. Method 200 may be performed by the self-checkout system (e.g., self-checkout system 100, including one or more components thereof). In some cases, method 200 may be performed while the self-checkout system is in service, while a customer is using the self-checkout system to purchase an item, or a combination thereof.

[0034]

[0036] Method 200 begins at block 205, where self-checkout system 100 determines whether a weight is detected on a scale of the self-checkout system. The scale may be a vending scale of the self-checkout system (e.g., an integrated scale of scanner 300), a security scale (or bagging scale) of the self-checkout system (e.g., scale 145), or a combination thereof. Self-checkout system 100 may communicate with the scale, for example, via POS application 165, to determine the current weight detected on the scale. For example, POS application 165 may query scale 145 for information regarding the weight detected on the scale, or may otherwise have access to information regarding the weight detected on the scale.

[0035]

[0037] If, at block 205, the self-checkout system determines that no weight is detected on the self-checkout system scale, method 200 ends. On the other hand, if, at block 205, the self-checkout system determines that weight is detected on the self-checkout system scale, method 200 proceeds to block 210. At block 210, the self-checkout system determines whether an object is located on the scale. The object may be an object associated with environment 190 (e.g., a retail store), such as, for example, item 115, or may be another object not associated with environment 190 (e.g., a non-purchasable object, such as a customer's handbag, an object in the customer's handbag, or any other type of object).

[0036]

[0038] In some embodiments, the self-checkout system may determine whether an object is located on the scale based on evaluating captured images (and / or videos) of the scale using an AI / ML model configured to perform object detection. In this embodiment, the self-checkout system may capture one or more images (and / or videos) of the scale via a camera (e.g., camera 175, camera 180, camera(s) 195), analyze the captured images (and / or videos) using an AI / ML model configured to perform object detection (via POS application 165), and determine (via POS application 165) whether an object is located on the scale based on the analysis.

[0037]

[0039] In some embodiments, the self-checkout system may determine whether an object is located on the scale based on an indication obtained from a camera. For example, a camera (e.g., camera 175, camera 180, camera(s) 195) may include an AI / ML model configured to perform object detection. In such an example, the camera may analyze a captured image (and / or video) of the scale with the AI / ML model and provide an indication to the self-checkout system (POS application 165) of whether an object is located on the scale.

[0038]

[0040] In some embodiments, the self-checkout system may determine whether an object is located on the scale based on an indication obtained from a computing system configured to perform object detection using an AI / ML model. Such a computing system may obtain captured images (and / or video) of the scale from a camera(s), analyze the captured images (and / or video) using the AI / ML model, and provide an indication to the self-checkout system (POS application 165) of whether an object is located on the scale.

[0039]

[0041] It should be noted that the techniques described herein can employ a variety of computer vision algorithms and deep learning methods for object detection and image processing. For example, the AI / ML model(s) used herein for object detection and image processing can include, but are not limited to, histogram of oriented gradients (HOG), region-based convolutional neural networks (R-CNN), Faster R-CNN, single shot detector (SSD), you only look once (YOLO), and RetinaNet, as illustrative, non-limiting examples.

[0040]

[0042] If the self-checkout system determines at block 210 that no object is located on the scale, method 200 ends. On the other hand, if the self-checkout system determines at block 210 that an object is located on the scale, method 200 proceeds to block 215. At block 215, the self-checkout system performs a zeroing operation for the scale. As part of the zeroing operation, the self-checkout system may involve determining a false-detected weight on the scale, setting a "temporary zero weight" parameter of the scale equal to the false-detected weight, and resetting the weight of the scale to the difference between the false-detected weight parameter and the false-detected weight.

[0041]

[0043] At block 220, the self-checkout system generates a support ticket for the scale. For example, the self-checkout system may generate a support ticket to alert store personnel to a potential problem with the scale and cause the store personnel to resolve the potential problem. In some cases, for example, the scale may be misdetecting weight due to factors such as malfunctioning or defective load sensor(s), environmental factors, low battery, and uneven surfaces, as illustrative, non-limiting examples. Note, however, that in some embodiments, method 20 may be performed without generating a support ticket at block 220.

[0042]

[0044] Consider the example scenario shown in Figures 3A and 3B. As shown in Figure 3A, self-checkout system 100 may detect a weight of 0.5 pounds (lbs) on scale 145 and determine that there is no object on the scale. As shown in Figure 3B, after detecting the weight on scale 145 and determining that there is no object on the scale, self-checkout system 100 may perform a zeroing operation for scale 145 to return the scale to a detected weight of 0.0 lbs.

[0043]

[0045] Advantageously, the embodiments described herein can reduce the occurrence of self-checkout systems being taken out of service due to false positive detection of an item(s) on the scale of the self-checkout system. Thus, the embodiments can significantly increase the efficiency of the checkout process at a retail store and improve the customer experience.

[0044]

[0046] As used herein, "processor," "at least one processor," or "one or more processors" generally refer to a single processor configured to perform one or more operations, or multiple processors configured to collectively perform one or more operations. In the case of multiple processors, performing one or more operations may be divided among different processors, although one processor may perform multiple operations and multiple processors may collectively perform a single operation. Similarly, "memory," "at least one memory," or "one or more memories" generally refer to a single memory configured to store data and / or instructions, or multiple memories configured collectively to store data and / or instructions.

[0045]

[0047] The descriptions of various embodiments are presented for illustrative purposes, but are not intended to be exhaustive or limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein has been selected to best explain the principles of the embodiments, practical applications, or technical improvements over technologies found in the market, or to enable those skilled in the art to understand the embodiments disclosed herein.

[0046]

[0048] In the foregoing description, reference is made to the embodiments presented in this disclosure. However, the scope of the present disclosure is not limited to the described embodiments. Rather, any combination of the following features and elements, whether associated with different embodiments or not, is contemplated to implement and practice the contemplated embodiments. Furthermore, while the embodiments disclosed herein may achieve advantages over other possible solutions or prior art, whether or not an advantage is achieved by a given embodiment does not limit the scope of the present disclosure. Accordingly, the following aspects, features, embodiments, and advantages are exemplary only and are not considered elements or limitations of the appended claims unless expressly recited in the claim(s). Similarly, reference to "the present disclosure" should not be considered a generalization of any inventive subject matter disclosed herein, nor should they be considered elements or limitations of the appended claims unless expressly recited in the claim(s). Furthermore, when elements of an embodiment are described in the form of "at least one of A and B" or "at least one of A or B," it will be understood that embodiments including exclusively element A, embodiments including exclusively element B, and embodiments including elements A and B are each contemplated.

[0047]

[0049] Aspects of the described embodiments may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, microcode, etc.), or an embodiment combining software and hardware aspects, which may be collectively referred to herein as a "circuit," "module," or "system."

[0048]

[0050] One or more of the described embodiments may be a system, a method, and / or a computer program product, which may include computer-readable storage medium(s) having computer-readable program instructions for causing a processor to perform aspects of the embodiment.

[0049]

[0051] A computer-readable storage medium may be a tangible device that can hold and store instructions for use by an instruction execution device. A computer-readable storage medium may be, for example, but not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of examples of computer-readable storage media includes portable computer diskettes, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital versatile disc (DVD), memory sticks, floppy disks, mechanical coding devices such as punch cards or ridge structures in grooves with instructions recorded thereon, and any suitable combination of the foregoing. As used herein, computer-readable storage media should not be considered to be transitory signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., light pulses through a fiber optic cable), or electrical signals transmitted through electrical wires.

[0050]

[0052] The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to each computing / processing device or to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network may comprise copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface within each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions to a computer-readable storage medium within the respective computing / processing device for storage.

[0051]

[0053] The computer-readable program instructions for carrying out the operations of the described embodiments may be either assembler instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state-setting data, or source or object code written in any combination of one or more programming languages, including, for example, object-oriented programming languages ​​such as Smalltalk or C++, or conventional procedural programming languages ​​such as the "C" programming language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA) may execute computer-readable program instructions by utilizing state information of the computer-readable program instructions to personalize the electronic circuitry to perform aspects of the described embodiments.

[0052]

[0054] Aspects of the illustrated embodiments are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to the embodiments. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0053]

[0055] These computer-readable program instructions may be provided to a processor of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, executed by the processor of the computer or other programmable data processing apparatus, provide means for implementing the functions / acts specified in one or more blocks of the flowcharts and / or block diagrams. These computer-readable program instructions may also be stored on a computer-readable storage medium that can direct a computer, programmable data processing apparatus, and / or other device to function in the manner described, such that the computer-readable storage medium having instructions stored thereon comprises an article of manufacture containing instructions that implement aspects of the functions / acts specified in one or more blocks of the flowcharts and / or block diagrams.

[0054]

[0056] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus, or other device to produce a computer-implemented process, such that the instructions executing on the computer, other programmable apparatus, or other device implement the functions / operations specified in one or more blocks of the flowcharts and / or block diagrams.

[0055]

[0057] The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of instructions, comprising one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the blocks may occur out of the order noted in the figures. For example, two blocks shown in succession may in fact be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending on the functionality involved. It should also be noted that each block of the block diagrams and / or flowchart diagrams, and combinations of blocks in the block diagrams and / or flowchart diagrams, may be implemented by a dedicated hardware-based system that performs the specified functions or operations or that implements a combination of dedicated hardware and computer instructions.

[0056]

[0058] Embodiments may be delivered to end users through a cloud computing infrastructure. Cloud computing generally refers to the provisioning of scalable computing resources as a service over a network. More formally, cloud computing may be defined as a computing capability that provides abstraction between computing resources and their underlying technical architecture (e.g., servers, storage, network), enabling convenient, on-demand network access to a shared pool of configurable computing resources that can be rapidly provisioned and released with minimal management effort or service provider interaction. Cloud computing thus enables users to access virtual computing resources (e.g., storage, data, applications, and even complete virtualized computing systems) in the "cloud" without regard to the underlying physical systems used to provide the computing resources (or the location of these systems).

[0057]

[0059] Typically, cloud computing resources are provided to users on a pay-per-use basis, where users are charged for the computing resources they actually use (e.g., the amount of storage space consumed by the user or the number of virtualization systems instantiated by the user). Users can access any of the resources present in the cloud at any time from anywhere across the Internet. In the context of the described embodiment, users may access applications (e.g., POS application 165, AI / ML model(s)), and / or associated data (e.g., captured images and / or videos) available in the cloud. For example, POS application 165 may run on a computing system in the cloud and perform one or more techniques described herein for performing scale zeroing for a self-checkout system (e.g., self-checkout system 100). In such a case, POS application 165 may access an AI / ML model in the cloud and evaluate captured images and / or videos using the AI / ML model. POS application 165 may store an indication of whether an object was detected on the scale(s) of the self-checkout system in a storage location in the cloud. Doing so allows users to access this information from any computing system attached to a network connected to the cloud (eg, the Internet).

[0058]

[0060] While the forgoing is directed to one or more embodiments, other and further embodiments may be devised without departing from the basic scope thereof, which scope is determined by the following claims.

Claims

1. 1. A computer-implemented method comprising: Detecting a weight on a scale of a self-checkout system; determining that no object is located on the scale; detecting the weight on the scale and, in response to determining that no object is located on the scale, performing a zeroing operation on the scale; 1. A computer-implemented method comprising:

2. 10. The computer-implemented method of claim 1, further comprising generating a support ticket for the scale in response to detecting the weight on the scale and determining that no object is located on the scale.

3. capturing one or more images of the scale; analyzing the one or more images using a machine learning (ML) model to determine if there is an object located on the scale; The computer-implemented method of claim 1 further comprising:

4. 4. The computer-implemented method of claim 3, wherein determining that no object is located on the scale comprises determining that no object is located on the scale based on the analysis.

5. 10. The computer-implemented method of claim 1, wherein determining that no object is located on the scale comprises receiving an indication from at least one computing device that no object is located on the scale.

6. 6. The computer-implemented method of claim 5, wherein the at least one computing device comprises a camera connected to the self-checkout system, the camera having a field of view of the scale of the self-checkout system.

7. 6. The computer-implemented method of claim 5, wherein the at least one computing device comprises a camera deployed in an environment comprising the self-checkout system, the camera being communicatively connected to the self-checkout system and having a field of the scale of the self-checkout system.

8. 2. The computer-implemented method of claim 1, wherein the scale is an integral scale of a scanner of the self-checkout system or a bagging scale located in a bagging area of ​​the self-checkout system.

9. A self-checkout system, Packing area and a scale disposed in the bagging area; a computing system for controlling the self-checkout system; Equipped with The computing system includes: Detecting a weight on the scale; determining that no object is located on the scale; detecting the weight on the scale and, in response to determining that no object is located on the scale, performing a zeroing operation on the scale; The self-checkout system is configured to:

10. 10. The self-checkout system of claim 9, wherein the computing system is further configured to generate a support ticket for the scale in response to detecting the weight on the scale and determining that no object is located on the scale.

11. 10. The self-checkout system of claim 9, further comprising a camera configured to capture one or more images of the scale, wherein the computing system is configured to analyze the one or more images using a machine learning (ML) model to determine if there is an object located on the scale.

12. The self-checkout system of claim 11 , wherein the computing system is configured to determine, based on the analysis, that no object is located on the scale.

13. 10. The self-checkout system of claim 9, wherein the computing system is configured to receive an indication from at least one computing device that there is no object located on the scale.

14. The self-checkout system of claim 13 , further comprising a camera having a field of view of the scale, and wherein the at least one computing device comprises the camera.

15. 14. The self-checkout system of claim 13, wherein the at least one computing device comprises a camera deployed in an environment comprising the self-checkout system, the camera being communicatively connected to the self-checkout system and having a field of the scale.

16. A non-transitory computer-readable medium comprising computer-executable instructions that, when collectively executed by one or more processors of a computing system, cause the computing system to perform operations, comprising: The operation is Detecting a weight on a scale of a self-checkout system; determining that no object is located on the scale; detecting the weight on the scale and, in response to determining that no object is located on the scale, performing a zeroing operation on the scale; 1. A non-transitory computer-readable medium comprising:

17. 17. The non-transitory computer-readable medium of claim 16, wherein the operations further comprise detecting the weight on the scale and generating a support ticket for the scale in response to determining that no object is located on the scale.

18. The operation is capturing one or more images of the scale; analyzing the one or more images using a machine learning (ML) model to determine if there is an object located on the scale; 20. The non-transitory computer-readable medium of claim 16, further comprising:

19. 20. The non-transitory computer-readable medium of claim 18, wherein determining that no object is located on the scale comprises determining that no object is located on the scale based on the analysis.

20. 17. The non-transitory computer-readable medium of claim 16, wherein determining that no object is located on the scale comprises receiving an indication from at least one computing device that no object is located on the scale.

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