SYSTEMS AND METHODS FOR REDUCING FALSE IDENTIFICATIONS OF PRODUCTS

Abstract

In some embodiments, apparatuses and methods are provided herein useful to processing captured images. In some embodiments, there is provided a system for processing captured images of objects at a product storage facility including a trained machine learning model; and a control circuit. The control circuit may identify a product identifier associated with an object in a captured image; generate predicted product identifiers associated with the object in the captured image based on text identified from the object in the captured image; aggregate the predicted product identifiers; determine a feature of the objects associated with the aggregated predicted product identifiers; determine one or more confusing product identifiers based on a determination of the aggregated predicted product identifiers being associated with the feature; and update a dataset with at least one of the one or more confusing product identifiers and images associated with the one or more confusing product identifiers.

Description

TECHNICAL FIELD

This invention relates generally to recognition of objects in images, and more specifically to training machine learning models to recognize objects in images.

BACKGROUND

A typical product storage facility (e.g., a retail store, a product distribution center, a warehouse, etc.) may have hundreds of shelves and thousands of products stored on the shelves or on pallets. It is common for workers of such product storage facilities to manually (e.g., visually) inspect or inventory product display shelves and/or pallet storage areas to determine which of the products are adequately stocked and which products are or will soon be out of stock and need to be replenished.

Given the very large number of product storage areas such as shelves, pallets, and other product displays at product storage facilities of large retailers, and the even larger number of products stored in the product storage areas, manual inspection of the products on the shelves/pallets by the workers is very time consuming and significantly increases the operations cost for a retailer, since these workers could be performing other tasks if they were not involved in manually inspecting the product storage areas.

BRIEF DESCRIPTION OF THE DRAWINGS

Disclosed herein are embodiments of systems, apparatuses and methods pertaining to processing captured images of objects at a product storage facility. This description includes drawings, wherein:

FIG. 1 is a diagram of an exemplary system of updating inventory of products at a product storage facility in accordance with some embodiments, depicting a front view of a product storage area storing groups of various individual products for sale and stored at a product storage facility;

FIG. 2 comprises a block diagram of an exemplary image capture device in accordance with some embodiments;

FIG. 3 is a functional block diagram of an exemplary computing device in accordance with some embodiments;

FIG. 4 illustrates a simplified block diagram of an exemplary system for processing captured images of objects at a product storage facility in accordance with some embodiments;

FIG. 5 is an example of confusing product identifiers in accordance with some embodiments;

FIG. 6 is an exemplary result of generating product identifiers predictions with Keyword Model in accordance with some embodiments;

FIG. 7 is an exemplary visual illustration of processing captured images of objects at a product storage facility in accordance with some embodiments;

FIG. 8 is an example mapping from a single product identifier to all confusing product identifiers;

FIG. 9 shows a flow diagram of an exemplary method of processing captured images of objects at a product storage facility in accordance with some embodiments;

FIG. 10 shows a flow diagram of an exemplary method of processing captured images of objects at a product storage facility in accordance with some embodiments;

FIG. 11 illustrates an exemplary system for use in implementing methods, techniques, devices, apparatuses, systems, servers, sources and processing captured images of objects at a product storage facility in accordance with some embodiments; and

FIG. 12 is an exemplary visual illustration of a graph network in accordance with some embodiments.

Elements in the figures are illustrated for simplicity and clarity and have not necessarily been drawn to scale. For example, the dimensions and/or relative positioning of some of the elements in the figures may be exaggerated relative to other elements to help to improve understanding of various embodiments of the present invention. Also, common but well-understood elements that are useful or necessary in a commercially feasible embodiment are often not depicted in order to facilitate a less obstructed view of these various embodiments of the present invention. Certain actions and/or steps may be described or depicted in a particular order of occurrence while those skilled in the art will understand that such specificity with respect to sequence is not actually required. The terms and expressions used herein have the ordinary technical meaning as is accorded to such terms and expressions by persons skilled in the technical field as set forth above except where different specific meanings have otherwise been set forth herein.

DETAILED DESCRIPTION

Generally speaking, pursuant to various embodiments, systems, apparatuses and methods are provided herein useful for processing captured images of objects at a product storage facility. In some embodiments, a system for processing captured images of objects at a product storage facility includes a control circuit executing a trained machine learning model stored in a memory. For example, the control circuit executing the trained machine learning model may determine confusing product identifiers corresponding to objects that are at least one of textually similar and visually similar such that the objects can be potentially mis-identified with an incorrect product identifier. In some embodiments, the control circuit executing the trained machine learning model may receive a plurality of captured images. For example, each captured image may depict at least one object for purchase at the product storage facility. In some embodiments, the control circuit executing the trained machine learning model may identify, for each captured image, a product identifier associated with an object in a captured image. In some embodiments, the control circuit executing the trained machine learning model may generate, for each captured image, predicted product identifiers associated with the object in the captured image based on text identified from the object in the captured image. In some embodiments, the control circuit executing the trained machine learning model may aggregate the predicted product identifiers associated with identical product identifiers. In some embodiments, the control circuit executing the trained machine learning model may determine a feature of the objects associated with the aggregated predicted product identifiers that is greater than a feature threshold. In some embodiments, the control circuit executing the trained machine learning model may determine one or more confusing product identifiers based on a determination of the aggregated predicted product identifiers being associated with the feature. In some embodiments, the control circuit executing the trained machine learning model may update a dataset with at least one of the one or more confusing product identifiers and images associated with the one or more confusing product identifiers.

In some embodiments, a method for processing captured images of objects at a product storage facility includes receiving, by a control circuit executing a trained machine learning model stored in a memory to determine confusing product identifiers, a plurality of captured images. For example, each captured image may depict at least one object for purchase at the product storage facility. In some embodiments, the confusing product identifiers correspond to objects that are at least one of textually similar and visually similar such that the objects can be potentially mis-identified with an incorrect product identifier. In some embodiments, the method includes identifying, by the control circuit executing the trained machine learning model and for each captured image, a product identifier associated with an object in a captured image. In some embodiments, the method includes generating, by the control circuit executing the trained machine learning model and for each captured image, predicted product identifiers associated with the object in the captured image based on text identified from the object in the captured image. In some embodiments, the method includes aggregating, by the control circuit executing the trained machine learning model, the predicted product identifiers associated with identical product identifiers. In some embodiments, the method includes determining, by the control circuit executing the trained machine learning model, a feature of the objects associated with the aggregated predicted product identifiers that is greater than a feature threshold. In some embodiments, the method includes determining, by the control circuit executing the trained machine learning model, one or more confusing product identifiers based on a determination of the aggregated predicted product identifiers being associated with the feature. In some embodiments, the method includes updating, by the control circuit executing the trained machine learning model, a dataset with at least one of the one or more confusing product identifiers and images associated with the one or more confusing product identifiers.

FIG. 1 shows an embodiment of a system 100 of updating inventory of products for sale and stored at product storage areas 110 and/or on product storage structures 115 of a product storage facility 105 (which may be a retail store, a product distribution center, a fulfillment center, a warehouse, etc.). The system 100 is illustrated in FIG. 1 for simplicity with only one movable image capture device 120 that moves about one product storage area 110 containing three separate product storage structures 115a, 115b, and 115c, but it will be appreciated that, depending on the size of the product storage facility, the system 100 may include multiple movable image capture devices 120 located throughout the product storage facility that monitor hundreds of product storage areas 110 and thousands of product storage structures 115a-115c. It is understood that the movement about the product storage area 110 by the image capture device(s) 120 may depend on the physical arrangement of the product storage area 110 and/or the size and shape of the product storage structure 115. For example, the image capture device 120 may move linearly down an aisle alongside a product storage structure 115 (e.g., a shelving unit), or may move in a circular fashion around a table having curved or multiple sides.

Notably, the term “product storage structure” as used herein generally refers to a structure on which products 190a-190c may be stored, and may include a rack, a pallet, a shelf cabinet, a single shelf, a shelving unit, table, rack, displays, bins, gondola, case, countertop, or another product display. Likewise, it will be appreciated that the number of individual products 190a-190c representing three exemplary distinct products (labeled as “Cereal 1,” “Cereal 2,” and “Cereal 3”) is chosen by way of example only. Further, the size and shape of the products 190a-190c in FIG. 1 have been shown by way of example only, and it will be appreciated that the individual products 190a-190c may have various sizes and shapes. Notably, the term products 190 may refer to individual products 190 (some of which may be single-piece/single-component products and some of which may be multi-piece/multi-component products), as well as to packages or containers of products 190, which may be plastic- or paper-based packaging that includes multiple units of a given product 190 (e.g., a plastic wrap that includes 36 rolls of identical paper towels, a paper box that includes 10 packs of identical diapers, etc.). Alternatively, the packaging of the individual products 190 may be a plastic- or paper-based container that encloses one individual product 190 (e.g., a box of cereal, a bottle of shampoo, etc.).

The image capture device 120 (also referred to as an image capture unit) of the exemplary system 100 depicted in FIG. 1 is configured to move around the product storage facility (e.g., on the floor via a motorized or non-motorized wheel-based/track-based locomotion system, via slidable tracks above the floor, via a toothed metal wheel/linked metal tracks system, etc.) such that, when moving (e.g., about an aisle or other area of a product storage facility 105), the image capture device 120 has a field of view that includes at least a portion of one or more of the product storage structures 115a-115c within a given product storage area 110 of the product storage facility 105, permitting the image capture device 120 to capture multiple images of the product storage area 110 from various viewing angles. In some embodiments, the image capture device 120 is configured as a robotic device that moves without being physically operated/manipulated by a human operator (as described in more detail below). In other embodiments, the image capture device 120 is configured to be driven or manually pushed (e.g., like a cart or the like) by a human operator. In still further embodiments, the image capture device 120 may be a hand-held or a wearable device (e.g., a camera, phone, tablet, or the like) that may be carried and/or work by a worker at the product storage facility 105 while the worker moves about the product storage facility 105. In some embodiments, the image capture device 120 may be incorporated into another mobile device (e.g., a floor cleaner, floor sweeper, forklift, etc.), the primary purpose of which is independent of capturing images of product storage areas 110 of the product storage facility 105.

In some embodiments, as will be described in more detail below, the images of the product storage area 110 captured by the image capture device 120 while moving about the product storage area are transmitted by the image capture device 120 over a network 130 to an electronic database 140 and/or to a computing device 150. In some aspects, the computing device 150 (or a separate image processing internet-based/cloud-based service module) is configured to process such images as will be described in more detail below.

The exemplary system 100 shown in FIG. 1 includes an electronic database 140. Generally, the exemplary electronic database 140 may be configured as a single database, or a collection of multiple communicatively connected databases (e.g., digital image database, meta data database, inventory database, pricing database, customer database, vendor database, manufacturer database, etc.) and may be configured to store various raw and processed images of the product storage area 110 captured by the image capture device 120 while the image capture device 120 may be moving around the product storage facility 105. In some embodiments, the electronic database 140 and the computing device 150 may be implemented as two separate physical devices located at the product storage facility 105. It will be appreciated, however, that the computing device 150 and the electronic database 140 may be implemented as a single physical device and/or may be located at different (e.g., remote) locations relative to each other and relative to the product storage facility 105. In some aspects, the electronic database 140 may be stored, for example, on non-volatile storage media (e.g., a hard drive, flash drive, or removable optical disk) internal or external to the computing device 150, or internal or external to computing devices distinct from the computing device 150. In some embodiments, the electronic database 140 may be cloud-based. In some embodiments, the electronic database 140 may include one or more memory devices, computer data storage, and/or cloud-based data storage configured to store one or more of product inventories, pricing, and/or demand, and/or customer, vendor, and/or manufacturer data.

The system 100 of FIG. 1 further includes a computing device 150 configured to communicate with the electronic database 140, user devices 160, and/or internet-based services 170, and the image capture device 120 over the network 130. The exemplary network 130 depicted in FIG. 1 may be a wide-area network (WAN), a local area network (LAN), a personal area network (PAN), a wireless local area network (WLAN), Wi-Fi, Zigbee, Bluetooth (e.g., Bluetooth Low Energy (BLE) network), or any other internet or intranet network, or combinations of such networks. Generally, communication between various electronic devices of system 100 may take place over hard-wired, wireless, cellular, Wi-Fi or Bluetooth networked components or the like. In some embodiments, one or more electronic devices of system 100 may include cloud-based features, such as cloud-based memory storage. In some embodiments, portions of the network 130 are located at or in the product storage facility.

The computing device 150 may be a stationary or portable electronic device, for example, a server, a cloud-server, a series of communicatively connected servers, a computer cluster, a desktop computer, a laptop computer, a tablet, a mobile phone, or any other electronic device including a control circuit (i.e., control unit) that includes a programmable processor. The computing device 150 may be configured for data entry and processing as well as for communication with other devices of system 100 via the network 130. As mentioned above, the computing device 150 may be located at the same physical location as the electronic database 140, or may be located at a remote physical location relative to the electronic database 140.

FIG. 2 presents a more detailed example of an exemplary motorized robotic image capture device 120. As mentioned above, the image capture device 102 does not necessarily need an autonomous motorized wheel-based and/or track-based system to move around the product storage facility 105, and may instead be moved (e.g., driven, pushed, carried, worn, etc.) by a human operator, or may be movably coupled to a track system (which may be above the floor level or at the floor level) that permits the image capture device 120 to move around the product storage facility 105 while capturing images of various product storage areas 110 of the product storage facility 105. In the example shown in FIG. 2, the motorized image capture device 120 has a housing 202 that contains (partially or fully) or at least supports and carries a number of components. These components include a control unit 204 comprising a control circuit 206 that controls the general operations of the motorized image capture device 120 (notably, in some implementations, the control circuit 310 of the computing device 150 may control the general operations of the image capture device 120). Accordingly, the control unit 204 also includes a memory 208 coupled to the control circuit 206 and that stores, for example, computer program code, operating instructions and/or useful data, which when executed by the control circuit implement the operations of the image capture device.

The control circuit 206 of the exemplary motorized image capture device 120 of FIG. 2, operably couples to a motorized wheel system 210, which, as pointed out above, is optional (and for this reason represented by way of dashed lines in FIG. 2). This motorized wheel system 210 functions as a locomotion system to permit the image capture device 120 to move within the product storage facility 105 (thus, the motorized wheel system 210 may be more generically referred to as a locomotion system). Generally, this motorized wheel system 210 may include at least one drive wheel (i.e., a wheel that rotates around a horizontal axis) under power to thereby cause the image capture device 120 to move through interaction with, e.g., the floor of the product storage facility. The motorized wheel system 210 can include any number of rotating wheels and/or other alternative floor-contacting mechanisms (e.g., tracks, etc.) as may be desired and/or appropriate to the application setting.

The motorized wheel system 210 may also include a steering mechanism of choice. One simple example may comprise one or more wheels that can swivel about a vertical axis to thereby cause the moving image capture device 120 to turn as well. It should be appreciated the motorized wheel system 210 may be any suitable motorized wheel and track system known in the art capable of permitting the image capture device 120 to move within the product storage facility 105. Further elaboration in these regards is not provided here for the sake of brevity save to note that the aforementioned control circuit 206 is configured to control the various operating states of the motorized wheel system 210 to thereby control when and how the motorized wheel system 210 operates.

In the exemplary embodiment of FIG. 2, the control circuit 206 operably couples to at least one wireless transceiver 212 that operates according to any known wireless protocol. This wireless transceiver 212 can comprise, for example, a Wi-Fi-compatible and/or Bluetooth-compatible transceiver (or any other transceiver operating according to known wireless protocols) that can wirelessly communicate with the aforementioned computing device 150 via the aforementioned network 130 of the product storage facility. So configured, the control circuit 206 of the image capture device 120 can provide information to the computing device 150 (via the network 130) and can receive information and/or movement instructions (instructions from the computing device 150. For example, the control circuit 206 can receive instructions from the computing device 150 via the network 130 regarding directional movement (e.g., specific predetermined routes of movement) of the image capture device 120 throughout the space of the product storage facility 105. These teachings will accommodate using any of a wide variety of wireless technologies as desired and/or as may be appropriate in a given application setting. These teachings will also accommodate employing two or more different wireless transceivers 212, if desired.

In the embodiment illustrated in FIG. 2, the control circuit 206 also couples to one or more on-board sensors 214 of the image capture device 120. These teachings will accommodate a wide variety of sensor technologies and form factors. According to some embodiments, the image capture device 120 can include one or more sensors 214 including but not limited to an optical sensor, a photo sensor, an infrared sensor, a 3-D sensor, a depth sensor, a digital camera sensor, a mobile electronic device (e.g., a cell phone, tablet, or the like), a quick response (QR) code sensor, a radio frequency identification (RFID) sensor, a near field communication (NFC) sensor, a stock keeping unit (SKU) sensor, a barcode (e.g., electronic product code (EPC), universal product code (UPC), European article number (EAN), global trade item number (GTIN)) sensor, or the like.

By one optional approach, an audio input 216 (such as a microphone) and/or an audio output 218 (such as a speaker) can also operably couple to the control circuit 206. So configured, the control circuit 206 can provide a variety of audible sounds to thereby communicate with workers at the product storage facility or other motorized image capture devices 120 moving around the product storage facility 105. These audible sounds can include any of a variety of tones and other non-verbal sounds. Such audible sounds can also include, in lieu of the foregoing or in combination therewith, pre-recorded or synthesized speech.

The audio input 216, in turn, provides a mechanism whereby, for example, a user (e.g., a worker at the product storage facility 105) provides verbal input to the control circuit 206. That verbal input can comprise, for example, instructions, inquiries, or information. So configured, a user can provide, for example, an instruction and/or query (e.g., where is pallet number so-and-so?, how many products are stocked on pallet number so-and-so? etc.) to the control circuit 206 via the audio input 216.

In the embodiment illustrated in FIG. 2, the motorized image capture device 120 includes a rechargeable power source 220 such as one or more batteries. The power provided by the rechargeable power source 220 can be made available to whichever components of the motorized image capture device 120 require electrical energy. By one approach, the motorized image capture device 120 includes a plug or other electrically conductive interface that the control circuit 206 can utilize to automatically connect to an external source of electrical energy to thereby recharge the rechargeable power source 220.

In some embodiments, the motorized image capture device 120 includes an input/output (I/O) device 224 that is coupled to the control circuit 206. The I/O device 224 allows an external device to couple to the control unit 204. The function and purpose of connecting devices will depend on the application. In some examples, devices connecting to the I/O device 224 may add functionality to the control unit 204, allow the exporting of data from the control unit 206, allow the diagnosing of the motorized image capture device 120, and so on.

In some embodiments, the motorized image capture device 120 includes a user interface 224 including for example, user inputs and/or user outputs or displays depending on the intended interaction with the user (e.g., worker at the product storage facility 105). For example, user inputs could include any input device such as buttons, knobs, switches, touch sensitive surfaces or display screens, and so on. Example user outputs include lights, display screens, and so on. The user interface 224 may work together with or separate from any user interface implemented at an optional user interface unit or user device 160 (such as a smart phone or tablet device) usable by a worker at the product storage facility. In some embodiments, the user interface 224 is separate from the image capture device 202, e.g., in a separate housing or device wired or wirelessly coupled to the image capture device 202. In some embodiments, the user interface may be implemented in a mobile user device 160 carried by a person and configured for communication over the network 130 with the image capture device 102.

In some embodiments, the motorized image capture device 120 may be controlled by the computing device 150 or a user (e.g., by driving or pushing the image capture device 120 or sending control signals to the image capture device 120 via the user device 160) on-site at the product storage facility 105 or off-site. This is due to the architecture of some embodiments where the computing device 150 and/or user device 160 outputs the control signals to the motorized image capture device 120. These controls signals can originate at any electronic device in communication with the computing device 150 and/or motorized image capture device 120. For example, the movement signals sent to the motorized image capture device 120 may be movement instructions determined by the computing device 150; commands received at the user device 160 from a user; and commands received at the computing device 150 from a remote user not located at the product storage facility 105.

In the embodiment illustrated in FIG. 2, the control unit 204 includes a memory 208 coupled to the control circuit 206 and that stores, for example, computer program code, operating instructions and/or useful data, which when executed by the control circuit implement the operations of the image capture device. The control circuit 206 can comprise a fixed-purpose hard-wired platform or can comprise a partially or wholly programmable platform. These architectural options are well known and understood in the art and require no further description here. This control circuit 206 is configured (for example, by using corresponding programming stored in the memory 208 as will be well understood by those skilled in the art) to carry out one or more of the steps, actions, and/or functions described herein. The memory 208 may be integral to the control circuit 206 or can be physically discrete (in whole or in part) from the control circuit 206 as desired. This memory 208 can also be local with respect to the control circuit 206 (where, for example, both share a common circuit board, chassis, power supply, and/or housing) or can be partially or wholly remote with respect to the control circuit 206. This memory 208 can serve, for example, to non-transitorily store the computer instructions that, when executed by the control circuit 206, cause the control circuit 206 to behave as described herein.

In some embodiments, the control circuit 206 may be communicatively coupled to one or more trained computer vision/machine learning/neural network modules 222 to perform at some of the functions. For example, the control circuit 206 may be trained to process one or more images of product storage areas 110 at the product storage facility 105 to detect and/or recognize one or more products 190 using one or more machine learning algorithms, including but not limited to Linear Regression, Logistic Regression, Decision Tree, SVM, Naïve Bayes, kNN, K-Means, Random Forest, Dimensionality Reduction Algorithms, Gradient Boosting Algorithms, Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Deep Neural Network (DNN), and/or algorithms associated with neural networks. In some embodiments, the trained machine learning model 222 includes a computer program code stored in a memory 208 and/or executed by the control circuit 206 to process one or more images, as described in more detail below.

It is noted that not all components illustrated in FIG. 2 are included in all embodiments of the motorized image capture device 120. That is, some components may be optional depending on the implementation of the motorized image capture device 120.

With reference to FIG. 3, the exemplary computing device 150 configured for use with exemplary systems and methods described herein may include a control circuit 310 including a programmable processor (e.g., a microprocessor or a microcontroller) electrically coupled via a connection 315 to a memory 320 and via a connection 325 to a power supply 330. The control circuit 310 can comprise a fixed-purpose hard-wired platform or can comprise a partially or wholly programmable platform, such as a microcontroller, an application specification integrated circuit, a field programmable gate array, and so on. These architectural options are well known and understood in the art and require no further description here.

The control circuit 310 can be configured (for example, by using corresponding programming stored in the memory 320 as will be well understood by those skilled in the art) to carry out one or more of the steps, actions, and/or functions described herein. In some embodiments, the memory 320 may be integral to the processor-based control circuit 310 or can be physically discrete (in whole or in part) from the control circuit 310 and is configured non-transitorily store the computer instructions that, when executed by the control circuit 310, cause the control circuit 310 to behave as described herein. (As used herein, this reference to “non-transitorily”will be understood to refer to a non-ephemeral state for the stored contents (and hence excludes when the stored contents merely constitute signals or waves) rather than volatility of the storage media itself and hence includes both non-volatile memory (such as read-only memory (ROM)) as well as volatile memory (such as an erasable programmable read-only memory (EPROM))). Accordingly, the memory and/or the control unit may be referred to as a non-transitory medium or non-transitory computer readable medium.

The control circuit 310 of the computing device 150 is also electrically coupled via a connection 335 to an input/output 340 that can receive signals from, for example, from the image capture device 120, etc., the electronic database 140, internet-based services 170 (e.g., image processing services, computer vision services, neural network services, etc.), and/or from another electronic device (e.g., an electronic or user device of a worker tasked with physically inspecting the product storage area 110 and/or the product storage structures 115a-115c and observe the individual products 190a-190c stocked thereon. The input/output 340 of the computing device 150 can also send signals to other devices, for example, a signal to the electronic database 140 including an image of a given product storage structure 115b selected by the control circuit 310 of the computing device 150 as fully showing the product storage structure 115b and each of the products 190b stored in the product storage structure 115b. Also, a signal may be sent by the computing device 150 via the input-output 340 to the image capture device 120 to, for example, provide a route of movement for the image capture device 120 through the product storage facility.

The processor-based control circuit 310 of the computing device 150 shown in FIG. 3 may be electrically or wirelessly coupled via a connection 345 to a user interface 350, which may include a visual display or display screen 360 (e.g., LED screen) and/or button input 370 that provide the user interface 350 with the ability to permit a user (e.g., worker at a the product storage facility 105 or a worker at a remote regional center) to access the computing device 150 by inputting commands via touch-screen and/or button operation and/or voice commands. Possible commands may, for example, cause the computing device 150 to cause transmission of an alert signal to an electronic mobile user device 160 of a worker at the product storage facility 105 to assign a task to the worker that requires the worker to visually inspect and/or restock a given product storage structure 115a-115c based on analysis by the computing device 150 of the image of the product storage structure 115a-115c captured by the image capture device 120.

In some embodiments, the user interface 350 of the computing device 150 may also include a speaker 380 that provides audible feedback (e.g., alerts) to the operator of the computing device 150. It will be appreciated that the performance of such functions by the processor-based control circuit 310 of the computing device 150 is not dependent on a human operator, and that the control circuit 210 may be programmed to perform such functions without a human user.

As pointed out above, in some embodiments, the image capture device 120 moves around the product storage facility 105 (while being controlled remotely by the computing device 150 (or another remote device such as the user device 160), or while being controlled autonomously by the control circuit 206 of the image capture device 120), or while being manually driven or pushed by a worker of the product storage facility 105. When the image capture device 120 moves about the product storage area 110 as shown in FIG. 1, the sensor 214 of the image capture device 120, which may be one or more digital cameras, captures (in sequence) multiple images of the product storage area 110 from various angles. In some aspects, the control circuit 310 of the computing device 150 obtains (e.g., from the electronic database 140 or directly from the image capture device 120) the images of the product storage area 110 captured by the image capture device 120 while moving about the product storage area 110.

The sensor 214 (e.g., digital camera) of the image capture device 120 is located and/or oriented on the image capture device 120 such that, when the image capture device 120 moves about the product storage area 110, the field of view of the sensor 214 includes only portions of adjacent product storage structures 115a-115c, or an entire product storage structure 115a-115c. In certain aspects, the image capture device 120 is configured to move about the product storage area 110 while capturing images of the product storage structures 115a-115c at certain predetermined time intervals (e.g., every 1 second, 5 seconds, 10 seconds, etc.).

The images captured by the image capture device 120 may be transmitted to the electronic database 140 for storage and/or to the computing device 150 for processing by the control circuit 310 and/or to a web-/cloud-based image processing service 170. In some embodiments, one or more of the image capture devices 120 of the exemplary system 100 depicted in FIG. 1 is mounted on or coupled to a motorized robotic unit similar to the motorized robotic image capture device 120 of FIG. 2.

In some embodiments, one or more of the image capture devices 120 of the exemplary system 100 depicted in FIG. 1 is configured to be stationary or mounted to a structure, such that the image capture device 120 may capture one or more images of an area having one or more products at the product storage facility. For example, the area may include a product storage area 110, and/or a portion of and/or an entire product storage structures 115a-115c of the product storage facility.

In some embodiments, the electronic database 140 stores data corresponding to the inventory of products in the product storage facility. The control circuit 310 processes the images captured by the image capture device 120 and causes an update to the inventory of products in the electronic database 140. In some embodiments, one or more steps in the processing of the images are via machine learning and/or computer vision models that may include one or more trained neural network models. In certain aspects, the neural network may be a deep convolutional neural network. The neural network may be trained using various data sets, including, but not limited to: raw image data extracted from the images captured by the image capture device 120; metadata extracted from the images captured by the image capture device 120; reference image data associated with reference images of various product storage structures 115a-115c at the product storage facility; reference images of various products 190a-190c stocked and/or sold at the product storage facility; and/or planogram data associated with the product storage facility.

FIG. 4 illustrates a simplified block diagram of an exemplary system for labeling objects in images captured at one or more product storage facilities in accordance with some embodiments. The system 400 includes a control circuit 310. Alternatively or in addition to, the system 400 may include memory storage/s 402, a user interface 350, and/or product storage facilities 105 coupled via a network 130. In some embodiments, the memory storage/s 402 may be one or more of a cloud storage network, a solid state drive, a hard drive, a random access memory (RAM), a read only memory (ROM), and/or any storage devices capable of storing electronic data, or any combination thereof. In some embodiments, the memory storage/s 402 includes the memory 320. In such an embodiment, a trained machine learning model 404 includes trained machine learning model/s 390. In some embodiments, the memory storage/s 402 is separate and distinct from the memory 320. In such an embodiment, the trained machine learning model 404 may be associated with the trained machine learning model/s 390. For example, the trained machine learning model/s 390 may be a copied version of the trained machine learning model 404. Alternatively or in addition to, the trained machine learning model 222 may be a copied version of the trained machine learning model 404. In some embodiments, the processing of unprocessed captured images is processed by the trained machine learning model 222.

In some embodiments, the memory storage/s 402 includes a trained machine learning model 404 and/or a database 140. In some embodiments, the database 140 may be an organized collection of structured information, or data, typically stored electronically in a computer system (e.g. the system 100). In some embodiments, the database 140 may be controlled by a database management system (DBMS). In some embodiments, the DBMS may include the control circuit 310. In yet some embodiments, the DBMS may include another control circuit (not shown) separate and/or distinct from the control circuit 310.

In some embodiments, the control circuit 310 may be communicatively coupled to the trained machine learning model 404 including one or more trained computer vision/machine learning/neural network modules to perform at some or all of the functions described herein. For example, the control circuit 310 using the trained machine learning model 404 may be trained to process one or more images of product storage areas (e.g., aisles, racks, shelves, pallets, to name a few) at product storage facilities 105 to detect and/or recognize one or more products for purchase using one or more machine learning algorithms, including but not limited to Linear Regression, Logistic Regression, Decision Tree, SVM, Naïve Bayes, kNN, K-Means, Random Forest, Dimensionality Reduction Algorithms, Gradient Boosting Algorithms, Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Deep Neural Network (DNN), and/or algorithms associated with neural networks. In some embodiments, the trained machine learning model 404 includes a computer program code stored in the memory storage/s 402 and/or executed by the control circuit 310 to process one or more images, as described herein.

The product storage facility 105 may include one of a retail store, a distribution center, and/or a fulfillment center. In some embodiments, a user interface 350 includes an application stored in a memory (e.g., the memory 320 or the memory storage/s 402) and executable by the control circuit 310. In some embodiments, the user interface 350 may be coupled to the control circuit 310 and may be used by a user to at least one of associate a product with at least one depicted object in processed images or resolve that one or more objects depicted in the images is only associated with a single product. In some embodiments, an output of the user interface 350 is used to retrain the trained machine learning model 404.

In some embodiments, the trained machine learning model 404 processes unprocessed captured images. For example, unprocessed captured images may include images captured by and/or output by the image capture device/s 120. Alternatively or in addition to, the unprocessed captured images may include images that have not gone through object detection or object classification by the control circuit 310. In some embodiments, at least some of the unprocessed captured images depict objects in the product storage facility 105.

In some embodiments, the control circuit 310 may use another/other trained machine learning model 408 to detect the objects and enclose each detected object inside the bounding box. The other trained machine learning model 408 may be distinct from the trained machine learning model 404.

In illustrative non-limiting examples, FIGS. 5-10 and 12 are concurrently described below. FIG. 5 is an example of confusing product identifiers in accordance with some embodiments. FIG. 6 is an exemplary result of generating product identifiers predictions with Keyword Model in accordance with some embodiments. FIG. 7 is an exemplary visual illustration of processing captured images of objects at a product storage facility 105 in accordance with some embodiments. FIG. 8 is an example mapping from a single product identifier to all confusing product identifiers. FIG. 9 shows a flow diagram of an exemplary method 900 of processing captured images of objects at a product storage facility 105 in accordance with some embodiments. FIG. 10 shows a flow diagram of an exemplary method 1000 of processing captured images of objects at a product storage facility 105 in accordance with some embodiments. For example, one or more image capture devices 120 may capture images (e.g., the image 500) of objects at the product storage facility 105. In some embodiments, a plurality of objects may include items for commercial sale. Alternatively or in addition to, at least one of the one or more image capture devices 120 may be coupled to the motorized robotic unit 406. In some embodiments, the database 140 may store the images. For example, the images may include images that have not gone through object detection or object classification by the control circuit. In another example, the images may include images that may have gone through an object detection and/or an object classification. In some embodiments, an image that have gone through an object detection may include an image output by the control circuit 310.

For example, the control circuit 310 executing the trained machine learning model 404 may determine confusing product identifiers. In some embodiments, the trained machine learning model 404 includes one or more machine learning models each trained to perform a corresponding operation executed by the control circuit to determine the confusing product identifiers. In some embodiments, a first trained machine learning model of the one or more machine learning models may be trained to perform the generation of the predicted product identifiers based on a determination of score values associated with the stored product identifiers based on at least one or more steps. For example, the one or more steps may include determining text associated with the stored product identifiers that matches the most relative to other text associated with the stored product identifiers with the text identified from the object in the captured image. Alternatively or in addition to, the one or more steps may include comparing whether a location associated with the text identified from the object in the captured image matches with one or more locations associated with the most matching text associated with the stored product identifiers within a threshold range. Alternatively or in addition to, the one or more steps may include determining whether one or more of the stored product identifiers and the object in the captured image are associated with a matching presence of a first text and a matching absence of a second text. In some embodiments, the predicted product identifiers include those stored product identifiers having corresponding score values (or confidence scores/values) that are greater than a score threshold.

In some embodiments, a first trained machine learning model of the one or more machine learning models may be trained to perform a determination of the feature of the objects associated with the aggregated predicted product identifiers based on metric learning algorithm.

In some embodiments, the confusing product identifiers may correspond to objects that are at least one of textually similar and visually similar such that the objects can be potentially mis-identified with an incorrect product identifier. For example, in FIG. 5, there are three separate images 502, 504, 506 each depicting a different flavor of a Premier Protein drink. Alternatively or in addition to, each object in the three images 502, 504, 506 may also be visually different. For example, each object in the three images 502, 504, 506 may be in different colors corresponding with the flavors. As such, the control circuit 310 executing the trained machine learning model 404 may determine that the objects depicted in the three images 502, 504, 506 may correspond to the same product (e.g., the Premier Protein drink) despite having different product identifiers, texts (e.g., flavors), and/or visual appearance (e.g., product packaging colors). In recognizing that the objects depicted in the three images 502, 504, 506 are of the same product, the control circuit 310 executing the trained machine learning model 404, at 1002, may receive a plurality of captured images. Each captured image may depict at least one object for purchase at the product storage facility 105. For example, the control circuit 310 and/or the trained machine learning model 404, at 902, may crop each product identifier (e.g., UPC code or QR code, to name a few) and/or object depicted in an image. In some embodiments, the control circuit 310 and/or the trained machine learning model 404, at 904, may obtain metadata associated with each product identifier and/or object. In some embodiments, each captured image includes metadata information determined by the control circuit 310 and/or the trained machine learning model 404. For example, the control circuit 310 and/or the trained machine learning model 404 may extract metadata from the depicted image of the product identifier and/or object. In some embodiments, metadata comprises Optical Character Recognition (OCR), store identification, cropped bounding box detection annotations, to name a few.

For example, the control circuit 310 and/or the trained machine learning model 404 may augment an image by overlaying each detected object on the image with a bounding box. It is understood that as used herein, the term bounding box is intended to be any shape that surrounds or defines boundaries about a detected object in an image. That is, a bounding box may be in the shape of a square, rectangle, circle, oval, triangle, and so on, or may be any irregular shape having curved, angled, straight and/or irregular sections within which the object is located, the irregular shape may loosely conform to the shape of the object or not. Further, a bounding box may not be complete in that it could include open sections (such that the bounding box is formed by connecting the dots). In any event, embodiments of a bounding box can be defined as a shape that surrounds or defines boundaries about a detected object.

In some embodiments, the metadata, at 906, may be provided to an Image Level Intelligence Schema. In some embodiments, the Image Level Intelligence schema may be designed to store efficiently the Input UPCs (e.g., product identifiers input by user/s to be stored at database 140), image details, confusing UPCs or product identifiers for each Image, excluded UPCs or product identifiers, OCR, retail facility or club identification, prediction status (e.g., Correct, Wrong and/or No prediction). In some embodiments, the UPC or product identifier Level schema may be constructed after applying multiple aggregation logics on the Image Level Intelligence Schema only. Alternatively or in addition to, the Image Level Intelligence may construct the complete lineage to help in providing debugging capabilities for errors in the process of constructing, determining, and/or generating confusing UPC or product identifier list or dataset. In some embodiments, the control circuit 310 and/or the trained machine learning model 404, at 1004, may identify, for each captured image, a product identifier associated with an object in a captured image. For example, the control circuit 310 and/or the trained machine learning model 404 may determine the text depicted on the image of an object and/or find stored product identifiers that may have a threshold match of associated text to the text depicted on the image. In some embodiments, a stored product identifier that has an associated text that matches with the text depicted on the image may be determined by the control circuit 310 and/or the trained machine learning model 404 to be the corresponding product identifier of the object depicted on the image.

In some embodiments, the control circuit 310 and/or the trained machine learning model 404, at 1006, may generate, for each captured image, predicted product identifiers associated with the object in the captured image based on text identified from the object in the captured image. For example, the control circuit 310 and/or the trained machine learning model 404, at 908, may perform keyword model predictions. In such examples, each stored product identifier may be associated with one or more keywords or text particularly associated with the particular stored product identifier. In some embodiments, the control circuit 310 and/or the trained machine learning model 404 may determine whether the text identified from the object in the image matches with one or more keywords. For example, in response to matching with one or more keywords, the control circuit 310 and/or the trained machine learning model 404 may generate the predicted product identifiers with those stored product identifiers associated with the most matched one or more keywords (for example, the top 3, 5, or 10). In some embodiments, the control circuit 310 and/or the trained machine learning model 404 may further narrow down the generated predicted product identifiers by matching the location of the most frequent text identified from the object with the location of the most frequent text associated with the stored product identifiers. By one approach, a location of the text on an object is the text's position relative to the coordinate associated with the bounding box corresponding to the object. In an illustrative non-limiting example, as shown in FIG. 6, the first predicted product identifier 602 may be included with the generated predicted product identifiers based on the fuzzy match count of 7 and/or match count of 6 (that is one or both being greater than a predetermined fuzzy match threshold and/or a predetermined exact match threshold, respectively). In some embodiments, the second predicted product identifier 604 and the third predicted product identifier 606 may be excluded from the generated predicted product identifiers based on having fuzzy match counts less than a fuzzy match threshold (e.g., a fuzzy match threshold of 3). In such examples, each of the product identifiers stored in the database 140 may be associated with one or more locations of text, the most frequent text, and/or presence and/or non-presence of particular keywords or text on a corresponding object associated with the product identifier.

In some embodiments, in response to the keyword model predictions at 908, the control circuit 310 and/or the trained machine learning model 404, at 910, may create a confusing UPCs list or confusing product identifiers list. For example, after performing the keyword model predictions, the control circuit 310 and/or the trained machine learning model 404 may output a list of stored product identifiers predicted to at least include the correct product identifier of the object depicted on the image. By one approach, the output may also include a corresponding confidence score or value indicating the likelihood that the corresponding stored product identifier matches with the object depicted on the image. In some embodiments, the confidence score or value is determined based on a comparison of the locations of text on the object of the image to the locations of text associated with the compared stored product identifier, a comparison of the most frequent text depicted on the object of the image to the most frequent text associated with the compared stored product identifier, and/or presence and/or non-presence (or absence) of particular keywords or text associated with the compared stored product identifier to the text depicted on the object of the image.

In some embodiments, the control circuit 310 and/or the trained machine learning model 404, at 912, may determine which of the stored product identifiers on the list created at 910 have a match count and/or a fuzzy match count that are greater than the predetermined exact match threshold and/or the predetermined fuzzy match threshold, respectively. Alternatively or in addition to, the stored product identifier having the match count greater than the predetermined exact match threshold and having the most matched count may correspond to the stored product identifier that is the most highly predicted to be the correct product identifier, thereby enabling the control circuit 310 and/or the trained machine learning model 404 to conclude that the stored product identifier to be the correct product identifier. Alternatively or in addition to, the stored product identifiers having the fuzzy match counts that are less than or equal to the predetermined fuzzy match threshold may be excluded by the control circuit 310 and/or the trained machine learning model 404, at 914, from the list created at 910. In some embodiments, the control circuit 310 and/or the trained machine learning model 404, at 906, may take the excluded list of UPCs or product identifiers from the 914 step in order to include it into the Image Level Intelligence schema, which is constructing the complete lineage to help in providing debugging capabilities for errors in the process of constructing, determining, and/or generating confusing UPC or product identifier list or dataset. Alternatively or in addition to, the generated predicted product identifiers may include the stored product identifiers having the fuzzy match counts that are greater than the predetermined fuzzy match threshold.

In some embodiments, the generated predicted product identifiers may correspond to the stored product identifiers that are associated with objects that are at least one of textually similar and visually similar to each other but can be potentially mis-identified as not being the same product or referring to the same product. For example, they are those objects that may be referring to the same product and may be textually and/or visually similar to one another, but may be mis-identified as being different products because each object is associated with a different product identifier. Thus, mis-identification of products or false positive identification of products (i.e., identification of objects depicted on an image as corresponding to at least two different product identifiers, thus implying two different products, when in actuality the objects are of the same product) may be reduced and accuracy of product recognition may be improved by training the trained machine learning model 404 to recognize the objects of the same products with the same product identifier and reducing the confusion with confusing product identifiers.

In some embodiments, the control circuit 310 and/or the trained machine learning model 404, at 916 and at 1008, may aggregate the predicted product identifiers associated with identical product identifiers at UPC level (or product identifier level) to create a mapping from a single product identifier (e.g., the correct product identifier described above) to all confusing product identifiers (e.g., those product identifiers included in the generated predicted product identifiers described above). In some embodiments, the mapping may include the frequency of occurrence of the confusing product identifiers in the product identifiers stored in the database 140 and/or the dataset used to train the trained machine learning model 404. For example, in FIG. 8, a mapping of four product identifiers 802, 804, 806, 808, 810 are shown. By one approach, a product identifier 804 is mapped to another product identifier 812 with a frequency of occurrence of 27.

In some embodiments, a first trained machine learning model of the one or more machine learning models 404 may be trained to perform a determination of the feature of the objects associated with the aggregated predicted product identifiers based on metric learning algorithm described herein.

In some embodiments, the control circuit 310 and/or the trained machine learning model 404, at 918, may perform graph clustering of the aggregated predicted product identifiers (e.g., clusters 1202, 1204a, 1206a, 1208, and 1210). For example, the control circuit 310 and/or the trained machine learning model 404 may create an undirected graph of UPCs or product identifiers as nodes having edge between two nodes if they are strongly present to each other's confusion UPCs list or product identifier's list In some embodiments, the frequency of co-occurrence of the confusing product identifiers as described above may be used as weight of the edges (normalized between 0 and 1) in a graph network as illustrated in FIG. 12. Alternatively or in addition to, edges may be added only if the co-occurrence is above a certain threshold (e.g., a feature threshold). For example, the threshold is determined by conducting experiments on sample data to maximize the grouping of only similar products together. In an illustrative non-limiting example, the resulting graph with weighted edges is illustrated in FIG. 12. As shown in FIG. 12, the graph network may be partitioned using Louvain Community Detection Algorithm, for example. Louvain community detection algorithm is an open source method to extract clusters or communities from graph networks.

In some embodiments, the control circuit 310 and/or the trained machine learning model 404, at 920, may perform target components generation (e.g., clusters 1204b and 1206b). For example, the target components generation may, at 1010, include the control circuit 310 and/or the trained machine learning model 404 determining a feature of the objects associated with the aggregated predicted product identifiers that is greater than a feature threshold as explained above. For example, for each connected component or cluster in the undirected graph of UPCs or product identifiers, the control circuit 310 and/or the trained machine learning model 404 may evaluate and/or generate metrices for feature vector model performance at a member UPC level using sample training images available for each UPC or product identifier. In some embodiments, the cluster/component level feature vector model performance is calculated by taking the minimum of all UPC or product identifier performances within the component.

In some embodiments, the target components generation may, at 1012, include the control circuit 310 and/or the trained machine learning model 404 determining one or more confusing product identifiers based on a determination of the aggregated predicted product identifiers being associated with the feature. In some embodiments, if the feature vector model performance at the component level is more than a component threshold (e.g., a predetermined fixed threshold or a range of predetermined threshold), the control circuit 310 and/or the trained machine learning model 404 may select the component as a target component In some embodiments, the minimum accuracy that may be acceptable to the business use case at hand is taken as the threshold for considering a component as target component

In some embodiments, the feature vector model may be based on metric learning. It is understood that person ordinary skilled in the art understands the general concept of metric learning. However, the feature vector model described herein may be particularly built with Efficient Net BO as the backbone and a Linear Layer below added at the top which gives a 128-d embedding vector as the output. The feature vector model may be fine-tuned in a triplet fashion with an online semi-hard mining strategy. In some embodiments, the 128-d is chosen as it provides good performance keeping downstream KNN computation within desirable time limits. Since feature vector model may be based on metric learning, the feature vector model may scale to new product identifiers or UPCs by just adding representative images of these product identifiers or UPCs (for example, images of the objects associated with confusing product identifiers or confusing UPCs as described herein) as template or datasets. In some embodiments, the trained machine learning model 404 may be originally trained with a few images of objects associated with product identifiers but may still recognize other product identifiers (e.g., new product identifiers, confusing product identifiers, to name a few) by automatically updating the template or the datasets with images and/or data associated with the visual and/or textual information of the objects depicted on the images and/or the other product identifiers. In an illustrative non-limiting example, the control circuit 310 and/or the trained machine learning model 404, at 1014, may update a dataset with at least one of the one or more confusing product identifiers and images associated with the one or more confusing product identifiers. In some embodiments, the control circuit 310 and/or the trained machine learning model 404, at 922, may modify ensemble logic of keyword model and feature vector model for target components. For example, for all product identifiers or UPCs residing in the target components, the control circuit 310 and/or the trained machine learning model 404 may modify keyword model and feature vector model ensemble logic to reduce false positive mistakes.

For example, final recognition score, which may be used for thresholding and ordering the UPCs or product identifiers in the final prediction list, may include a combination of keyword score and feature vector score. In some embodiments, for final UPCs or product identifiers, which are not lying within any of the target components, the keyword and feature vector scores may be given equal importance. In some embodiments, the components, where Feature vector model may be able to precisely or within a threshold range distinguish among the resident UPCs or product identifiers only with their visual appearances, may be regarded as target components. In an illustrative non-limiting example, the Keyword and Feature Vector ensemble logic may be modified if two strong UPCs or product identifiers in the final prediction list or dataset are lying in any one of the target components. In such examples, full importance may be given to feature vector score only and keyword score may not be used at all. In another illustrative non-limiting example, the Keyword and Feature Vector ensemble logic may be modified when any two UPCs or product identifiers (Strong or Weak) are lying in any one of the target component. In such, examples, Feature Vector score may only be used. It is understood that many other ways may be used in combination to have the best impact on reduction of false positives.

In some embodiments, to determine the confusing product identifiers, the control circuit 310 executing the trained machine learning model 404 identifies, based on the updated dataset, correct product identifiers to associate with at least one of textually similar and visually similar objects depicted in the captured images reducing mis-identification and/or false positive identifications.

FIG. 7 provides another non-limiting illustrative example of processing captured images of objects at a product storage facility as described herein. In some embodiments, the control circuit 310 and/or the trained machine learning model 404, at 702, may receive images 1 through 5. Each image, for example, may an object for purchase at the product storage facility 105. Alternatively or in addition to, the control circuit 310 and/or the trained machine learning model 404, at 704, may generate product identifier or UPC predictions based on the keyword model described herein. Alternatively or in addition to, the control circuit 310 and/or the trained machine learning model 404 may determine strong and weak UPCs or product identifiers based on their corresponding confidence scores or values indicating the likelihood that the corresponding product identifier is the correct product identifier. In some embodiments, the control circuit 310 and/or the trained machine learning model 404 may identify the correct product identifier 706 to associate with the image based on the predicted product identifier having the highest confidence score or value. For example, the correct product identifier 706 may be associated with images 1 through 5. In some embodiments, the correct product identifier 706 to associate with the image may be confirmed by a user. Alternatively or in addition to, the control circuit 310 and/or the trained machine learning model 404 may select the top set of strong UPCs or product identifiers for each image. For example, the top set of strong UPCs or product identifiers for Image 1 may include UPC1, UPC2, UPC4. Alternatively or in addition to, the control circuit 310 and/or the trained machine learning model 404, at 708, may create a confusion list of UPCs based on the selected top set of strong UPCs or product identifiers for the correct product identifier 706. Alternatively or in addition to, the control circuit 310 and/or the trained machine learning model 404, at 710, may determine distances of each product identifier in the confusion list to the correct product identifier 706 based on the feature vector model described herein. For example, the value of 0.8 associated with UPC2 may be determined by the number of times the UPC2 is associated with the images depicting the correct product identifier 706 (e.g., 4 times as shown at 708) divided by the number of associated images (e.g., 5 for images 1 through 5). Alternatively or in addition to, the control circuit 310 and/or the trained machine learning model 404, at 712, may determine cluster grouping of the product identifiers in the confusion list based on whether the corresponding value shown at 710 is greater than a predetermined threshold (e.g., threshold of 4). The predetermined thereshold may be determined as previously described above. For example, Cluster 1 may include UPC1, UPC2, UPC3, UPC4 and UPC5. However, Cluster 1 may not include UPC6 and UPCx1 due to having the corresponding value being less than the predetermined threshold. Alternatively or in addition to, the control circuit 310 and/or the trained machine learning model 404 may store the cluster grouping at 712 in the database 140. Alternatively or in addition to, the control circuit 310 and/or the trained machine learning model 404 may associate in the database 140 for each cluster grouping a resolution type 714. For example, for Cluster 1, the resolution type may indicate the images of the UPCs included in the cluster are visually different from one another.

Further, the circuits, circuitry, systems, devices, processes, methods, techniques, functionality, services, servers, sources and the like described herein may be utilized, implemented and/or run on many different types of devices and/or systems. FIG. 11 illustrates an exemplary system 1100 that may be used for implementing any of the components, circuits, circuitry, systems, functionality, apparatuses, processes, or devices of the system 100 of FIG. 1, the movable image capture device 120 of FIG. 2, the computing device 150 of FIG. 3, the system 400 of FIG. 4, the method 900 of FIG. 9, the method 1000 of FIG. 10, and/or other above or below mentioned systems or devices, or parts of such circuits, circuitry, functionality, systems, apparatuses, processes, or devices. For example, the system 1100 may be used to implement some or all of the system for processing captured images of objects at a product storage facility, the user interface 350, the control circuit 310, the memory storage/s 402, the database 140, the network 130, the image capture device/s 120 and the motorized robotic unit 406, and/or other such components, circuitry, functionality and/or devices. However, the use of the system 1100 or any portion thereof is certainly not required.

By way of example, the system 1100 may comprise a processor module (or a control circuit) 1112, memory 1114, and one or more communication links, paths, buses or the like 1118. Some embodiments may include one or more user interfaces 1116, and/or one or more internal and/or external power sources or supplies 1140. The control circuit 1112 can be implemented through one or more processors, microprocessors, central processing unit, logic, local digital storage, firmware, software, and/or other control hardware and/or software, and may be used to execute or assist in executing the steps of the processes, methods, functionality and techniques described herein, and control various communications, decisions, programs, content, listings, services, interfaces, logging, reporting, etc. Further, in some embodiments, the control circuit 1112 can be part of control circuitry and/or a control system 1110, which may be implemented through one or more processors with access to one or more memory 1114 that can store instructions, code and the like that is implemented by the control circuit and/or processors to implement intended functionality. In some applications, the control circuit and/or memory may be distributed over a communications network (e.g., LAN, WAN, Internet) providing distributed and/or redundant processing and functionality. Again, the system 1100 may be used to implement one or more of the above or below, or parts of, components, circuits, systems, processes and the like. For example, the system 1100 may implement the system for processing captured images of objects at a product storage facility with the control circuit 310 being the control circuit 1112.

The user interface 1116 can allow a user to interact with the system 1100 and receive information through the system. In some instances, the user interface 1116 includes a display 1122 and/or one or more user inputs 1124, such as buttons, touch screen, track ball, keyboard, mouse, etc., which can be part of or wired or wirelessly coupled with the system 1100. Typically, the system 1100 further includes one or more communication interfaces, ports, transceivers 1120 and the like allowing the system 1100 to communicate over a communication bus, a distributed computer and/or communication network (e.g., a local area network (LAN), the Internet, wide area network (WAN), etc.), communication link 1118, other networks or communication channels with other devices and/or other such communications or combination of two or more of such communication methods. Further the transceiver 1120 can be configured for wired, wireless, optical, fiber optical cable, satellite, or other such communication configurations or combinations of two or more of such communications. Some embodiments include one or more input/output (I/O) interface 1134 that allow one or more devices to couple with the system 1100. The I/O interface can be substantially any relevant port or combinations of ports, such as but not limited to USB, Ethernet, or other such ports. The I/O interface 1134 can be configured to allow wired and/or wireless communication coupling to external components. For example, the I/O interface can provide wired communication and/or wireless communication (e.g., Wi-Fi, Bluetooth, cellular, RF, and/or other such wireless communication), and in some instances may include any known wired and/or wireless interfacing device, circuit and/or connecting device, such as but not limited to one or more transmitters, receivers, transceivers, or combination of two or more of such devices.

In some embodiments, the system may include one or more sensors 1126 to provide information to the system and/or sensor information that is communicated to another component, such as the user interface 350, the control circuit 310, the memory storage/s 402, the database 140, the network 130, the image capture device/s 120 and the motorized robotic unit 406, etc. The sensors can include substantially any relevant sensor, such as temperature sensors, distance measurement sensors (e.g., optical units, sound/ultrasound units, etc.), optical based scanning sensors to sense and read optical patterns (e.g., bar codes), radio frequency identification (RFID) tag reader sensors capable of reading RFID tags in proximity to the sensor, and other such sensors. The foregoing examples are intended to be illustrative and are not intended to convey an exhaustive listing of all possible sensors. Instead, it will be understood that these teachings will accommodate sensing any of a wide variety of circumstances in a given application setting.

The system 1100 comprises an example of a control and/or processor-based system with the control circuit 1112. Again, the control circuit 1112 can be implemented through one or more processors, controllers, central processing units, logic, software and the like. Further, in some implementations the control circuit 1112 may provide multiprocessor functionality.

The memory 1114, which can be accessed by the control circuit 1112, typically includes one or more processor readable and/or computer readable media accessed by at least the control circuit 1112, and can include volatile and/or nonvolatile media, such as RAM, ROM, EEPROM, flash memory and/or other memory technology. Further, the memory 1114 is shown as internal to the control system 1110; however, the memory 1114 can be internal, external or a combination of internal and external memory. Similarly, some or all of the memory 1114 can be internal, external or a combination of internal and external memory of the control circuit 1112. The external memory can be substantially any relevant memory such as, but not limited to, solid-state storage devices or drives, hard drive, one or more of universal serial bus (USB) stick or drive, flash memory secure digital (SD) card, other memory cards, and other such memory or combinations of two or more of such memory, and some or all of the memory may be distributed at multiple locations over the computer network. The memory 1114 can store code, software, executables, scripts, data, content, lists, programming, programs, log or history data, user information, customer information, product information, and the like. While FIG. 11 illustrates the various components being coupled together via a bus, it is understood that the various components may actually be coupled to the control circuit and/or one or more other components directly.

Those skilled in the art will recognize that a wide variety of other modifications, alterations, and combinations can also be made with respect to the above described embodiments without departing from the scope of the invention, and that such modifications, alterations, and combinations are to be viewed as being within the ambit of the inventive concept.

This application is related to the following applications, each of which is incorporated herein by reference in its entirety: entitled SYSTEMS AND METHODS OF SELECTING AN IMAGE FROM A GROUP OF IMAGES OF A RETAIL PRODUCT STORAGE AREA filed on Oct. 11, 2022, application Ser. No. 17/963,787 (attorney docket No. 8842-154648-US_7074US01); entitled SYSTEMS AND METHODS OF IDENTIFYING INDIVIDUAL RETAIL PRODUCTS IN A PRODUCT STORAGE AREA BASED ON AN IMAGE OF THE PRODUCT STORAGE AREA filed on Oct. 11, 2022, application Ser. No. 17/963,802 (attorney docket No. 8842-154649-US_7075US01); entitled CLUSTERING OF ITEMS WITH HETEROGENEOUS DATA POINTS filed on Oct. 11, 2022, application Ser. No. 17/963,903 (attorney docket No. 8842-154650-US_7084US01); entitled SYSTEMS AND METHODS OF TRANSFORMING IMAGE DATA TO PRODUCT STORAGE FACILITY LOCATION INFORMATION filed on Oct. 11, 2022, application Ser. No. 17/963,751 (attorney docket No. 8842-155168-US_7108US01); entitled SYSTEMS AND METHODS OF MAPPING AN INTERIOR SPACE OF A PRODUCT STORAGE FACILITY filed on Oct. 14, 2022, application Ser. No. 17/966,580 (attorney docket No. 8842-155167-US_7109US01); entitled SYSTEMS AND METHODS OF DETECTING PRICE TAGS AND ASSOCIATING THE PRICE TAGS WITH PRODUCTS filed on Oct. 21, 2022, application Ser. No. 17/971,350 (attorney docket No. 8842-155164-US_7076US01); entitled SYSTEMS AND METHODS OF VERIFYING PRICE TAG LABEL-PRODUCT PAIRINGS filed on Nov. 9, 2022, application Ser. No. 17/983,773 (attorney docket No. 8842-155448-US_7077US01); entitled SYSTEMS AND METHODS OF USING CACHED IMAGES TO DETERMINE PRODUCT COUNTS ON PRODUCT STORAGE STRUCTURES OF A PRODUCT STORAGE FACILITY filed Jan. 24, 2023, application Ser. No. 18/158,969 (attorney docket No. 8842-155761-US_7079US01); entitled METHODS AND SYSTEMS FOR CREATING REFERENCE IMAGE TEMPLATES FOR IDENTIFICATION OF PRODUCTS ON PRODUCT STORAGE STRUCTURES OF A RETAIL FACILITY filed Jan. 24, 2023, application Ser. No. 18/158,983 (attorney docket No. 8842-155764-US_7079US01); entitled SYSTEMS AND METHODS FOR PROCESSING IMAGES CAPTURED AT A PRODUCT STORAGE FACILITY filed Jan. 24, 2023, application Ser. No. 18/158,925 (attorney docket No. 8842-155165-US_7085US01); and entitled SYSTEMS AND METHODS FOR PROCESSING IMAGES CAPTURED AT A PRODUCT STORAGE FACILITY filed Jan. 24, 2023, application Ser. No. 18/158,950 (attorney docket No. 8842-155166-US_7087US01); entitled SYSTEMS AND METHODS FOR ANALYZING AND LABELING IMAGES IN A RETAIL FACILITY filed Jan. 30, 2023, application Ser. No. 18/161,788 (attorney docket No. 8842-155523-US_7086US01); entitled SYSTEMS AND METHODS FOR ANALYZING DEPTH IN IMAGES OBTAINED IN PRODUCT STORAGE FACILITIES TO DETECT OUTLIER ITEMS filed Feb. 6, 2023, application Ser. No. 18/165,152 (attorney docket No. 8842-155762-US_7083US01); entitled SYSTEMS AND METHODS FOR IDENTIFYING DIFFERENT PRODUCT IDENTIFIERS THAT CORRESPOND TO THE SAME PRODUCT filed Feb. 13, 2023, application Ser. No. ______ (attorney docket No. 8842-156079-US_7090US01); entitled SYSTEMS AND METHODS OF UPDATING MODEL TEMPLATES ASSOCIATED WITH IMAGES OF RETAIL PRODUCTS AT PRODUCT STORAGE FACILITIES filed Jan. 30, 2023, application Ser. No. 18/102,999 (attorney docket No. 8842-156080-US_7092US01); entitled SYSTEMS AND METHODS FOR RECOGNIZING PRODUCT LABELS AND PRODUCTS LOCATED ON PRODUCT STORAGE STRUCTURES OF PRODUCT STORAGE FACILITIES filed Feb. 6, 2023, application Ser. No. 18/106,269 (attorney docket No. 8842-156081-US_7093US01); and entitled SYSTEMS AND METHODS FOR DETECTING SUPPORT MEMBERS OF PRODUCT STORAGE STRUCTURES AT PRODUCT STORAGE FACILITIES, filed Jan. 30, 2023, application Ser. No. 18/103,338 (attorney docket No. 8842-156082-US_7094US01).

Claims

1. A system for processing captured images of objects at a product storage facility, the system comprising: a trained machine learning model stored in a memory; anda control circuit executing the trained machine learning model to determine confusing product identifiers, wherein the confusing product identifiers correspond to objects that are at least one of textually similar and visually similar such that the objects can be potentially mis-identified with an incorrect product identifier, the control circuit configured to: receive a plurality of captured images, wherein each captured image depicts at least one object for purchase at the product storage facility;identify, for each captured image, a product identifier associated with an object in a captured image;generate, for each captured image, predicted product identifiers associated with the object in the captured image based on text identified from the object in the captured image;aggregate the predicted product identifiers associated with identical product identifiers;determine a feature of the objects associated with the aggregated predicted product identifiers that is greater than a feature threshold;determine one or more confusing product identifiers based on a determination of the aggregated predicted product identifiers being associated with the feature; andupdate a dataset with at least one of the one or more confusing product identifiers and images associated with the one or more confusing product identifiers.

2. The system of claim 1, wherein the control circuit executing the trained machine learning model to determine the confusing product identifiers is further configured to identify, based on the updated dataset, correct product identifiers to associate with at least one of textually similar and visually similar objects depicted in the captured images.

3. The system of claim 1, further comprising: one or more image capture devices configured to capture a plurality of images of objects at the product storage facility; anda database configured to store the plurality of images.

4. The system of claim 3, wherein at least one of the one or more image capture devices is coupled to a motorized robotic unit.

5. The system of claim 3, wherein the plurality of images comprise images that have not gone through object detection or object classification.

6. The system of claim 1, wherein the trained machine learning model comprises one or more machine learning models each trained to perform a corresponding operation executed by the control circuit to determine the confusing product identifiers.

7. The system of claim 6, wherein a first trained machine learning model of the one or more machine learning models is trained to perform the generation of the predicted product identifiers based on a determination of score values associated with stored product identifiers based on at least one or more steps comprising: determine text associated with stored product identifiers that matches the most relative to other text associated with the stored product identifiers with the text identified from the object in the captured image;compare whether a location associated with the text identified from the object in the captured image matches with one or more locations associated with the most matching text associated with the stored product identifiers within a threshold range; anddetermine whether one or more of the stored product identifiers and the object in the captured image are associated with a matching presence of a first text and a matching absence of a second text,wherein the predicted product identifiers comprise those stored product identifiers having corresponding score values that are greater than a score threshold.

8. The system of claim 7, further comprising a database configured to store the stored product identifiers.

9. The system of claim 6, wherein a first trained machine learning model of the one or more machine learning models is trained to perform the determination of the feature of the objects associated with the aggregated predicted product identifiers based on metric learning algorithm.

10. The system of claim 1, wherein each captured image comprises metadata information determined by the control circuit.

11. A method for processing captured images of objects at a product storage facility, the method comprising: receiving, by a control circuit executing a trained machine learning model stored in a memory to determine confusing product identifiers, a plurality of captured images, wherein each captured image depicts at least one object for purchase at the product storage facility, and wherein the confusing product identifiers correspond to objects that are at least one of textually similar and visually similar such that the objects can be potentially mis-identified with an incorrect product identifier;identifying, by the control circuit executing the trained machine learning model and for each captured image, a product identifier associated with an object in a captured image;generating, by the control circuit executing the trained machine learning model and for each captured image, predicted product identifiers associated with the object in the captured image based on text identified from the object in the captured image;aggregating, by the control circuit executing the trained machine learning model, the predicted product identifiers associated with identical product identifiers;determining, by the control circuit executing the trained machine learning model, a feature of the objects associated with the aggregated predicted product identifiers that is greater than a feature threshold;determining, by the control circuit executing the trained machine learning model, one or more confusing product identifiers based on a determination of the aggregated predicted product identifiers being associated with the feature; andupdating, by the control circuit executing the trained machine learning model, a dataset with at least one of the one or more confusing product identifiers and images associated with the one or more confusing product identifiers.

12. The method of claim 11, further comprising identifying, by the control circuit executing the trained machine learning model and based on the updated dataset, correct product identifiers to associate with at least one of textually similar and visually similar objects depicted in the captured images.

13. The method of claim 11, further comprising: capturing, by one or more image capture devices, a plurality of images of objects at the product storage facility; andstoring, by a database, the plurality of images.

14. The method of claim 13, wherein at least one of the one or more image capture devices is coupled to a motorized robotic unit.

15. The method of claim 13, wherein the plurality of images comprise images that have not gone through object detection or object classification.

16. The method of claim 11, wherein the trained machine learning model comprises one or more machine learning models each trained to perform a corresponding operation executed by the control circuit to determine the confusing product identifiers.

17. The method of claim 16, wherein the generating of the predicted product identifiers is based on determining score values associated with stored product identifiers based on at least one or more steps comprising: determining, by a first trained machine learning model of the one or more machine learning models, text associated with stored product identifiers that matches the most relative to other text associated with the stored product identifiers with the text identified from the object in the captured image;comparing, by the first trained machine learning model, whether a location associated with the text identified from the object in the captured image matches with one or more locations associated with the most matching text associated with the stored product identifiers within a threshold range; anddetermining, by the first trained machine learning model, whether one or more of the stored product identifiers and the object in the captured image are associated with a matching presence of a first text and a matching absence of a second text,wherein the predicted product identifiers comprise those stored product identifiers having corresponding score values that are greater than a score threshold.

18. The method of claim 17, further comprising storing, by a database, the stored product identifiers.

19. The method of claim 16, wherein a first trained machine learning model of the one or more machine learning models is trained to perform the determining of the feature of the objects associated with the aggregated predicted product identifiers based on metric learning algorithm.

20. The method of claim 11, wherein each captured image comprises metadata information determined by the control circuit.