Abstract: Devices, systems, and methods for weight-based tracking and pricing of consumer goods. The devices, systems, and methods may include detecting, by a scale device, a first change in weight of a display area; capturing, by a first imaging camera, a first image featuring the display area; detecting, by one or more processors, a first object in the first image; identifying, by the one or more processors, the first object; associating, by the one or more processors, the detected first change in weight with the identified first object to determine a transaction value associated with the identified first object based upon the detected first change in weight; and displaying, by a display device, a rendition of the display area, the rendition containing a representation of the first object and the transaction value.

Abstract: Systems and methods for method for predicting a required number of point of sale (POS) stations to accommodate a number of customers are disclosed herein. An example method includes analyzing image data to determine (i) a set of customer data and (ii) a cart occupancy value associated with the customers, and generating, utilizing a machine learning (ML) algorithm, a first value based on the set of customer data and the cart occupancy value associated with the customers. The ML algorithm may be trained using a plurality of training data including a plurality of training customer data and a plurality of training cart occupancy values. The example method further includes determining whether the first value exceeds a second value, and responsive to determining that the first value exceeds the second value, generating an alert for transmission to a device indicating that the first value exceeds the second value.

Abstract: Devices, systems, and methods for weight-based tracking of prepared foods for affecting checkout. The devices, systems, and methods may include detecting, by one or more scale devices, a change in weight of a respective prepared food item stored at each of the one or more scale devices; controlling, by the respective scale device, a first transceiver of the respective scale device to transmit prepared food transaction data to a second transceiver of a user tray; storing, by the user tray, the transmitted prepared food transaction data in a memory slot at the second transceiver of the user tray; repeating the foregoing at each of the one or more scale devices at which a change in weight of a respective prepared food item is detected; and completing, by a transaction processing station, a checkout of the user tray.

Abstract: Disclosed is a robot management system comprising a control circuit configured to receive data indicative of a worker task being completed by a human from a human task management system coupled to the robot management system, generate a new robot task based on the data, add the new robot task to a robot task queue, detect an available robot, determine capabilities of the available robot, and filter the robot task queue based on the capabilities of the available robot to generate a group of robot tasks, the group being a subset of the robot task queue and each task in the group can be performed by the available robot. The control circuit is further configured to determine a priority group comprising a priority for the available robot for each task in the group and assign a robot task from the group to the available robot based on the priority group.

Abstract: Systems and methods for enhancing decoding of indicia are disclosed herein. An example implementation includes: generating a visual embedding from image data; identifying, in the image data, a partial indicia and decoding the partial indicia to determine at least a partial payload; responsive to the at least partial payload not being a full payload, determining one or more candidate visual embeddings corresponding to the partial payload; comparing the one or more candidate visual embeddings to the visual embedding generated from the image data and assigning a correspondence score for each of the one or more candidate visual embeddings based on the comparison; and determining a recommendation based on the correspondence score for each of the one or more candidate visual embeddings.

Abstract: Techniques for three-dimensional product reconstruction using multiple images collected at checkout lanes are disclosed herein. An example method includes capturing, by a barcode reader associated with a point of sale (POS) workstation, first image data associated with each of a plurality of products passing through a product scanning region of the POS workstation; analyzing barcode data from the first image data captured by the barcode reader to identify each product of the plurality of products passing through the product scanning region of the POS workstation; capturing, by one or more color cameras associated with the POS workstation, second image data associated with the identified product, of the plurality of products passing through the product scanning region of the POS workstation; and generating, by a processor, a textured three-dimensional mesh reconstruction for the identified product based on the second image data associated with the identified product.

Abstract: Techniques for three-dimensional product reconstruction using multiple images collected at checkout lanes are disclosed herein. An example method includes capturing, by a barcode reader associated with a point of sale (POS) workstation, first image data associated with each of a plurality of products passing through a product scanning region of the POS workstation; analyzing barcode data from the first image data captured by the barcode reader to identify each product of the plurality of products passing through the product scanning region of the POS workstation; capturing, by one or more color cameras associated with the POS workstation, second image data associated with the identified product, of the plurality of products passing through the product scanning region of the POS workstation; and generating, by a processor, a textured three-dimensional mesh reconstruction for the identified product based on the second image data associated with the identified product.

Abstract: In some implementations, a system may receive, from a barcode reader, read data associated with the barcode reader reading a barcode associated with a product. The system may receive, from an imager, image data associated with an image that depicts packaging of the product. The system may decode the read data to obtain product information that is associated with the product. The system may generate a product entry that associates the product information with the image data. The system may perform an action associated with the product entry to enable an image processing model to identify the product information based on the packaging depicted in the image.

Abstract: An object analysis system is disclosed herein. The object analysis system may receive an input image that depicts an object. The object analysis system may determine, using a feature extraction model and from the input image, a first feature output that is associated with one or more features of the object. The feature extraction model may be trained based on reference images that depict reference objects that are a type of the object. The object analysis system may determine, using a classification model, that an anomaly status of the object is indicative of the object including an anomaly. The classification model may be trained based on the reference images. The object analysis system may determine, using an anomaly localization model, a location of the anomaly in the input image based on a second feature output of the convolutional neural network encoder. The anomaly localization model may be trained based on the reference images.