Abstract: A computing device retrieves a stabilized weight and a weight-based item count for one or more items to be purchased. Responsive to retrieving the stabilized weight, the computing device retrieves an image-based item count and an expected weight based on the image-based item count for the one or more items to be purchased. The computing device selects between authorizing and blocking checkout of the one or more items based on the weight-based item count, the image-based item count, the stabilized weight, and the expected weight.

Abstract: Computer vision grouping recognition is provided by receiving training images that include unpackaged items; identifying, by a computer vision model, a candidate identities for unpackaged items in a given training image; receiving, from a human user, a selected identity for the unpackaged item as feedback for the candidate identity; constructing a confusion matrix tallying matches and mismatches between candidate identities and the selected identities as analyzed across the training images for each unpackaged item; identifying at least one product category that includes at least a first unpackaged item and a second unpackaged item that the confusion matrix indicates as being misidentified for each other by the computer vision model; and reconfiguring the computer vision model to identify the product category instead of the first unpackaged item or the second unpackaged item when analyzing a given image including one or more of the first unpackaged item or the second unpackaged item.

Abstract: Systems and methods of providing navigation information associated with a retail space are provided. In one exemplary embodiment, a method comprises, a network node operationally coupled to a set of optical sensor devices positioned throughout a retail space, the network node receiving a sequence of images from each optical sensor device. The method comprises, obtaining a current position of a certain subject in the retail space based on one or more sequence of images received. The method comprises, obtaining a current position of an obstacle in the retail space based on one or more sequence of images received. The method comprises, sending to a wireless device associated with the certain subject, navigation information associated with the current positions of the certain subject and the obstacle in the retail space.

Abstract: The present disclosure provides a system and method for identifying items. The method includes scanning a first item using a first barcode scanner and a second barcode scanner and determining, based on signals from the first barcode scanner and the second barcode scanner, a first two-dimensional light grid indicating where the first item broke beams of the first barcode scanner and the second barcode scanner. The method also includes predicting, using a machine learning model and based on the first two-dimensional light grid, a first category for the first item, receiving, from a camera, an image of the first item, and comparing the image of the first item to a first set of images assigned to the first category to determine an identity of the first item.

Abstract: Computer vision grouping recognition is provided by receiving training images that include unpackaged items; identifying, by a computer vision model, a candidate identities for unpackaged items in a given training image; receiving, from a human user, a selected identity for the unpackaged item as feedback for the candidate identity; constructing a confusion matrix tallying matches and mismatches between candidate identities and the selected identities as analyzed across the training images for each unpackaged item; identifying at least one product category that includes at least a first unpackaged item and a second unpackaged item that the confusion matrix indicates as being misidentified for each other by the computer vision model; and reconfiguring the computer vision model to identify the product category instead of the first unpackaged item or the second unpackaged item when analyzing a given image including one or more of the first unpackaged item or the second unpackaged item.

Abstract: Techniques for training an item recognition machine learning (ML) model are disclosed. An image of a first item for purchase is received. The image is captured by a point of sale (POS) system. A purchaser selection of a second item for purchase is also received. The purchaser makes the selection at the POS system. It is determined that the first item for purchase matches the second item for purchase, and in response training data is generated for an image recognition ML model, based on the image and the determination that the first item for purchase matches the second item for purchase. The ML model is trained using the training data, and the trained ML model is configured to recognize items for purchase in a plurality of images captured by a plurality of POS systems.

Abstract: Computer vision grouping recognition is provided by receiving training images that include unpackaged items; identifying, by a computer vision model, a candidate identities for unpackaged items in a given training image; receiving, from a human user, a selected identity for the unpackaged item as feedback for the candidate identity; constructing a confusion matrix tallying matches and mismatches between candidate identities and the selected identities as analyzed across the training images for each unpackaged item; identifying at least one product category that includes at least a first unpackaged item and a second unpackaged item that the confusion matrix indicates as being misidentified for each other by the computer vision model; and reconfiguring the computer vision model to identify the product category instead of the first unpackaged item or the second unpackaged item when analyzing a given image including one or more of the first unpackaged item or the second unpackaged item.

Abstract: The disclosure provides a method of generating business rules for a retail system. Particularly, a computing device receives rule data from a user interface. The rule data comprises retail context information associated with a retail business operating the retail system. A business rule template is then generated based on the retail context information included in the rule data, and validated based on the retail context information and on sample business data. Validation may occur by conditionally performing one or more Point of Sale (POS) functions on the sample business data according to the retail context information. If valid, the business rule template is activated for use in the retail system.

Abstract: The disclosure provides a method of generating business rules for a retail system. Particularly, a computing device receives rule data from a user interface. The rule data comprises retail context information associated with a retail business operating the retail system. A business rule template is then generated based on the retail context information included in the rule data, and validated based on the retail context information and on sample business data. Validation may occur by conditionally performing one or more Point of Sale (POS) functions on the sample business data according to the retail context information. If valid, the business rule template is activated for use in the retail system.

Abstract: Techniques for configuration management are provided. A plurality of configurations is received, where each of the plurality of configurations corresponds to a respective service and is associated with a first plan. The plurality of configurations is stored, in association with the first plan, without implementing the plurality of configurations. Upon receiving approval of the first plan, the first plan is implemented by, for each respective configuration of the plurality of configurations: identifying a respective corresponding service for the respective configuration and modifying a configuration of the respective corresponding service based on the respective configuration. Upon completing implementation of the first plan, each of the plurality of configurations is labeled with a production identifier.

Abstract: Techniques for configuration management are provided. A plurality of configurations is received, where each of the plurality of configurations corresponds to a respective service and is associated with a first plan. The plurality of configurations is stored, in association with the first plan, without implementing the plurality of configurations. Upon receiving approval of the first plan, the first plan is implemented by, for each respective configuration of the plurality of configurations: identifying a respective corresponding service for the respective configuration and modifying a configuration of the respective corresponding service based on the respective configuration. Upon completing implementation of the first plan, each of the plurality of configurations is labeled with a production identifier.