A machine-learning algorithm is trained with features relevant to basket data for items of transactions. The trained algorithm is trained to predict whether a given transaction is more or less likely to be associated with theft being engaged in by a transaction operator for the transaction. The trained algorithm is then provided basket data for a given transaction and produces as output a theft prediction value. When the theft prediction value exceeds a configured threshold value, the transaction is flagged for manual intervention or the transaction is flagged for subsequent manual verification.

An inventory tracking and management system includes storage devices comprising carts, cabinets, or shelves, sensors and/or monitoring devices associated with the storage devices, a central database connecting the storage devices, sensors, and monitoring devices within a hospital, and a processing server associated with the central database. The processing server including a software system controlling operation of the inventory tracking and management system.

An information processing method according to an aspect of the present disclosure includes: acquiring, from a video, flow line information of a customer; detecting that the customer acquires an item; and storing, in a storage, flow line information of the customer and information on a number of items acquired by the customer, in association with each other.

An information processing device acquires, from an accounting machine, product information generated when the accounting machine reads a code on a product identifies a first feature amount related to first number of times indicating number of products purchased, based on the acquired product information acquires an image obtained by capturing an image of an object disposed in a certain area adjacent to the accounting machine and containing the product identifies a second feature amount related to second number of times indicating number of times of taking out operations of a product placed in the object and notifies with an alert based on the first feature amount and the second feature amount.

Described herein are systems and techniques for implementing a third-party item tracking and payment system that enables a user to walk out without a manual checkout process. The techniques include receiving user identifying information associated with a payment account and conveying an identifier of the account to a management system of the retailer. Items, as selected by the user, are added to a virtual cart that is checked out automatically when the user exits the store and the third party system communicates the cart contents to the retailer for cost calculation and then instructs payment using the stored payment information without disrupting existing inventory and account management systems.

A method includes receiving a first image of an item captured by a first camera when the item is purchased and linking the first image to a transaction component corresponding to the purchase of the item. The method also includes determining that the first image is linked to the transaction component and, in response to a request from a user device to display the item and in response to determining that the first image is linked to the transaction component, communicating the first image to the user device.

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.

A point of sale system includes a payment module having a plurality of payment modes, and a discount module configured to determine a discount associated with each of the plurality of payment modes. The point of sale system includes a processor configured to receive a request for processing the payment for the purchase order, and generate at least one first invoice receipt for the purchase order. The at least one first invoice receipt includes an indication of the discount available corresponding to each of the plurality of payment modes. The processor is also configured to process the payment of the purchase order using a desired payment mode of a customer and generate at least one final invoice receipt indicating the desired payment mode and the discount associated with the desired payment mode.

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.

A mobile smart container system comprises a housing, an access component configured to secure access to a compartment within the housing when in a closed position, a communication interface configured to wirelessly receive a request to access the compartment, a perceivable output device, an electromechanical latch configured to engage with the access component to releasably lock the access component in the closed position, and a processor. The processor receives and authenticates the request to access the compartment and, in response to receiving and authenticating the request, activates the electromechanical latch to unlock the access component to make the compartment accessible, and outputs, upon actuation of the electromechanical latch, an alert via the perceivable output device to identify the smart container system.