Disclosed herein are systems and methods for determining whether an unknown product matches a scanned barcode during a checkout process. An edge computing device or other computer system can receive, from an overhead camera at a checkout lane, image data of an unknown product that is placed on a flatbed scanning area, identify candidate product identifications for the unknown product based on applying a classification model and/or product identification models to the image data, and determine based on the candidate product identifications, whether the unknown product matches a product associated with a barcode that is scanned at a POS terminal in the checkout lane. The classification model can be used to determine n-dimensional space feature values for the unknown product and determine which product the unknown product likely matches. The product identification models can be used to determine whether the unknown product is one of the products that are modeled.

Described herein are systems and methods for generating security event case files with unstructured data. For example, the method can include receiving, by a computing system, unstructured data and system-based inferences from devices positioned throughout a store, and adding structure to the unstructured data and system-based inferences based on applying one or more structuring models. Adding structure can include labeling the data and system-based inferences, classifying them into security event categories, and identifying objective identifiers to identify users in the data and system-based inferences. The method also can include generating case files for each of the objective identifiers, where the case files include the associated data. The method can include determining whether the case files satisfy alerting rules. The case files can then be reported out and acted upon (e.g., based on satisfying the alerting rules) and/or stored for subsequent analysis and use.

A sales management system includes a registration device having a processor configured to perform commodity registration based on operation of a customer who purchases a commodity. The sales management system also includes a gate device having a processor configured to execute error processing in response to receiving, as an input, information indicating that payment for the commodity registered by the registration device is completed is not input when the customer passes the gate device. The processor of the registration device is also configured to receive stop information indicating that the customer stops shopping without purchasing the commodity output information for passing the gate device. The processor of the gate device is also configured to receive, as the input, the information for passing the gate device, and allow passage of the customer without executing the error processing.

There are provided systems and methods relating to checking and confirming merchandise purchased at shopping facilities. In one form, the system includes: a shopping cart containing merchandise items to be purchased; a sales transaction database; a point-of-sales system that creates transaction records identified by transaction identifiers; and a mobile device used by an employee that includes a sensor to scan a paper or digital receipt to obtain the transaction identifier and a camera to capture images of the items in the shopping cart. The system also includes a control circuit that receives the transaction identifier, accesses the database using the identifier to determine the purchased items; analyzes the images of the merchandise items in the shopping cart and creates a computer vision receipt listing detected items; compares the purchased items with the detected items; and takes an action if there is a discrepancy.

A method, computer program product, and system to perform a sale transaction are provided. The method includes identifying each item of a plurality of items, based on at least one image of the plurality of items, determining a cost for each item, optionally identifying a person based on an image of the person, adding each of the items and each of the costs to a sale transaction, and charging the person for the sale transaction.

Disclosed herein is a vehicle theft prevention device. The device can include a data store including event configuration data. The device can include one or more sensors that can sense various types of measurements proximate to a vehicle. The device can include a computing device in communication with the sensors. The computing device can read measurements from the sensors and determine that a particular event has occurred. The computing device can analyze the measurements to determine the particular event occurred based on the event configuration data. When the particular event occurs, the computing device can perform one or more remedial actions.

A customer behavioural system (100) in a store (10), the customer behavioural system (100) comprising a sensor arrangement (110) comprising one or more sensors (114a-c) and a behavioural analysis module (120). The behavioural analysis module (120) is configured to determine at least one behavioural feature (160) of a customer (20) based on sensor data from said one or more sensors (114a-c) and/or determine at least one motion event (160) of a customer (20) based on sensor data from said one or more sensors (114a-c), and determine behavioural information (122) for said customer (20) based at least on one determined behavioural feature (140) and/or at least one determined motion event (160).

A store management system includes a first determination unit, a monitoring unit, a settlement unit, a second determination unit, and a generation unit. The first determination unit determines a customer entering a store displaying and selling a commodity. The monitoring unit monitors that the customer determined by the first determination unit carries the commodity. The settlement unit settles price of the commodity monitored by the monitoring unit as being carried by the customer, according to instruction from the customer. The second determination unit determines that the customer leaves the store. The generation unit generates history data including suspicious data indicating that the customer, identified by an identification code, is associated with an unsettled commodity being carried out of the store, when a settlement of the commodity carried by the customer which is determined by the second determination unit as leaving the store has not been completed by the settlement unit.

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 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.