A non-transitory computer-readable recording medium stores therein an action determination program that causes a computer to execute a process including acquiring history information of a user operation on a checkout machine to which a product to be purchased is registered and that performs checkout processing of the registered product, specifying, based on an image obtained by capturing a user in front of the checkout machine, an action in which the user operates the checkout machine while grasping an object, and determining, based on the acquired history information, whether the action in which the user operates the checkout machine while grasping an object is an action in which a product to be purchased is registered to the checkout machine.

A system determines an electronic product code (EPC) and a checksum read from a radio-frequency identification (RFID) tag. The system calculates a new checksum based on the EPC and a retailer-specific number associated with a retailer that owns one or more RFID tags. The system compares the new checksum with the checksum read from the RFID tag, and determines whether the RFID tag is owned by the retailer based on whether the new checksum matches the checksum read from the RFID tag.

According to at least one embodiment, a reading device includes a first device and a second device. The first device includes an imaging unit, a first recognition unit configured to recognize a commodity from a captured image of the imaging unit, and a voice output unit configured to emit a voice if the first recognition unit recognizes the commodity. The second device is separate from the first device, and includes a second recognition unit configured to recognize a commodity from the captured image by a method different from that of the first recognition unit and a second voice generation unit configured to output voice data for sounding the voice output unit if the second recognition unit recognizes the commodity. The first device includes a first voice generation unit configured to output voice data for sounding the voice output unit if the first recognition unit recognizes the commodity, an input unit configured to take in voice data output by the second voice generation unit into the first device, and a voice mixer configured to input the voice data taken in by the input unit and the voice data output by the first voice generation unit to the voice output unit.

A server device (a commodity storage support device) includes a commodity-information acquiring unit configured to acquire information relating to storage of registered commodities, a storage-body-information acquiring unit configured to acquire information relating to a storage body for storing commodities purchased by a customer, a storage-method calculating unit configured to calculate a storage method in storing the commodities in the storage body, and a storage-method output unit (an information presenting unit) configured to output, in a displayable manner, the storage method to a portable terminal carried by the customer.

An electronic medication storage cabinet includes a plurality of drawers configured with a plurality of sensors arranged to identify a positioning of storage pockets within the drawers. Upon receiving an indication of a new medicine container to be loaded in the cabinet, a sequence of steps to load the medicine container into the cabinet is generated based on a mapping algorithm. A first step is displayed on a display the cabinet, and a determination is made as to whether the first step is associated with one of the plurality of drawers. In response to determining that the first step is associated with one of the plurality of drawers, the drawer is automatically unlocked. In response to determining that the first step is successfully completed, a determination of whether execution of any additional steps is pending and indicated on the display.

In general, in one aspect, one or more embodiments relate to a method including receiving, in a business rules engine, input data from disparate data sources. The input data describes a merchant and an application by the merchant to use an electronic payments system for processing transactions between the merchant and customers. Featurization is performed on the input data to form a machine readable vector. By applying the machine readable vector as input to a machine learning model in a machine learning layer, a risk score is predicted. The machine learning model is trained using training data describing use of the electronic payments system by other merchants. The risk score is an estimated probability of the merchant being unable to satisfy an obligation of using the electronic payments system. A business rules engine, based on the risk score, limits use of the electronic payments system by the merchant.

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.

The disclosure includes a fixed retail scanner includes a data reader. The data reader includes a main board including a system processor disposed within the data reader, and one or more camera modules disposed within the data reader and operably coupled with the system processor. Each camera module may include a local on-board imager AI engine configured to perform AI tasks according to a loaded trained AI model. A system artificial intelligence (AI) engine may be disposed within the data reader and configured to perform AI tasks according to a loaded trained AI model. The system processor is operably coupled to each of the imager AI engines and the system AI engine for scheduling and dispatching AI tasks across a distributed network of AI resources including the imager AI engines and the system AI engine.

Disclosed herein are components, systems, and methods of a scanning system that operates in a retail environment to deter and prevent theft of items. The systems and methods include a plurality of imagers to capture a plurality of images of the item, and identifying key feature locations of the item based on the captured images. The systems and methods include a scanner to read a machine-readable symbol attached to the item, and accessing a database that includes stored key feature data associated with the specific machine-readable symbol attached to the item. The systems and methods further include one or more processors that generate key feature data for the item based on the key feature locations, compares the stored key feature data with the generated key feature data, and determines whether the stored key feature data matches the generated key feature data to a degree sufficient to surpass a predetermined threshold.

This disclosure is directed to techniques in which a first user in an environment scans visual indicia associated with an item, such as a barcode, before handing the item to a second user. One or more computing devices may receive an indication of the scan, retrieve image data of the interaction from a camera within the environment, identify the user that received the item, and update a virtual cart associated with the second user to indicate addition of the item.