Techniques for configuring a point of sale (POS) system may include using an optical sensing module to read an identifier associated with a POS component and associating the POS component with a zone that is monitored by the optical sensing module, where the zone includes the POS system. In response to optically sensing the identifier, the system may automatically configure the component to function within the POS system.

A method and system for generating perceivable data representing a digital order is provided. A GUI receives a user selection of a particular button of a plurality of buttons. Each button of the plurality of buttons is for adding a respective menu or catalog item to a checkout cart. The particular button displays within its border at least one line of copy and at least one emoji, where the at least one emoji indicates the respective menu or catalog item. The at least one line of copy provides a text description of the respective menu or catalog item and is displayed in at least a minimum font point size and at least a minimum color contrast ratio relative to a background of the particular button. The GUI is then updated to indicate that the respective menu or catalog item for the particular button is added to the checkout cart.

A method of network configuration of a personal identification number (PIN) pads includes receiving a request to upgrade a PIN pad from a merchant or an individual software vendor (ISV) to read microprocessor-enabled or contactless payment vehicles, retrieving a PIN pad configuration hash value from the PIN pad, obtaining current configuration using the hash value, sending instructions to the PIN pad to upgrade the PIN pad over a network if the PIN pad is capable of reading microprocessor-enabled or contactless payment vehicles, and downloading and installing drivers to enable PIN pad readers to read and process at least one of the microprocessor-enabled payment vehicles and contactless payment vehicles according to the instructions. The method further performs additional operations such as updating the PIN pad registry with the PIN pad configuration and enabling the PIN pad to read the microprocessor-enabled or contactless payment vehicles.

A system for monitoring financial threats includes: at least one central processing unit and system memory that causes the system to: receive information about a threat associated with a financial services device; identify a type of the threat; identify a location associated with the threat; and notify one or more customers associated with the financial services device based upon the type or the location of the threat.

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.

Aspects described herein include a method (and associated system and computer program product) comprises receiving an amount of a transaction from a point of sale (POS) terminal, and receiving, at a payment terminal, one or more user inputs comprising payment presentation information for the transaction. The method further comprises communicating the payment presentation information to a payment processing service to complete processing of the transaction. The method further comprises determining that the payment presentation information is not received at the payment processing service, and in response, encrypting the payment presentation information and transmitting the encrypted payment presentation information from the payment terminal to an external device.

A payment terminal, including: a display, an input device configured to generate input signals indicative of a user input, a payment instrument reader configured to receive payment instrument data from a financial payment instrument, a secure processor connected to the payment instrument reader, a main processor connected to the display, the main processor separate and distinct from the secure processor, a secure input processor connected to the input device and the secure processor, wherein the secure processor is operable between an unsecured mode, wherein the secure processor sends input coordinates based on the input signals received from the secure input processor to the main processor; and a secured mode, wherein the secure input processor ceases input coordinate forwarding to the main processor.

This disclosure describes an automated process for training an ML model used by a computer vision system in a point of sale (POS) system to recognize a new item. Instead of relying on a manual process performed by a data scientist, the automated process can use images of a new (i.e., unknown) item captured at one or more POS systems to then retrain the ML model to recognize the new item. That is, the images of the item are used to retrain the ML model and to test the accuracy of the updated ML model. If the updated ML model can confidently identify the new item, the updated ML model is then used by the computer vision system to identify items at the POS system.

A cash drawer is provided. The cash drawer includes a plurality of compartments configured to hold a plurality of objects. The cash drawer further includes a weight sensor coupled to each of the plurality of compartments, the weight sensor being configured to detect a weight change within a respective compartment and produce an output indicative of the detected weight change. The cash drawer further includes a processor configured to receive the output from the weight sensor and determine an object count based on the detected weight change within the respective compartment.

A group of inventions relates to artificial neural networks and their application for computer vision, in particular for the surveillance camera data processing systems and methods to automatically detect cashier fraud by verifying images using artificial neural networks. To detect cashier fraud, a POS system includes a barcode reader, memory, an image capture device, and a data processing module. The data processing module is configured to receive the data about the scanned product from the product database and to receive the video data from the image capture device. An automatic generation of product image set for artificial neural network learning contains stages when the barcode is read by placing an item against a barcode reader by a cashier, the barcode data signal then provides the data about the scanned product from product database, when the barcode data signal gives the image of the placed product from the image capture device, the received image of the placed item is saved with the data about the scanned product in the product database, then the abovementioned stages are repeated for each item placed against the barcode reader.