An authentication system is provided for authenticating users in accordance with an encryption/decryption algorithm using first and second separately unique encryption keys that are time variable and are uniquely associated with each user, having a first user controlled computing device under the control of the user for generating said first encryption key using an encryption key generating algorithm. The first user controlled computing device includes a key transmitter for transmitting wirelessly within the immediate vicinity of the user the first encryption key, a second user controlled computing device, operating as a coordinating device under the control of the user, for generating the second encryption key using the encryption key generating algorithm. The second user controlled computing device includes a key receiver for receiving the first encryption key.

Affect and value relations according to logic and reason.

A clerking assembly for performing auction participant registration, tracking of items won, and payment therefor includes a housing, which has a microprocessor, a data storage module, and a transceiver engaged thereto and positioned therein. A card reader, a display, and a scanner are engaged to the housing and are operationally engaged to the microprocessor, along with the transceiver and the data storage module, which comprises a database. The card reader establishes an identity of a user for registration as an auction participant. The microprocessor generates a bidder number and a quick response code unique to the auction participant so that items won by the auction participant are associated thereto. The transceiver receives notification of the items won to allow updating of the database. The scanner scans the quick response code and the display presents the items associated thereto and a total cost thereof. Point of sale code enables payment processing.

A system and method for produce and bulk good monitoring that includes: at a computer vision monitoring system, collecting image data; detecting selection of a set of items by a first user through computer vision, the items having been displayed in an environment; applying computer vision to the image data and detecting an item identity of the set of items; collecting weight information from a scale when the item is present at the scale; and generating an item price factoring in weight information and the item identity.

A point of sale (POS) is programmed to add unrestricted items to a transaction. In response to presentation of unique customer identification, the customer's identity is verified by transmitting an image associated with the unique identification to the POS. In response to receiving verification that the image corresponds to the presenter of the unique identification, a self-checkout interface is displayed by the POS. Restricted items are input by the customer and, if the customer is determined eligible to purchase the restricted items, they are added to the transaction. Payment for both restricted and unrestricted items in the transaction is then processed. Eligibility may be determined on a server system that determines the customer's age and possibly one or more other eligibility criteria.

A device may receive receipt information associated with items in an order. The receipt information may include item identifiers and price descriptors associated with the items in the order. The device may receive product information associated with the items in the order. The product information may include product identifiers based on images of the items in the order and object descriptors. The device may map a first item identifier for a first item in the order and a first product identifier for determining that the first item corresponds to a first price descriptor and a first object descriptor. The device may map a second item identifier for a second item in the order and a second product identifier for determining that the second item corresponds to a second price descriptor and a second object descriptor. The device may assign the first and second items to respective first and second sub-receipts.

An exemplary system, method, and computer-accessible medium for determining product abandonment for an item(s) can include receiving first information related to a plurality of in-person transactions for a plurality of customers from a point of sale (POS) device(s) located at a merchant(s), where the first information can include transaction information related to the item(s), determining second information related to a reduction of a quantity of the item(s) from the first information by an operator(s) of the POS device(s), generating third information by identifying particular transactions that include the reduction of the quantity, and determining the product abandonment based on the third information by applying (i) a pattern recognition procedure or (ii) a machine learning procedure to the third information. The first information can include in-person transactions that occurred over a particular period of time, and the particular period of time can correspond to a sales cycle of the merchant(s).

A method of dynamically displaying visual identifiers. The method includes receiving transaction data associated with a transaction between a registered customer and a registered merchant; based on a customer identifying data in the transaction data, determine a heart group associated with a customer profile of the registered customer; update a heart group score for a merchant profile of the registered merchant based on the received transaction data; and controlling display of a visual identifier associated with the heart group at a display associated with the merchant based on the heart group score.

The preference based data collection and discounting system and methods herein collect consumer product preferences and track the buying habits of a consumer and reward consumers through discounts. The buying habits may be interrogated by a database of one or more algorithms, which will result in a collated database about consumer buying habits. This metadata may be used by vendors to make marketing assumptions that will influence a consumer's future purchase decisions. Consumers directly input and update their preferences as well.

This application relates to apparatus and methods for identifying anomalies within data, such as pricing data. In some examples, a computing device receives data updates and selects a machine learning model to apply to the data update. The computing device may train the machine learning model with features generated based on historical purchase order data. An anomaly score is generated based on application of the machine learning model. Based on the anomaly score, the data update is either allowed, or denied. In some examples, the computing device re-trains the machine learning model with detected anomalies. In some embodiments, the computing device prioritizes detected anomalies for further investigation. In some embodiments, the computing device identifies the cause of the anomalies by identifying at least one feature that is causing the anomaly.