Abstract: A system and method are provided for dynamically updating a digital menu using deep-learning-based preparation-time prediction. A first deep learning neural network generates item-level embeddings for menu items based on historical preparation time records. Actual and estimated item-level preparation time vectors are generated using cosine-similarity weighting across subsets of the historical data. A second deep learning neural network is trained using the item-level vectors, ground-truth preparation times, and normalized non-categorical metadata processed through dense vector layers and concatenation. The trained network is executed to generate predicted item-level preparation times for menu items currently available for ordering. An updated digital menu including the predicted item-level preparation times is generated and transmitted to a client device, and the digital menu is automatically updated in real time on the client device in response to changes in the predicted preparation times.

Abstract: A computer-implemented method for displaying a digital menu on a point-of-sale device, the method including: on a touchscreen display, displaying selectable icons as a single virtual page, where: the selectable icons correspond to menu tier elements in menu tiers according to a menu tier hierarchy; and the touchscreen display includes scrolling mechanisms to display one or more of the selectable icons that are not initially visible on the single virtual page; selecting one of the selectable icons in a menu tier for an order, where the menu tier is not a highest-level tier in the menu tier hierarchy; and responsive to selection of the one of the selectable icons, modifying the single virtual page by replacing one or more of the selectable icons in higher-level and lower-level menu tiers relative to the menu tier with new replacement icons that correspond to the one of the selectable icons in the menu tier hierarchy.

Abstract: A computer-implemented method for displaying a digital menu on a point-of-sale device, the method including, on a touchscreen display, displaying selectable icons as a single virtual page, where the selectable icons correspond to menu tier elements in menu tiers according to a menu tier hierarchy; via a processor, periodically analyzing historical fulfilled orders corresponding to the digital menu to determine a ranking of the menu tier elements from most ordered to least ordered; and, responsive to the ranking, rearranging order of the first selectable icons on the single virtual page so that the order of displayed first selectable icons is from most ordered to least ordered.

Abstract: A method for predicting preparation times includes: retrieving set of item-level records from a database for preparation menu items for all subscriber restaurants; training and executing a first neural network to generate embeddings for each of the menu items; for a first subset of the set, calculating actual item-level preparation time vectors; for a second subset of the historical set, generating estimated item-level preparation time vectors; retrieving a set of order-level records for preparation of orders from the database; training a second neural network to predict the order-level preparation times, wherein inputs to the second neural network comprise one or more of the item-level preparation time vectors and metadata taken from the order-level records; and following training, executing the second neural network to generate predicted order-level preparation times for current orders within a restaurant.

Abstract: A method for handoff includes: scanning an indirect token displayed on a guest device, where a guest has previously registered a payment instrument with a server, and associated the payment instrument with a plurality of indirect tokens, and the server has stored a payment token that associates the plurality of indirect tokens with the payment instrument in a database record along with a notification identifier; transmitting the ephemeral token to the server; simultaneously displaying details for a transaction, where the server has accessed the notification identifier to transmit a push notification to the guest device, and where the guest device is executing a proprietary application that allows for simultaneously displaying details for the transaction; and receiving notification from the server that the transaction is complete, where transaction completion data was entered via the guest device and transmitted to the server.

Abstract: A computer-implemented method for predicting interchange charges includes: retrieving a historical transactions set, where each completed transaction in the set includes transaction features, a bank identification number (BIN), and a corresponding true interchange code; transforming all BINs in the set into a corresponding plurality of BIN features that comprise probabilities; creating a first training set including all transaction features, all pluralities of BIN features, and all true interchange codes associated with the historical transactions set; training a random forest model using the first training set and generating a second training set including rounded BIN features, rounded transaction features, discrete ones of the transaction features, and the true interchange codes; training the random forest model using the second training set to generate a trained random forest model for prediction of the interchange codes; and executing the trained random forest model for new transactions to generate corres

Abstract: A computer-implemented method for predicting interchange charges includes: retrieving a historical transactions set, where each completed transaction in the set includes transaction features, a bank identification number (BIN), and a corresponding true interchange code; transforming all BINs in the set into a corresponding plurality of BIN features that comprise probabilities; creating a first training set including all transaction features, all pluralities of BIN features, and all true interchange codes associated with the historical transactions set; training a random forest model using the first training set and generating a second training set including rounded BIN features, rounded transaction features, discrete ones of the transaction features, and the true interchange codes; training the random forest model using the second training set to generate a trained random forest model for prediction of the interchange codes; and executing the trained random forest model for new transactions to generate corres

Abstract: An apparatus for dynamic credit card reader mode management is provided, the apparatus including: a point-of sale (POS) terminal, configured to accept employee login credentials via a magnetic stripe card, to display menu items for selection, to add selected menu items to an order, and to accept payment for the order, where the payment may be obtained via swipe, dip, or tap, the POS terminal including: a first card reader, configured to read first provided cards when enabled; a second card reader, configured to read second provided cards when enabled; and a dual reader controller, coupled to the first and second card readers, configured to determine one or more workflow states of the POS terminal, and configured to dynamically control modes of the first and second card readers according to the one or more workflow states.

Abstract: A computer-implemented method for predicting interchange charges includes: retrieving a historical transactions set, where each completed transaction in the set includes transaction features, a bank identification number (BIN), and a corresponding true interchange code; transforming all BINs in the set into a corresponding plurality of BIN features that comprise probabilities; creating a first training set including all transaction features, all pluralities of BIN features, and all true interchange codes associated with the historical transactions set; training a random forest model using the first training set and generating a second training set including rounded BIN features, rounded transaction features, discrete ones of the transaction features, and the true interchange codes; training the random forest model using the second training set to generate a trained random forest model for prediction of the interchange codes; and executing the trained random forest model for new transactions to generate corres

Abstract: A system for credit card reader mode management includes: a point-of sale (POS) terminal, that accepts login credentials via a magnetic stripe card, displays menu items, adds selected menu items to an order, and accepts payment instruments for the order, the POS terminal including: a first card reader, that reads first cards when enabled; a second card reader, that reads second cards when enabled; and a dual reader controller, that determines one or more workflow states of the POS terminal, and controls modes of the first and second card readers; and a backend server, that receives messages transmitted by the POS terminal including payment information obtained by the POS terminal, and that employs the payment information to process one of more transactions with one or more financial institutions to complete the payment for the order, and that notifies the POS terminal that the payment for the order is complete.

Abstract: A computer-implemented method for predicting interchange charges includes: retrieving a historical transactions set, where each completed transaction in the set includes transaction features, a bank identification number (BIN), and a corresponding true interchange code; transforming all BINs in the set into a corresponding plurality of BIN features that comprise probabilities; creating a first training set including all transaction features, all pluralities of BIN features, and all true interchange codes associated with the historical transactions set; training a random forest model using the first training set and generating a second training set including rounded BIN features, rounded transaction features, discrete ones of the transaction features, and the true interchange codes; training the random forest model using the second training set to generate a trained random forest model for prediction of the interchange codes; and executing the trained random forest model for new transactions to generate corres

Abstract: A method for dynamic adaptation of card reader protocol includes: providing a point-of sale (POS) terminal, that accepts login credentials via a magnetic stripe card, displays menu items for selection, adds selected menu items to an order, and accepts payment for the order, where the payment may be obtained via swipe, dip, or tap; via a card reader, reading provided cards when enabled; via a reader controller, determining one or more workflow states of the POS terminal, and controlling states and modes of the card reader; determining a type and capabilities of the card reader upon power up; and receiving instructions indicating the type and the capabilities, and communicating with the card reader according to the type and the capabilities responsive to a common set of commands employed by the POS terminal, and formatting data received from the card reader into a common data format.

Abstract: A method for handoff includes: scanning an ephemeral token displayed on a guest device, where a guest has previously registered a payment instrument with a server, and associated the payment instrument with a plurality of ephemeral tokens, and the server has stored a payment token that associates the plurality of ephemeral tokens with the payment instrument in a database record along with a notification identifier, and where the guest device has received the ephemeral token; transmitting the ephemeral token to the server; simultaneously displaying details for a transaction, where the server has accessed the notification identifier to transmit a push notification to the guest device, and where the guest device is executing a proprietary application that allows for simultaneously displaying details for the transaction; and receiving notification from the server that the transaction is complete, where transaction completion data was entered via the guest device and transmitted to the server.

Abstract: A method for transaction handoff includes: registering a payment instrument with a server, associating the payment instrument with indirect tokens, and storing a payment token that associates the indirect tokens with the payment instrument and a device identifier in a database record; via a device, receiving an indirect token from the server; via a point-of-sale (POS) terminal, entering the indirect token, and transmitting indirect token via the internet cloud to the server; via the server, using the payment token to access the device identifier and transmitting a push notification; upon acceptance of the push notification, executing an application on the device and simultaneously displaying transaction details on both the device and POS terminal; receiving completion data that is entered via the device and transmitting the completion data to the server; and completing the transaction at the server and notifying the POS terminal and the device that the transaction is complete.

Abstract: A computer-implemented method for predicting interchange charges includes: retrieving a historical transactions set, where each completed transaction in the set includes transaction features, a bank identification number (BIN), and a corresponding true interchange code; transforming all BINs in the set into a corresponding plurality of BIN features that comprise probabilities; creating a first training set including all transaction features, all pluralities of BIN features, and all true interchange codes associated with the historical transactions set; training a random forest model using the first training set and generating a second training set including rounded BIN features, rounded transaction features, discrete ones of the transaction features, and the true interchange codes; training the random forest model using the second training set to generate a trained random forest model for prediction of the interchange codes; and executing the trained random forest model for new transactions to generate corres

Abstract: An apparatus for context-based credit card reader management is provided that includes a point-of sale (POS) terminal, configured to accept employee login credentials via a magnetic stripe card, to display menu items for selection, to add selected menu items to an order, and to accept payment for the order, where the payment may be obtained via swipe, dip, or tap. The POS terminal has a card reader and a reader controller. The card reader reads provided cards when fully powered and enabled. The reader controller is coupled to the card reader, and determines one or more workflow states of the POS terminal, and dynamically controls power states of the card reader according to the one or more workflow states.

Abstract: A method for handoff of a transaction for completion includes: registering a payment instrument with a server and storing a payment token that represents the payment instrument; storing a device identifier that corresponds to the payment token in the database record; via a point-of-sale terminal, reading the payment instrument, and transmitting it to the server; via the server, using the payment token to access the device identifier and transmitting a push notification to a device corresponding to the device identifier; upon acceptance of the push notification, executing an application on the device and simultaneously displaying details for the transaction on both the device and the point-of-sale terminal; receiving transaction completion data that is entered via the device and transmitting the transaction completion data to the server; and completing the transaction at the server and notifying the point-of-sale terminal and the device that the transaction is complete.

Abstract: A system for automated origination of capital includes a rate processor, a revenue forecaster, and an offer processor. The rate processor generates prices for capital product offers to subscribers of a point-of-sale (POS) subscription service, where the prices are generated by employing probability of default (PD) values that are derived from historical POS data corresponding to each of the subscribers. The revenue forecaster is coupled to the rate processor and is configured to employ the configured to employ the historical POS data and locations of establishments corresponding to the each of said subscribers to predict future POS data for the establishments corresponding to the each of the subscribers, and to employ the future POS data to generate predicted total revenues corresponding to the each of the subscribers over a payback period.