US 12,190,370 B2
Systems and methods for dynamic post-transaction orders and redemption
Gabriel Saunkeah, Berkeley, CA (US); Roy Erez, Palo Alto, CA (US); Alex Sirota, Los Altos, CA (US); Shane Parrish, Menlo Park, CA (US); and David Warmerdam, Stamford, CT (US)
Assigned to LOOP COMMERCE, INC., Stamford, CT (US)
Filed by Loop Commerce, Inc., Stamford, CT (US)
Filed on Feb. 14, 2023, as Appl. No. 18/168,804.
Claims priority of provisional application 63/310,417, filed on Feb. 15, 2022.
Prior Publication US 2023/0260009 A1, Aug. 17, 2023
Int. Cl. G06Q 30/0601 (2023.01)
CPC G06Q 30/0637 (2013.01) 28 Claims
OG exemplary drawing
 
1. A computer-implemented method, comprising:
identifying, by a gifting service system including one or more processors, order confirmation details for a completed transaction;
performing an analysis using the order confirmation details and sender information;
selecting one or more sender gift options that include one or more partner products associated with a retailer system, wherein the selection is performed by a send offer analysis algorithm of a machine learning system of the gifting service system, and wherein the send offer analysis algorithm uses order details to dynamically predict gift options likely to result in a gift action;
receiving send information, wherein the send information includes a gift selection and a recipient identifier;
transmitting a gift notification based on the recipient identifier, wherein the gift notification includes one or more recipient gift options;
receiving one or more recipient gift selections associated with the one or more recipient gift options;
performing, by the gifting service system, the gift action based on the one or more recipient gift selections;
updating the send offer analysis algorithm using the send information and the gift selections gift selection as training data for updating the send offer analysis algorithm; and
dynamically updating the send offer analysis algorithm wherein dynamically updating includes continuously training the send offer analysis algorithm over time to improve predictive accuracy using confirmation details, product data, or sender data, wherein the sender data includes data associated with purchases for which an offer to send a gift was provided to a consumer.