US 12,217,280 B2
Computer system and method for offering coupons
Balarajan Balasubramaniam, Toronto (CA); and Mohammadrasool Raeesi Nafchi, Richmond Hill (CA)
Assigned to Medixin Inc., Toronto (CA)
Filed by Medixin Inc., Toronto (CA)
Filed on Jan. 8, 2024, as Appl. No. 18/406,896.
Application 18/406,896 is a division of application No. 17/038,107, filed on Sep. 30, 2020, granted, now 11,869,032.
Claims priority of provisional application 62/908,656, filed on Oct. 1, 2019.
Prior Publication US 2024/0144314 A1, May 2, 2024
Int. Cl. G06Q 30/02 (2023.01); G06K 7/14 (2006.01); G06N 3/08 (2023.01); G06Q 30/0207 (2023.01); G06Q 30/0234 (2023.01); G06Q 30/0238 (2023.01)
CPC G06Q 30/0238 (2013.01) [G06K 7/1417 (2013.01); G06N 3/08 (2013.01); G06Q 30/0224 (2013.01); G06Q 30/0234 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A computer system for offering coupons to a user, the computer system comprising:
a data storage device for storing pre-purchase image data including at least one pre-purchase item and post-purchase image data including at least one post-purchase item, wherein the pre-purchase image data and the post-purchase image data are from the same purchase transaction;
at least one processor in communication with the data storage device, the at least one processor configured to execute:
a first item detection module configured to process the pre-purchase image data to obtain first detected item data;
a second item detection module configured to process the post-purchase image data to obtain second detected item data;
wherein at least one of the first item detection module and the second item detection module uses a machine learning model to generate the first detected item data or the second detected item data, respectively;
wherein at least one of the first detected item data and the second detected item data is used by a coupon matcher engine to retrieve a matched coupon from a coupon database;
a training module configured to:
compare the first detected item data to the second detected item data to obtain comparison data indicating one or more items that were not correctly detected by the machine learning model when generating the first detected item data or the second detected item data;
retrain the machine learning model based on the comparison data to obtain a retrained machine learning model; and
deploy the retrained machine learning model to the first item detection module or the second item detection module for future processing of the pre-purchase image data or the post-purchase image data.