Patent ID: 11875335
Assignee: CAPITAL ONE SERVICES, LLC
Field: IT methods for management (Electrical engineering)
Classification: CPC G | IPC G

Claim 0:
1. A computer-implemented method comprising:
receiving, by a computing device, via an electronic payment network and from a plurality of users, transaction data associated with a plurality of merchants;
generating a histogram of payments associated with a merchant category based on the transaction data;
filtering out, based on an analysis of the histogram of payments, transaction data having purchase amounts above a predetermined threshold;
determining, by the computing device and based on the filtered transaction data, a first average purchase amount associated with merchants in the merchant category;
determining, by the computing device and based on the first average purchase amount in the filtered transaction, a second average purchase amount associated with each merchant in the merchant category;
training, using first training data comprising a transaction amount and a transaction location associated with one or more merchants in the merchant category, a first machine learning model to determine transaction patterns of the one or more merchants in the merchant category;
providing, as input to the first machine learning model, filtered transaction data associated with a first merchant from the plurality of users;
receiving, as output from the first machine learning model, a spending pattern associated with the first merchant;
providing, as input to a second machine learning model, the spending pattern associated with the first merchant and the second average purchase amount, wherein the second machine learning model is trained based on second training data comprising:
prelabeled transaction data from training merchants having a similar size as the first merchant;
prelabeled transaction data from training merchants located within a geographic location as the first merchant;
prelabeled transaction data from training merchants in a same merchant category as the first merchant; and
training labels indicating whether the training merchants enforce card enforce a card-based minimum purchase amount;

determining, based on output from the second machine learning model, that the first merchant in the merchant category enforces one or more card-based transaction rules comprising a minimum purchase amount on card-based transactions;
determining, by the computing device, that a user device, associated with a first user of the plurality of users, has connected to a wireless network associated with the first merchant for a predetermined period of time;
sending, by the computing device and to the user device associated with the first user, a notification indicating that the first merchant enforces the one or more card-based transaction rules comprising the minimum purchase amount on card-based transactions; and
causing, by the computing device, the user device to display the first merchant on a user interface with a flag indicating that the first merchant enforces the one or more card-based transaction rules comprising the minimum purchase amount on card-based transactions;
receiving, by the computing device and from the user interface on the user device, an indication for merchants that do not enforce the one or more card-based transaction rules comprising the minimum purchase amount on card-based transactions;
determining, based on the output of the second machine learning model, one or more merchants that do not enforce the one or more card-based transaction rules, wherein the second machine learning model is further trained based on prelabeled transaction data from training merchants that do not enforce the one or more card-based transaction rules; and
causing, by the computing device and on the user interface, the user device to display the one or more merchants that do not enforce the one or more card-based transaction rules.