Capital One Services, LLC

Ashish Bansal

Jonathan Stahlman

Pavel Fort

Philip John Spiegel

Chang W Kim

John E Schlerf

Kai-Ting Neo

A device may receive a merchant query including first merchant data associated with a first merchant. The first merchant data may be provided, as input, to a merchant matching model associated with a merchant data structure, the merchant matching model having been trained to determine a measure of confidence that input merchant data corresponds to an existing merchant in the merchant data structure. The device may receive, as output from the merchant matching model, a measure of confidence that the first merchant data corresponds to a second merchant, the second merchant being associated with second merchant data stored in the merchant data structure. The device may also determine, based on the measure of confidence, that the first merchant corresponds to the second merchant. Based on the determination, the device may obtain the second merchant data from the merchant data structure and perform an action based on the second merchant data.

Identifying merchant data associated with multiple data structures

An artificial intelligence system for communicating predicted hours of operation to a client device. The system may include a processor in communication with a client device and a database; and a storage medium storing instructions. When executed, the instructions in the storage medium configure the processor to: receive, from the client device, a request for hours of operation of a merchant, the request specifying a day of the week; obtain, from the database in response to the request, a set of credit card authorizations associated with the merchant; determine a selected day authorizations subset by selecting, from the set of credit card authorizations, credit card authorizations issued on the specified day of the week; generate a posted transaction array based on the selected day authorizations subset, the posted transaction array may include a plurality of time intervals and numbers of transactions for the time intervals; generate a predictions list based on the posted transaction array, the predictions list including the time intervals and prediction indications for the time intervals; and communicate the predictions list to the client device.

Machine learning artificial intelligence system for predicting hours of operation

A system for generating a graphical user interface in a client device. The system may include a processor in communication with the client device and a database. The processor may execute: receiving a request for occupancy information of a specified merchant; obtaining a plurality of credit card authorizations associated with the merchant; generating a posted transaction array based on the credit card authorizations; removing outlier members of the posted transaction array by applying a threshold filter; generating a transaction frequency array based on the posted transaction array, the transaction frequency array comprising weekdays and aggregated transactions associated with the weekdays; modifying the transaction frequency array by applying a transformation to the aggregated transactions; generating a smoothed array by applying a kernel density estimate to the transaction frequency array; and generating a graphical user interface displaying information in the smoothed array.

Machine learning artificial intelligence system for predicting popular hours

An artificial intelligence system for predicting hours of operation for merchants. The system may include a processor, a database, and a storage medium storing instructions. When executed, the instructions configure the processor to: receive a request for hours of operation for the merchants; obtain, from the database, a set of credit card authorizations associated with the merchants; generate input model features based on the set of credit card authorizations; obtain ground-truth data associated with the merchants; generate a training dataset based on the ground truth data and the set of credit card authorizations; determine an hours-of-operation model based on the training data set; calculate the total prediction of hours of operation using the hours-of-operation model.

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San Ramon, CA, United States of America

Ashish Bansal