The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Feb. 13, 2024

Filed:

May. 09, 2023
Applicant:

American Express Travel Related Services Company, Inc., New York, NY (US);

Inventors:

Lee Chau, New York, NY (US);

Tirthankar Choudhuri, Gurgaon, IN;

Ajay Choudhary, Gurgaon, IN;

Vikas Grover, Ambala Cantt, IN;

Mohd Arshad Naeem, Gurgaon, IN;

Subhajit Sanyal, Bangalore, IN;

Dawn Thomas, Kew Gardens, NY (US);

Amit Jagdish Agarwal, Bangalore, IN;

Pranav Mehta, Gurgaon, IN;

Kamal Gupta, Bangalore, IN;

Subhra Purkayastha, Gurgaon, IN;

Prakruthi Prabhakar, Cuddalore, IN;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06Q 30/00 (2023.01); G06Q 30/0204 (2023.01); G06Q 30/0251 (2023.01); G06Q 30/0601 (2023.01);
U.S. Cl.
CPC ...
G06Q 30/0204 (2013.01); G06Q 30/0254 (2013.01); G06Q 30/0631 (2013.01);
Abstract

The present disclosure presents systems and related methods for creating real-time predictions. One such method comprises receiving, by a computing device, a first set of data and a second set of data, wherein the first set of data comprises a plurality of items available from a first source for a first set of users and the second set of data comprises transaction purchase data for a second set of users that have reward accounts, utilizing a predictive data model that determines a propensity score for a user from only behavior data that is not attributed to the user; receiving a third set of data from a third source comprising social media channel data for a third set of users; and updating the predictive data model to determine the propensity score for the user based at least in part on the third set of data.


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