IP Library Granted Patent US 12,299,702
Granted Patent B2
US 12,299,702 · App. 17/849,128 · Granted May 13, 2025

Systems and methods for computer analytics of associations between online and offline purchase events

Inventors: Nicole Jass (Aurora, CO); Dennis A. Kettler (Lebanon, OH)
Assignee: Worldpay, LLC
G06Q30/0201G06Q30/0633
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Quick Facts
Patent No.
US 12,299,702
App. No.
17/849,128
Granted
May 13, 2025
Kind
B2
Abstract

Systems and methods are disclosed for generating consumer analytics for products placed in online shopping carts. A profiler computing system generates a unique tacking profile for associating purchase events by a purchaser. Payment vehicle data and a tracking element are associated with the identified purchaser profile. The purchaser profile may be generated based on purchase information associated with an initial purchase event by the purchaser. The profiler computing system determines whether products abandoned in online shopping carts are purchased at brick-and-mortar affiliates or other merchant forums. Other embodiments are described and claimed.

Claims (48)

1. A computer-implemented method of generating consumer analytics for products placed in online shopping carts using a profiler computing system, the method comprising:

receiving, by one or more processors of the profiler computing system and from a user device in electronic communication with the profiler computing system over an electronic network and via a tracking identifier associated with the user device, a first time value and a first product identifier associated with a first product being added, by a user using the user device, to an online shopping cart of a first merchant system, wherein the tracking identifier associated with the user device is one of a primary account number (PAN), a device identification (ID), or an email address;

receiving, by the one or more processors, a second time value and a second product identifier associated with a purchase transaction associated with a second product purchased by the user;

determining, by the one or more processors, that the first product identifier and the second product identifier identify matching products;

determining, by the one or more processors, a time difference between the first time value and the second time value;

updating, by the one or more processors, a unique tracking profile of the user stored by the profiler computing system to include a characterization of a purchase event based on the time difference; and

transmitting, by the one or more processors and to a receiving entity user device, the unique tracking profile.

2. The computer-implemented method of claim 1 , further comprising:

comparing, by the one or more processors, the time difference to a predetermined period of time to determine that the time difference is less than the predetermined period of time or greater than the predetermined period of time; and

determining, by the one or more processors, the characterization of the purchase event based on the comparing.

3. The computer-implemented method of claim 1 , wherein determining that the first product identifier and the second product identifier identify matching products further includes:

determining, by the one or more processors, a first matching characteristic of the first product and a second matching characteristic of the second product, the first matching characteristic and the second matching characteristic based on one or more of: a model number, a part number, a stock keeping unit (SKU), and an international standard book number (ISBN) of the first product and the second product, respectively.

4. The computer-implemented method of claim 1 , further comprising:

receiving, by the one or more processors, an indication that the user completed the purchase transaction associated with the second product using a second merchant system; and

determining, by the one or more processors, the characterization of the purchase event based on the indication.

5. The computer-implemented method of claim 4 , wherein the second merchant system is associated with one of an online store or a brick-and-mortar store.

6. A profiler computing system for generating consumer analytics for products placed in online shopping carts, the system comprising:

a data storage device storing instructions for generating the consumer analytics for the products placed in the online shopping carts; and

one or more processors configured to execute the instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising:

receiving, by one or more processors of the profiler computing system and from a user device in electronic communication with the profiler computing system over an electronic network and via a tracking identifier associated with the user device, a first time value and a first product identifier associated with a first product being added, by a user using the user device, to an online shopping cart of a first merchant system, wherein the tracking identifier associated with the user device is one of a primary account number (PAN), a device identification (ID), or an email address;

receiving, by the one or more processors, a second time value and a second product identifier associated with a purchase transaction associated with a second product purchased by the user;

determining, by the one or more processors, that the first product identifier and the second product identifier identify matching products;

determining, by the one or more processors, a time difference between the first time value and the second time value;

updating, by the one or more processors, a unique tracking profile of the user stored by the profiler computing system to include a characterization of a purchase event based on the time difference; and

transmitting, by the one or more processors and to a receiving entity user device, the unique tracking profile.

7. The profiler computing system of claim 6 , wherein the operations further comprise:

comparing, by the one or more processors, the time difference to a predetermined period of time to determine that the time difference is less than the predetermined period of time or greater than the predetermined period of time; and

determining, by the one or more processors, the characterization of the purchase event based on the comparing.

8. The profiler computing system of claim 6 , wherein the operations further comprise:

receiving, by the one or more processors, an indication that the user completed the purchase transaction associated with the second product using a second merchant system; and

determining, by the one or more processors, the characterization of the purchase event based on the indication.

9. The profiler computing system of claim 8 , wherein the second merchant system is associated with one of an online store or a brick-and-mortar store.

10. A non-transitory computer readable medium for generating consumer analytics for products placed in online shopping carts, the non-transitory computer readable medium storing instructions that, when executed by one or more processors of a profiler computing system, cause the one or more processors to perform operations comprising:

receiving, by one or more processors of the profiler computing system and from a user device in electronic communication with the profiler computing system over an electronic network and via a tracking identifier associated with the user device, a first time value and a first product identifier associated with a first product being added, by a user using the user device, to an online shopping cart of a first merchant system, wherein the tracking identifier associated with the user device is one of a primary account number (PAN), a device identification (ID), or an email address;

receiving, by the one or more processors, a second time value and a second product identifier associated with a purchase transaction associated with a second product purchased by the user;

determining, by the one or more processors, that the first product identifier and the second product identifier identify matching products;

determining, by the one or more processors, a time difference between the first time value and the second time value;

updating, by the one or more processors, a unique tracking profile of the user stored by the profiler computing system to include a characterization of a purchase event based on the time difference; and

transmitting, by the one or more processors and to a receiving entity user device, the unique tracking profile.

11. The non-transitory computer readable medium of claim 10 , wherein the operations further comprise:

comparing, by the one or more processors, the time difference to a predetermined period of time to determine that the time difference is less than the predetermined period of time or greater than the predetermined period of time; and

determining, by the one or more processors, the characterization of the purchase event based on the comparing.

12. The non-transitory computer readable medium of claim 10 , wherein determining that the first product identifier and the second product identifier identify matching products further includes:

determining, by the one or more processors, a matching characteristic, the matching characteristic based on one or more of: a model number, a manufacturer part number, a stock keeping unit (SKU), and an international standard book number (ISBN).

13. The non-transitory computer readable medium of claim 10 , wherein the operations further comprise:

receiving, by the one or more processors, an indication that the user completed the purchase transaction associated with the second product using a second merchant system; and

determining, by the one or more processors, the characterization of the purchase event based on the indication.

14. The non-transitory computer readable medium of claim 13 , wherein the second merchant system is associated with one of an online store or a brick-and-mortar store.

Assignments (6)
RELEASE OF SECURITY INTERESTS RECORDED AT REEL/FRAMES 066626/0655, 066625/0426, 066625/0347, AND 066625/0276 Recorded Jan 12, 2026
From: WILMINGTON TRUST, NATIONAL ASSOCIATION, AS NOTES COLLATERAL AGENT
To: WORLDPAY, LLC; WORLDPAY ISO AND ECOMMERCE, LLC; PAYMETRIC, LLC; WORLDPAY US, LLC
Reel/Frame 074314/0622 →
RELEASE OF SECURITY INTEREST IN INTELLECTUAL PROPERTY RECORDED AT R/F 066624/0719 Recorded Jan 12, 2026
From: JPMORGAN CHASE BANK, N.A., AS COLLATERAL AGENT
To: WORLDPAY, LLC
Reel/Frame 074315/0412 →
SECURITY INTEREST Recorded Feb 19, 2024
From: WORLDPAY, LLC
To: JPMORGAN CHASE BANK, N.A., AS COLLATERAL AGENT
Reel/Frame 066624/0719 →
SECURITY INTEREST Recorded Feb 19, 2024
From: WORLDPAY, LLC
To: WILMINGTON TRUST, NATIONAL ASSOCIATION, AS NOTES COLLATERAL AGENT
Reel/Frame 066626/0655 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jul 12, 2022
From: VANTIV, LLC
To: WORLDPAY, LLC
Reel/Frame 060796/0323 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jul 8, 2022
From: JASS, NICOLE; KETTLER, DENNIS
To: VANTIV, LLC
Reel/Frame 060464/0829 →
Continuity (4)
Continuation 17104480 · Nov 25, 2020
Continuation 16805048 · Feb 28, 2020
Continuation 15367992 · Dec 2, 2016
Related Publication 20220327559A1 · Oct 13, 2022
References Cited (25)
US 9569760B2 · Deutscher · 2017 [cited by examiner]
US 10977701B2 · Crutchfield, Jr. · 2021 [cited by examiner]
US 20060265406A1 · Chkodrov · 2006 [cited by examiner]
US 20120166268A1 · Griffiths · 2012 [cited by examiner]
US 20130173426A1 · Deutscher · 2013 [cited by applicant]
US 20130205220A1 · Yerli · 2013 [cited by examiner]
US 20130332273A1 · Gu · 2013 [cited by examiner]
US 20140365336A1 · Hurewitz · 2014 [cited by examiner]
US 20150112826A1 · Crutchfield, Jr. · 2015 [cited by applicant]
US 20150112836A1 · Godsey · 2015 [cited by examiner]
US 20150154674A1 · Todasco · 2015 [cited by examiner]
US 20150154675A1 · Todasco · 2015 [cited by examiner]
US 20150278888A1 · Lu · 2015 [cited by applicant]
US 20160005038A1 · Kamal · 2016 [cited by examiner]
US 20160140645A1 · Whang · 2016 [cited by examiner]
EP 3164841A4 · 2017 [cited by examiner]
WO WO2014210227A1 · 2014 [cited by examiner]
WO WO2017201231A1 · 2017 [cited by examiner]
Tewari, Anand Shanker, Abhay Kumar, and Asim Gopal Barman. “Book recommendation system based on combine features of content based filtering, collaborative filtering and association rule mining.” 2014 IEEE International … [cited by examiner]
Jai, Tun-Min Catherine, Leslie Davis Burns, and Nancy J. King. “The effect of behavioral tracking practices on consumers' shopping evaluations and repurchase intention toward trusted online retailers.” Computers in Huma… [cited by examiner]
Pauwels, Koen, et al. “Does online information drive offline revenues?: Only for specific products and consumer segments!.” Journal of retailing 87.1 (2011): 1-17. (Year: 2011). [cited by examiner]
Shangguan, Longfei, et al. “ShopMiner: Mining customer shopping behavior in physical clothing stores with COTS RFID devices.” Proceedings of the 13th ACM conference on embedded networked sensor systems. 2015. (Year: 201… [cited by examiner]
Marquardt, Philip, David Dagon, and Patrick Traynor. “Impeding individual user profiling in shopper loyalty programs.” Financial Cryptography and Data Security: 15th International Conference, FC 2011, Gros Islet, St. Lu… [cited by examiner]
Xu, Yin, et al., “Factors Influencing Cart Abandonment in the Online Shopping Process.” Social Behavior and Personality: An International Journal, 43.10 (2015): 1617-1627, Year: 2015. [cited by applicant]
Kukar-Kinney, et al., “The Determinants of Consumers' Online Shopping Cart Abandonment.” Jouarnal of the Academy of Marketing Science 38.2 (2010): 240-250, Year: 2010. [cited by applicant]