IP Library Granted Patent US 12,327,257
Granted Patent B1
US 12,327,257 · App. 18/098,639 · Granted Jun 10, 2025

Fresh food return processing system and related methods

Inventors: Jacob Bowman (Greensboro, NC); Pam Forster (King of Prussia, PA); Leonel Jerez (Pfafftown, NC); Seth Maxwell (Lewisville, NC)
Assignee: INMAR CLEARING, INC.
G06Q30/016G06Q30/0609G06V10/774G06V20/68G06V30/191G06Q30/0222
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Quick Facts
Patent No.
US 12,327,257
App. No.
18/098,639
Granted
Jun 10, 2025
Kind
B1
Abstract

A fresh food return processing system may include a mobile wireless communications device associated with a user to obtain an image of a previously purchased fresh food product, and initiate a mobile return of the previously purchased fresh food product based upon the image. A server may store a product purchase history associated with the user, obtain the image from the mobile wireless communications device, and provide the image to a machine learning algorithm to train the machine learning algorithm to identify the previously purchased fresh food product from the image. The server may also verify a purchase by the given user based upon comparing the identification of the image via the machine learning algorithm and the product purchase history associated with the given user, and generate and communicate a user credit redeemable toward a future purchase based upon verifying the purchase by the user.

Claims (74)

1. A fresh food return processing system comprising:

a mobile wireless communications device associated with a given user and configured to

obtain an image of a previously purchased fresh food product, and

initiate a mobile return of the previously purchased fresh food product based upon the image; and

a fresh food return processing server configured to

store a product purchase history associated with the given user,

obtain the image of the previously purchased fresh food product from the mobile wireless communications device,

provide the image of the previously purchased fresh food product to a machine learning algorithm to train the machine learning algorithm to identify the previously purchased fresh food product from the image based upon matching of pixels of the image to pixels of a baseline image, the machine learning algorithm being updated as the image is identified based upon updating of the baseline image,

verify a purchase by the given user of the previously purchased fresh food product based upon comparing the identification of the image of the previously purchased fresh food product via the machine learning algorithm and the product purchase history associated with the given user, and

generate and communicate a user credit redeemable toward a future purchase based upon verifying the purchase by the given user.

2. The fresh food return processing system of claim 1 wherein the fresh food return processing server is configured to generate and communicate a digital promotion redeemable toward a future purchase of a given product for purchase based upon verifying the purchase by the given user.

3. The fresh food return processing system of claim 1 wherein the fresh food return processing server is configured to generate a risk score associated with the given user based upon a number of previously returned products, and generate and communicate the user credit based upon the risk score.

4. The fresh food return processing system of claim 3 wherein the fresh food return processing server is configured to discontinue the mobile return based upon the risk score.

5. The fresh food return processing system of claim 4 wherein the fresh food return processing server is configured to cooperate with the mobile wireless communications device to prompt the given user to return the previously purchased fresh food product at a physical store.

6. The fresh food return processing system of claim 1 wherein the fresh food return processing server is configured to:

determine, based upon the machine learning algorithm, a determined reason for the mobile return;

cooperate with the mobile wireless communications device to prompt the given user to provide a stated reason for the mobile return; and

train the machine learning algorithm with respect to the determined reason based upon the stated reason for the mobile return.

7. The fresh food return processing system of claim 1 wherein the fresh food return processing server is configured to prompt the given user to provide a user identifier, obtain the user identifier from the mobile wireless communications device, and obtain the product purchase history based upon the user identifier.

8. The fresh food return processing system of claim 1 wherein the fresh food return processing server is configured to:

obtain a receipt image having the previously purchased fresh food product thereon;

perform an optical character recognition of the receipt image to obtain at least one purchased product description; and

verify the purchase of the previously purchased fresh food product based upon comparing the identification of the image of the previously purchased fresh food product to the at least one purchased product description.

9. A fresh food return processing server comprising:

a processor and an associated memory configured to

store a product purchase history associated with a given user;

obtain an image of a previously purchased fresh food product from a mobile wireless communications device associated with the given user upon initiation of a mobile return of the previously purchased fresh food product based upon the image;

provide the image of the previously purchased fresh food product to a machine learning algorithm to train the machine learning algorithm to identify the previously purchased fresh food product from the image based upon matching of pixels of the image to pixels of a baseline image, the machine learning algorithm being updated as the image is identified based upon updating of the baseline image;

verify a purchase by the given user of the previously purchased fresh food product based upon comparing the identification of the image of the previously purchased fresh food product via the machine learning algorithm and the product purchase history associated with the given user; and

generate and communicate a user credit redeemable toward a future purchase based upon verifying the purchase by the given user.

10. The fresh food return processing server of claim 9 wherein the processor is configured to generate and communicate a digital promotion redeemable toward a future purchase of a given product for purchase based upon verifying the purchase by the given user.

11. The fresh food return processing server of claim 9 wherein the processor is configured to generate a risk score associated with the given user based upon a number of previously returned products, and generate and communicate the user credit based upon the risk score.

12. The fresh food return processing server of claim 9 wherein the processor is configured to:

determine, based upon the machine learning algorithm, a determined reason for the mobile return;

cooperate with the mobile wireless communications device to prompt the given user to provide a stated reason for the mobile return; and

train the machine learning algorithm with respect to the determined reason based upon the stated reason for the mobile return.

13. The fresh food return processing server of claim 9 wherein the server is configured to prompt the given user to provide a user identifier, obtain the user identifier from the mobile wireless communications device, and obtain the product purchase history based upon the user identifier.

14. The fresh food return processing server of claim 9 wherein the processor is configured to:

obtain a receipt image having the previously purchased fresh food product thereon;

perform an optical character recognition of the receipt image to obtain at least one purchased product description; and

verify the purchase of the previously purchased fresh food product based upon comparing the identification of the image of the previously purchased fresh food product to the at least one purchased product description.

15. A method of processing a fresh food return comprising:

using a fresh food return processing server to

store a product purchase history associated with a given user;

obtain an image of a previously purchased fresh food product from a mobile wireless communications device associated with the given user upon initiating a mobile return of the previously purchased fresh food product based upon the image;

provide the image of the previously purchased fresh food product to a machine learning algorithm to train the machine learning algorithm to identify the previously purchased fresh food product from the image based upon matching of pixels of the image to pixels of a baseline image, the machine learning algorithm being updated as the image is identified based upon updating of the baseline image;

verify a purchase by the given user of the previously purchased fresh food product based upon comparing the identification of the image of the previously purchased fresh food product via the machine learning algorithm and the product purchase history associated with the given user; and

generate and communicate a user credit redeemable toward a future purchase based upon verifying the purchase by the given user.

16. The method of claim 15 wherein using the fresh food return processing server comprises using the fresh food return processing server to generate and communicate a digital promotion redeemable toward a future purchase of a given product for purchase based upon verifying the purchase by the given user.

17. The method of claim 15 wherein using the fresh food return processing server comprises using the fresh food return processing server to generate a risk score associated with the given user based upon a number of previously returned products, and generate and communicate the user credit based upon the risk score.

18. The method of claim 15 wherein using the fresh food return processing server comprises using the fresh food return processing server to:

determine, based upon the machine learning algorithm, a determined reason for the mobile return;

cooperate with the mobile wireless communications device to prompt the given user to provide a stated reason for the mobile return; and

train the machine learning algorithm with respect to the determined reason based upon the stated reason for the mobile return.

19. The method of claim 15 wherein using the fresh food return processing server comprises using the fresh food return processing server to:

obtain a receipt image having the previously purchased fresh food product thereon;

perform an optical character recognition of the receipt image to obtain at least one purchased product description; and

verify the purchase of the previously purchased fresh food product based upon comparing the identification of the image of the previously purchased fresh food product to the at least one purchased product description.

20. A non-transitory computer readable medium for processing a fresh food return, the non-transitory computer readable medium comprising computer executable instructions that when executed by a processor cause the processor to perform operations comprising:

storing a product purchase history associated with a given user;

obtaining an image of a previously purchased fresh food product from a mobile wireless communications device associated with the given user upon initiating a mobile return of the previously purchased fresh food product based upon the image;

providing the image of the previously purchased fresh food product to a machine learning algorithm to train the machine learning algorithm to identify the previously purchased fresh food product from the image based upon matching of pixels of the image to pixels of a baseline image, the machine learning algorithm being updated as the image is identified based upon updating of the baseline image;

verifying a purchase by the given user of the previously purchased fresh food product based upon comparing the identification of the image of the previously purchased fresh food product via the machine learning algorithm and the product purchase history associated with the given user; and

generating and communicating a user credit redeemable toward a future purchase based upon verifying the purchase by the given user.

21. The non-transitory computer readable medium of claim 20 wherein the operations comprise generating and communicating a digital promotion redeemable toward a future purchase of a given product for purchase based upon verifying the purchase by the given user.

22. The non-transitory computer readable medium of claim 20 wherein the operations comprise generating a risk score associated with the given user based upon a number of previously returned products, and generate and communicate the user credit based upon the risk score.

23. The non-transitory computer readable medium of claim 20 wherein the operations comprise:

determining, based upon the machine learning algorithm, a determined reason for the mobile return;

cooperating with the mobile wireless communications device to prompt the given user to provide a stated reason for the mobile return; and

training the machine learning algorithm with respect to the determined reason based upon the stated reason for the mobile return.

24. The non-transitory computer readable medium of claim 20 wherein the operations comprise:

obtaining a receipt image having the previously purchased fresh food product thereon;

performing an optical character recognition of the receipt image to obtain at least one purchased product description; and

verifying the purchase of the previously purchased fresh food product based upon comparing the identification of the image of the previously purchased fresh food product to the at least one purchased product description.

Assignments (2)
SECURITY INTEREST Recorded Jun 28, 2023
From: INMAR, INC.; INMAR SUPPLY CHAIN SOLUTIONS, LLC; AKI TECHNOLOGIES, INC.; INMAR ANALYTICS, INC.; INMAR BRAND SOLUTIONS, INC.; INMAR CLEARING, INC.; INMAR RX SOLUTIONS, INC.; INMAR - YOUTECH, LLC; QUALANEX, LLC; CAROLINA COUPON CLEARING, INC. (N/K/A INMAR CLEARING, INC.); COLLECTIVE BIAS, INC. (N/K/A INMAR BRAND SOLUTIONS, INC.); MED-TURN, INC. (N/K/A INMAR RX SOLUTIONS, INC.)
To: JEFFERIES FINANCE LLC
Reel/Frame 064148/0903 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jan 20, 2023
From: BOWMAN, JACOB; FORSTER, PAM; JEREZ, LEONEL; MAXWELL, SETH
To: INMAR CLEARING, INC.
Reel/Frame 062440/0365 →