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:
Jan. 28, 2025

Filed:

Apr. 26, 2021
Applicant:

Blue Yonder Group, Inc., Scottsdale, AZ (US);

Inventors:

Ramakrishna Perla, Hyderabad, IN;

Arun Raj Parwana Adiraju, Hyderabad, IN;

Vineet Chaudhary, Hyderabad, IN;

Assignee:

Blue Yonder Group, Inc., Scottsdale, AZ (US);

Attorneys:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06F 18/214 (2023.01); G06N 20/20 (2019.01); G06T 3/4046 (2024.01); G06T 7/00 (2017.01); G06T 7/11 (2017.01); G06V 10/22 (2022.01); G06V 10/25 (2022.01); G06V 10/40 (2022.01); G06V 10/75 (2022.01); G06V 10/774 (2022.01); G06V 10/82 (2022.01);
U.S. Cl.
CPC ...
G06V 10/774 (2022.01); G06F 18/2148 (2023.01); G06N 20/20 (2019.01); G06T 3/4046 (2013.01); G06T 7/0004 (2013.01); G06T 7/11 (2017.01); G06V 10/235 (2022.01); G06V 10/25 (2022.01); G06V 10/40 (2022.01); G06V 10/759 (2022.01); G06V 10/82 (2022.01); G06T 2207/10004 (2013.01); G06T 2207/20021 (2013.01); G06T 2207/20092 (2013.01); G06T 2207/20104 (2013.01); G06T 2207/30108 (2013.01);
Abstract

A system and method of automatic product attribute recognition receive training images having bounding boxes associated with one or more products in the training images, receive attribute values for each of the one or more products in the training images, and train a first convolutional neural network (CNN) model to generate bounding boxes for and identify each of the one or more products with the training images until the accuracy of the first CNN model is above a first predetermined threshold. The system and method further train a second CNN model for each of the products associated with the cropped images until the second CNN generates attribute values for the one or more attributes with an accuracy above a second predetermined threshold, and automatically recognize the one or more attributes for a new product image by presenting the product image to the first and second CNN models.


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