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. 11, 2025

Filed:

Apr. 05, 2021
Applicant:

Ebay Inc., San Jose, CA (US);

Inventors:

Shuai Zheng, Beijing, CN;

Robinson Piramuthu, Oakland, CA (US);

Assignee:

eBay Inc., San Jose, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06Q 30/00 (2023.01); G06F 16/532 (2019.01); G06F 16/583 (2019.01); G06F 18/214 (2023.01); G06F 18/22 (2023.01); G06F 18/24 (2023.01); G06N 3/006 (2023.01); G06N 3/045 (2023.01); G06N 3/08 (2023.01); G06N 5/02 (2023.01); G06Q 20/12 (2012.01); G06Q 20/40 (2012.01); G06Q 30/0601 (2023.01); G06T 7/00 (2017.01); G06V 20/62 (2022.01); G06N 7/01 (2023.01);
U.S. Cl.
CPC ...
G06Q 30/0625 (2013.01); G06F 16/532 (2019.01); G06F 16/583 (2019.01); G06F 18/214 (2023.01); G06F 18/22 (2023.01); G06F 18/24 (2023.01); G06N 3/006 (2013.01); G06N 3/045 (2023.01); G06N 3/08 (2013.01); G06N 5/02 (2013.01); G06Q 20/12 (2013.01); G06Q 20/40 (2013.01); G06T 7/0004 (2013.01); G06V 20/63 (2022.01); G06N 7/01 (2023.01); G06T 2207/30108 (2013.01);
Abstract

Systems, methods, and computer program products for identifying a candidate product in an electronic marketplace based on a visual comparison between candidate product image visual text content and input query image visual text content. Unlike conventional optical character recognition (OCR) based systems, embodiments automatically localize and isolate portions of a candidate product image and an input query image that each contain visual text content, and calculate a visual similarity measure between the respective portions. A trained neural network may be re-trained to more effectively find visual text content by using the localized and isolated visual text content portions as additional ground truths. The visual similarity measure serves as a visual search result score for the candidate product. Any number of images of any number of candidate products may be compared to an input query image to enable text-in-image based product searching without resorting to conventional OCR techniques.


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