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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.

Patent No.:

US 11200611 B1

PDFFull Text
Date of Patent:
Dec. 14, 2021

Filed:

Dec. 28, 2018

Computer vision for unsuccessful queries and iterative search

Applicant:

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

Inventors:

Robinson Piramuthu, Oakland, CA (US);

Timothy Samuel Keefer, San Jose, CA (US);

Ashmeet Singh Rekhi, Campbell, CA (US);

Padmapriya Gudipati, San Jose, CA (US);

Mohammadhadi Kiapour, San Francisco, CA (US);

Shuai Zheng, Berkeley, CA (US);

Alberto Ordonez Pereira, Santa Clara, CA (US);

Ravindra Surya Lanka, San Jose, CA (US);

Md Atiq ul Islam, San Jose, CA (US);

Nicholas Anthony Whyte, San Jose, CA (US);

Giridharan Iyengar, San Jose, CA (US);

Bryan Allen Plummer, Urbana, IL (US);

Assignee:

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

Attorney:

FIG. 1 Patents

Primary Examiner:

Pavan Mamillapalli

Int. Cl.
CPC ...
G06F 17/00 (2019.01); G06F 7/00 (2006.01); G06Q 30/06 (2012.01); G06F 16/538 (2019.01); G06F 16/9535 (2019.01); G06F 16/532 (2019.01); G06K 9/66 (2006.01); G06F 16/583 (2019.01); G06N 20/00 (2019.01); G06F 3/01 (2006.01); G06K 9/00 (2006.01); G06K 9/62 (2006.01); G06F 3/0482 (2013.01); G06F 3/0484 (2013.01); G06F 3/0488 (2013.01);
U.S. Cl.
CPC ...
G06Q 30/0627 (2013.01); G06F 3/017 (2013.01); G06F 16/532 (2019.01); G06F 16/538 (2019.01); G06F 16/583 (2019.01); G06F 16/9535 (2019.01); G06K 9/00671 (2013.01); G06K 9/6202 (2013.01); G06K 9/66 (2013.01); G06N 20/00 (2019.01); G06Q 30/0643 (2013.01); G06F 3/0482 (2013.01); G06F 3/0488 (2013.01); G06F 3/04842 (2013.01); G06Q 30/0621 (2013.01); G06Q 30/0631 (2013.01);
Abstract

Computer vision for unsuccessful queries and iterative search is described. The described system leverages visual search techniques by determining visual characteristics of objects depicted in images and describing them, e.g., using feature vectors. In some aspects, these visual characteristics are determined for search queries that are identified as not being successful. Aggregated information describing visual characteristics of images of unsuccessful search queries is used to determine common visual characteristics and objects depicted in those images. This information can be used to inform other users about unmet needs of searching users. In some aspects, these visual characteristics are used in connection with iterative image searches where users select an initial query image and then the search results are iteratively refined. Unlike conventional techniques, the described system iteratively refines the returned search results using an embedding space learned from binary attribute labels describing images.

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