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:
Mar. 04, 2025

Filed:

Jun. 26, 2023
Applicant:

Pinterest, Inc., San Francisco, CA (US);

Inventors:

Navin Agarwal, San Mateo, CA (US);

Judy Yi-Chun Hsieh, San Francisco, CA (US);

Debbie Ayano Limongan, San Mateo, CA (US);

Lianghao Chen, San Jose, CA (US);

Amit Aggarwal, Los Altos, CA (US);

Julie Bornstein, Hillsborough, CA (US);

Assignee:

Pinterest, Inc., San Francisco, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 16/20 (2019.01); G06F 16/2457 (2019.01); G06N 20/00 (2019.01); G06Q 30/0601 (2023.01); H04L 67/50 (2022.01); G06N 3/08 (2023.01);
U.S. Cl.
CPC ...
G06F 16/2457 (2019.01); G06N 20/00 (2019.01); G06Q 30/0631 (2013.01); H04L 67/535 (2022.05); G06N 3/08 (2013.01);
Abstract

A user preference hierarchy is determined from user response to images. Images may be tagged using machine learning models trained to determine values for images. Products are clustered according to product vectors. Images of products within a cluster are clustered according to composition and groups of images are selected from image clusters for soliciting feedback regarding user preference for products of a cluster. Feedback is used to train a user preference model to estimate affinity for a product vector. A user may provide feedback regarding a price point and products are weighted according to a distribution about the price point. The distribution may be asymmetrical according to direction of movement of the price point. Filters may be dynamically defined and presented to a user based on popularity and frequency of occurrence of attribute-value pairs of search results and based on feedback regarding the search results.


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