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.:
Date of Patent:
Sep. 05, 2023
Filed:
Sep. 09, 2020
Applicant:
Life Spectacular, Inc., San Francisco, CA (US);
Inventors:
Zaoshi Amy Yuan, San Francisco, CA (US);
Ming S. Zhao, San Francisco, CA (US);
Assignee:
LIFE SPECTACULAR, INC., San Francisco, CA (US);
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G16H 20/00 (2018.01); G16H 50/70 (2018.01); G06Q 30/0601 (2023.01); G06N 20/00 (2019.01); G06Q 30/0282 (2023.01); A61K 8/00 (2006.01); A61Q 19/00 (2006.01); A61Q 19/08 (2006.01); A61Q 19/10 (2006.01); G01N 33/68 (2006.01); A61N 5/06 (2006.01); G16H 50/20 (2018.01); G16H 50/80 (2018.01); G16H 50/30 (2018.01); A61B 5/00 (2006.01); A61F 7/00 (2006.01); A61N 7/00 (2006.01);
U.S. Cl.
CPC ...
G06Q 30/0631 (2013.01); A61B 5/441 (2013.01); A61K 8/00 (2013.01); A61N 5/0616 (2013.01); A61Q 19/005 (2013.01); A61Q 19/007 (2013.01); A61Q 19/008 (2013.01); A61Q 19/08 (2013.01); A61Q 19/10 (2013.01); G01N 33/6881 (2013.01); G06N 20/00 (2019.01); G06Q 30/0282 (2013.01); G16H 20/00 (2018.01); G16H 50/20 (2018.01); G16H 50/30 (2018.01); G16H 50/70 (2018.01); G16H 50/80 (2018.01); A61B 5/411 (2013.01); A61B 2505/05 (2013.01); A61F 2007/0052 (2013.01); A61N 2007/0034 (2013.01); G01N 2800/00 (2013.01); G06T 2207/30088 (2013.01);
Abstract
Systems and methods for generating user-specific recommendations of products or services by collaborative filtering executed and/or performed by one or more trained or untrained predictive models configured to ingest product attribute(s), product purpose(s), user location data, and/or user demographics. The predictive model(s) are leveraged to detect and determine user-specific preferences for, and preferences against, particular attributes, features, ingredients, aesthetic styles, and so on.