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:

Jun. 25, 2020
Applicant:

Microsoft Technology Licensing, Llc, Redmond, WA (US);

Inventors:

Rohan Ramanath, Saratoga, CA (US);

Konstantin Salomatin, San Francisco, CA (US);

Jeffrey Douglas Gee, San Francisco, CA (US);

Onkar Anant Dalal, Santa Clara, CA (US);

Gungor Polatkan, San Jose, CA (US);

Sara Smoot Gerrard, Redwood City, CA (US);

Deepak Kumar, Mountain View, CA (US);

Rupesh Gupta, Sunnyvale, CA (US);

Jiaqi Ge, Sunnyvale, CA (US);

Lingjie Weng, Sunnyvale, CA (US);

Shipeng Yu, Sunnyvale, CA (US);

Assignee:
Attorneys:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06Q 50/00 (2024.01); G06F 16/9535 (2019.01); G06F 16/958 (2019.01); G06F 18/214 (2023.01); G06N 5/04 (2023.01); G06Q 10/1053 (2023.01);
U.S. Cl.
CPC ...
G06Q 50/01 (2013.01); G06F 16/9535 (2019.01); G06F 16/958 (2019.01); G06F 18/214 (2023.01); G06N 5/04 (2013.01); G06Q 10/1053 (2013.01);
Abstract

In some embodiments, a computer system generates a recommendation for a user of an online service based on user actions that have been performed by the user within a threshold amount of time before the generation of the recommendation. For each user action, the computer system determines an intent classification that identifies an activity of the user and that corresponds to different types of user actions, as well as a preference classification that identifies a target of the activity, and then stores these intent and preference classifications as part of indications of the user actions for use in generating different types of recommendations using different types of recommendation models. Additionally, the computer system may use mini-batches of data from an incoming stream of logged data to train an incremental update to one or more recommendation models.


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