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:
Jan. 09, 2024

Filed:

Jan. 25, 2021
Applicant:

Verizon Media Inc., New York, NY (US);

Inventors:

Jelena Gligorijevic, San Jose, CA (US);

Ivan Stojkovic, San Jose, CA (US);

Martin Pavlovski, Philadelphia, PA (US);

Shubham Agrawal, San Jose, CA (US);

Djordje Gligorijevic, San Jose, CA (US);

Srinath Ravindran, Santa Clara, CA (US);

Richard Hin-Fai Tang, Saratoga, CA (US);

Shabhareesh Komirishetty, Sunnyvale, CA (US);

Chander Jayaraman Iyer, Santa Clara, CA (US);

Lakshmi Narayan Bhamidipati, Sunnyvale, CA (US);

Assignee:

Yahoo Assets LLC, New York, NY (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06N 3/08 (2023.01); G06F 11/34 (2006.01); G06F 16/955 (2019.01); G06F 18/22 (2023.01); G06F 18/214 (2023.01); G06F 18/2413 (2023.01); G06N 3/048 (2023.01);
U.S. Cl.
CPC ...
G06N 3/08 (2013.01); G06F 11/3438 (2013.01); G06F 16/9566 (2019.01); G06F 18/214 (2023.01); G06F 18/22 (2023.01); G06F 18/24147 (2023.01); G06N 3/048 (2023.01); G06F 2201/835 (2013.01);
Abstract

One or more computing devices, systems, and/or methods for generating time-preserving embeddings are provided. User trails of user activities performed by users are generated. Frequencies at which the activities were performed are identified. Indices are assigned to a set of activities identified from the activities as having frequencies above a threshold. Activity descriptions of the set of activities are mapped to the indices to generate a vocabulary. A model is trained using the user trails, timestamps of the activities, and the vocabulary to learn a set of time-preserving embeddings.


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