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, 2020

Filed:

Nov. 16, 2017
Applicant:

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

Inventors:

Sungchul Kim, San Jose, CA (US);

Deepali Jain, Karnataka, IN;

Deepali Gupta, New Delhi, IN;

Eunyee Koh, San Jose, CA (US);

Branislav Kveton, San Jose, CA (US);

Nikhil Sheoran, Chandigarh, IN;

Atanu Sinha, Bangalore, IN;

Hung Hai Bui, Sunnyvale, CA (US);

Charles Li Chen, Athens, OH (US);

Assignee:

ADOBE INC., San Jose, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/00 (2006.01); G06N 3/04 (2006.01); G06N 3/08 (2006.01); G06F 16/954 (2019.01); G06K 9/62 (2006.01);
U.S. Cl.
CPC ...
G06K 9/00335 (2013.01); G06F 16/954 (2019.01); G06N 3/049 (2013.01); G06N 3/0427 (2013.01); G06N 3/0445 (2013.01); G06N 3/08 (2013.01); G06N 3/088 (2013.01); G06K 9/6269 (2013.01);
Abstract

Systems and methods provide for generating predictive models that are useful in predicting next-user-actions. User-specific navigation sequences are obtained, the navigation sequences representing temporally-related series of actions performed by users during navigation sessions. To each navigation sequence, a Recurrent Neural Network (RNN) is applied to encode the navigation sequences into user embeddings that reflect time-based, sequential navigation patterns for the user. Once a set of navigation sequences is encoded to a set of user embeddings, a variety of classifiers (prediction models) may be applied to the user embeddings to predict what a probable next-user-action may be and/or the likelihood that the next-user-action will be a desired target action.


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