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. 02, 2024

Filed:

Mar. 25, 2021
Applicant:

Optimizely North America Inc., Nashua, NH (US);

Inventors:

Brian Taylor, White Hall, MD (US);

Spencer Eldon Pingry, Leesburg, VA (US);

Laura Kreisberg, Reston, VA (US);

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06Q 30/0242 (2023.01); G06Q 30/0251 (2023.01); G06N 3/04 (2023.01); G06N 3/08 (2023.01); G06Q 30/0204 (2023.01);
U.S. Cl.
CPC ...
G06Q 30/0244 (2013.01); G06N 3/04 (2013.01); G06N 3/08 (2013.01); G06Q 30/0204 (2013.01); G06Q 30/0254 (2013.01);
Abstract

Various implementations of the invention for predicting customer behavior are described. Various implementations of the invention comprise an embedding component configured to receive and embed sequential inputs regarding a plurality of customer interactions with an online presence of a client; a plurality of causal dilated convolutional 'CDC' elements configured to receive the embedded sequential inputs and to output a feature vector, where each CDC element comprises two causal dilated convolutions with regularization that is bypassed with a skip connection; a plurality of dense neural network elements configured to receive the feature vector and non-sequential inputs regarding a plurality of other customer interactions with the client, where each of the plurality of dense neural network elements comprises two dense neural networks with regularization that is bypassed with a skip connection; and an output generator configured to receive the output from the plurality of dense neural network elements and to generate a distribution of times over which a particular customer event will occur and/or a likelihood estimation that the particular customer event will occur within a particular time period.


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