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:
Apr. 21, 2020

Filed:

Nov. 30, 2018
Applicant:

Affectiva, Inc., Boston, MA (US);

Inventors:

Taniya Mishra, New York, NY (US);

George Alexander Reichenbach, Andover, MA (US);

Rana el Kaliouby, Milton, MA (US);

Assignee:

Affectiva, Inc., Boston, MA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 13/40 (2011.01); G06K 9/62 (2006.01); G06K 9/00 (2006.01); G06K 9/46 (2006.01); G10L 15/00 (2013.01); G10L 15/06 (2013.01); G10L 15/16 (2006.01); G10L 15/22 (2006.01); G10L 25/63 (2013.01); G10L 13/027 (2013.01); G10L 25/78 (2013.01);
U.S. Cl.
CPC ...
G06T 13/40 (2013.01); G06K 9/00302 (2013.01); G06K 9/4628 (2013.01); G06K 9/6256 (2013.01); G06K 9/6262 (2013.01); G06K 9/6271 (2013.01); G10L 15/005 (2013.01); G10L 15/063 (2013.01); G10L 15/16 (2013.01); G10L 15/22 (2013.01); G10L 25/63 (2013.01); G10L 13/027 (2013.01); G10L 25/78 (2013.01); G10L 2015/223 (2013.01);
Abstract

Techniques are described for image generation for avatar image animation using translation vectors. An avatar image is obtained for representation on a first computing device. An autoencoder is trained, on a second computing device comprising an artificial neural network, to generate synthetic emotive faces. A plurality of translation vectors is identified corresponding to a plurality of emotion metrics, based on the training. A bottleneck layer within the autoencoder is used to identify the plurality of translation vectors. A subset of the plurality of translation vectors is applied to the avatar image, wherein the subset represents an emotion metric input. The emotion metric input is obtained from facial analysis of an individual. An animated avatar image is generated for the first computing device, based on the applying, wherein the animated avatar image is reflective of the emotion metric input and the avatar image includes vocalizations.


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