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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.

Patent No.:

US 11501154 B1

PDFFull Text
Date of Patent:
Nov. 15, 2022

Filed:

Mar. 05, 2018

Sensor transformation attention network (stan) model

Applicants:

Samsung Electronics Co., Ltd., Suwon-si, KR;

Universitaet Zuerich, Zurich, CH;

Inventors:

Stefan Braun, Saint-Louis, FR;

Daniel Neil, Zurich, CH;

Enea Ceolini, Zurich, CH;

Jithendar Anumula, Zurich, CH;

Shih-Chii Liu, Zurich, CH;

Assignees:

SAMSUNG ELECTRONICS CO., LTD., Suwon-si, KR;

UNIVERSITAET ZUERICH, Zurich, CH;

Attorney:

Sughrue Mion, PLLC

Primary Examiner:

Shane D Woolwine

Int. Cl.
CPC ...
G06N 3/08 (2006.01); G06N 3/04 (2006.01); G10L 15/16 (2006.01); G06K 9/62 (2022.01); G06V 10/44 (2022.01); G06V 20/10 (2022.01); G06V 10/80 (2022.01); G06V 10/764 (2022.01); G10L 15/24 (2013.01); G06V 10/82 (2022.01);
U.S. Cl.
CPC ...
G06N 3/08 (2013.01); G06K 9/627 (2013.01); G06K 9/6293 (2013.01); G06N 3/04 (2013.01); G06V 10/454 (2022.01); G06V 10/764 (2022.01); G06V 10/811 (2022.01); G06V 20/10 (2022.01); G10L 15/16 (2013.01); G06V 10/82 (2022.01); G10L 15/24 (2013.01);
Abstract

A sensor transformation attention network (STAN) model including sensors, attention modules, a merge module and a task-specific module is provided. The attention modules calculate attention scores of feature vectors corresponding to the input signals collected by the sensors. The merge module calculates attention values of the attention scores, and generates a merged transformation vector based on the attention values and the feature vectors. The task-specific module classifies the merged transformation vector.

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