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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 12106214 B1

PDFFull Text
Date of Patent:
Oct. 01, 2024

Filed:

Oct. 18, 2022

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 (2023.01); G06F 18/2413 (2023.01); G06F 18/25 (2023.01); G06N 3/04 (2023.01); G06N 3/0442 (2023.01); G06N 3/0455 (2023.01); G06N 3/0464 (2023.01); G06N 3/084 (2023.01); G06V 10/44 (2022.01); G06V 10/46 (2022.01); G06V 10/764 (2022.01); G06V 10/80 (2022.01); G06V 10/82 (2022.01); G06V 20/10 (2022.01); G10L 15/16 (2006.01); G10L 15/20 (2006.01); G10L 15/24 (2013.01);
U.S. Cl.
CPC ...
G06N 3/08 (2013.01); G06F 18/2413 (2023.01); G06F 18/256 (2023.01); G06N 3/04 (2013.01); G06N 3/0442 (2023.01); G06N 3/0455 (2023.01); G06N 3/0464 (2023.01); G06N 3/084 (2013.01); G06V 10/454 (2022.01); G06V 10/462 (2022.01); G06V 10/764 (2022.01); G06V 10/806 (2022.01); G06V 10/811 (2022.01); G06V 20/10 (2022.01); G10L 15/16 (2013.01); G06V 10/82 (2022.01); G10L 15/20 (2013.01); G10L 15/24 (2013.01);
Abstract

A sensor transformation attention network (STAN) model including sensors configured to collect input signals, attention modules configured to calculate attention scores of feature vectors corresponding to the input signals, a merge module configured to calculate attention values of the attention scores, and generate a merged transformation vector based on the attention values and the feature vectors, and a task-specific module configured to classify the merged transformation vector is provided.

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