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:
Jun. 03, 2025

Filed:

Aug. 19, 2021
Applicant:

Samsung Electronics Co., Ltd., Gyeonggi-Do, KR;

Inventors:

Md Mahbubur Rahman, San Jose, CA (US);

Bashima Islam, Champaign, IL (US);

Tousif Ahmed, San Jose, CA (US);

Nathan Robert Folkman, San Francisco, CA (US);

Anh Minh Dinh, San Francisco, CA (US);

Sean Bornheimer, San Francisco, CA (US);

Ebrahim Nematihosseinabadi, Santa Clara, CA (US);

Jilong Kuang, San Jose, CA (US);

Jun Gao, Menlo Park, CA (US);

Assignee:
Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
A61B 5/08 (2006.01); A61B 5/00 (2006.01);
U.S. Cl.
CPC ...
A61B 5/08 (2013.01); A61B 5/7267 (2013.01); A61B 5/7405 (2013.01); A61B 5/742 (2013.01); A61B 2562/0204 (2013.01);
Abstract

Passively monitoring a user's breathing with a device can include identifying breathing modes of the user's breathing and responsive to detecting a trigger mode based on the identifying, generating an instruction adapted to the trigger mode. The instruction can be conveyed to the user via the device. The monitoring can include determining phases of the user's breathing with the device. Determining the phases can include receiving acoustic signals generated by an acoustic sensor in response to a user's breathing and generating acoustic data comprising features extracted from the acoustic signals. Phases of the user's breathing can be determined by classifying the acoustic data using a machine learning model trained based on signal processing of motion signals generated by a motion sensor in response to human breathing motions. Though trained using signal processing of motion signals, the machine learning model is trained to classify acoustic data.


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