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. 13, 2023

Filed:

Jun. 24, 2020
Applicant:

West Affum Holdings Dac, Dublin, IE;

Inventors:

Zoie R. Engman, Kirkland, WA (US);

Erick M. Roane, Bellevue, WA (US);

Steven E. Sjoquist, Lynnwood, WA (US);

Jonathan P. Niegowski, Issaquah, WA (US);

Robert R. Buchanan, Bothell, WA (US);

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
A61N 1/39 (2006.01); G05B 13/02 (2006.01);
U.S. Cl.
CPC ...
A61N 1/3904 (2017.08); G05B 13/0265 (2013.01);
Abstract

'Artificial Intelligence' or 'AI' technology can be applied to Wearable Cardioverter Defibrillators (“WCDs”) and other wearable medical equipment in various ways, including: garment fitting and adjustment; analyzing electrocardiogam (“ECG”), other sensor data and/or other patient data (e.g., age, gender, previous medical conditions, etc.) in real time to detect/assess the patient's present condition and/or need for treatment for cardiac and other conditions (e.g., stroke, coughing, apnea, etc.); detecting imminent failure of the wearable medical device components; capturing and reporting data collected from the patient for presenting to clinicians; adjusting thresholds for alarms and notifications based on patient's responses; improving patient compliance based on the patient's past non-compliant behavior and actions that resulted in the patient becoming compliant; providing tests to the patient (e.g., grip test, dexterity tests, balance tests, etc.) and learning the patient's responses to detect/assess the patient's present condition and/or need for treatment; learning the patient's voice, activity, posture, time of day, etc. for implementing intelligent voice recognition/activation of the medical device.


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