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. 07, 2026

Filed:

Jun. 29, 2020
Applicant:

Aetna Inc., Hartford, CT (US);

Inventors:

Naiqian Zhi, Hartford, CT (US);

Benjamin Wanamaker, Highland, UT (US);

Rajiv Bhan, Hartford, CT (US);

Sumeet Kumar, San Jose, CA (US);

Assignee:

Aetna Inc., Hartford, CT (US);

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
A61B 5/0205 (2006.01); A61B 5/00 (2006.01); A61B 5/11 (2006.01); A61B 5/1455 (2006.01); G06F 16/2452 (2019.01); G06N 3/04 (2023.01); G06N 3/08 (2023.01); G16H 10/60 (2018.01); G16H 20/30 (2018.01); G16H 40/67 (2018.01); A61B 5/024 (2006.01);
U.S. Cl.
CPC ...
A61B 5/0205 (2013.01); A61B 5/0022 (2013.01); A61B 5/1118 (2013.01); A61B 5/14551 (2013.01); A61B 5/681 (2013.01); A61B 5/7264 (2013.01); G06F 16/24522 (2019.01); G06N 3/04 (2013.01); G06N 3/08 (2013.01); G16H 10/60 (2018.01); G16H 20/30 (2018.01); G16H 40/67 (2018.01); A61B 5/02438 (2013.01);
Abstract

A system and method are disclosed for monitoring health conditions based on data collected by a wearable device such as an activity tracker or a smart watch. Deep learning algorithms are configured to process an input vector that includes monitored parameter data collected by the wearable device as well as embedding data obtained from health records corresponding to a user account registered to the wearable device. In some embodiments, the input vector can also include social determinants data and/or demographic data. The output of the deep learning algorithms provides classifiers that represent probabilities that the user of the wearable device has an underlying health condition. If any underlying health condition is detected, then the user can be notified directly, via the wearable device or an associated application or technology, or indirectly, via a primary care provider associated with the user.


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