US 11,877,830 B2
Machine learning health analysis with a mobile device
Alexander Vainius Valys, Sunnyvale, CA (US); Frank Losasso Petterson, Los Altos Hills, CA (US); Conner Daniel Cross Galloway, Sunnyvale, CA (US); David E. Albert, Oklahoma City, OK (US); Ravi Gopalakrishnan, San Francisco, CA (US); Lev Korzinov, San Francisco, CA (US); Fei Wang, San Francisco, CA (US); Euan Thomson, Los Gatos, CA (US); Nupur Srivastava, San Francisco, CA (US); Omar Dawood, San Francisco, CA (US); and Iman Abuzeid, San Francisco, CA (US)
Assigned to ALIVECOR, INC., Mountain View, CA (US)
Filed by AliveCor, Inc., Mountain View, CA (US)
Filed on Sep. 24, 2019, as Appl. No. 16/580,574.
Application 16/580,574 is a continuation of application No. 16/153,403, filed on Oct. 5, 2018, abandoned.
Claims priority of provisional application 62/589,477, filed on Nov. 21, 2017.
Claims priority of provisional application 62/569,309, filed on Oct. 6, 2017.
Prior Publication US 2020/0107733 A1, Apr. 9, 2020
Prior Publication US 2020/0281485 A9, Sep. 10, 2020
Int. Cl. A61B 5/0205 (2006.01); A61B 5/00 (2006.01); A61B 5/024 (2006.01); A61B 5/0245 (2006.01); G16H 50/20 (2018.01); G16Z 99/00 (2019.01); G16H 40/63 (2018.01); G16H 40/67 (2018.01); G16H 50/70 (2018.01); A61B 5/349 (2021.01); A61B 5/361 (2021.01); G16H 50/30 (2018.01); A61B 5/11 (2006.01); A61B 5/021 (2006.01); G16H 10/60 (2018.01); G16H 15/00 (2018.01)
CPC A61B 5/02055 (2013.01) [A61B 5/0022 (2013.01); A61B 5/0245 (2013.01); A61B 5/02405 (2013.01); A61B 5/02416 (2013.01); A61B 5/349 (2021.01); A61B 5/361 (2021.01); A61B 5/681 (2013.01); A61B 5/7264 (2013.01); A61B 5/7267 (2013.01); A61B 5/7275 (2013.01); A61B 5/742 (2013.01); A61B 5/746 (2013.01); G16H 40/63 (2018.01); G16H 40/67 (2018.01); G16H 50/20 (2018.01); G16H 50/70 (2018.01); G16Z 99/00 (2019.02); A61B 5/021 (2013.01); A61B 5/02438 (2013.01); A61B 5/1118 (2013.01); A61B 5/6898 (2013.01); G16H 10/60 (2018.01); G16H 15/00 (2018.01); G16H 50/30 (2018.01)] 20 Claims
OG exemplary drawing
 
1. An apparatus, comprising:
a processing device;
a heath-indicator data sensor operatively coupled to the processing device;
and a memory having instructions stored thereon that, when executed by the processing device, cause the processing device to:
receive measured low-fidelity health-indicator data and other-factor data of a user at a time, wherein the measured low-fidelity health-indicator data is obtained by the health-indicator data sensor;
input a set of data comprising the low-fidelity health-indicator data and the other-factor data into a trained high-fidelity machine learning model, wherein the trained high-fidelity machine learning model is configured to predict whether an event is normal or abnormal, wherein if the high-fidelity machine learning model predicts that the event will be abnormal, the high-fidelity machine learning model further calculates an amount of time the event will be abnormal; and in response to the event being abnormal, send a notification that a health of the user is abnormal.