US 11,869,641 B2
Systems and methods for determining whether an individual is sick based on machine learning algorithms and individualized data
Dwayne Kurfirst, Hartford, CT (US); and Robert E. Bates, III, Hartford, CT (US)
Assigned to Aetna Inc., Hartford, CT (US)
Filed by Aetna Inc., Hartford, CT (US)
Filed on Dec. 11, 2020, as Appl. No. 17/119,982.
Prior Publication US 2022/0189591 A1, Jun. 16, 2022
Int. Cl. G16H 10/60 (2018.01); G06Q 10/0635 (2023.01); G06N 20/00 (2019.01); G06F 18/214 (2023.01)
CPC G16H 10/60 (2018.01) [G06F 18/2148 (2023.01); G06N 20/00 (2019.01); G06Q 10/0635 (2013.01)] 24 Claims
OG exemplary drawing
 
1. A user device, comprising:
one or more processors; and
a non-transitory computer-readable medium having processor-executable instructions stored thereon, wherein the processor-executable instructions, when executed, facilitate:
receiving, from a wearable device and at a first instance in time, first sensor information indicating first health characteristics associated with an individual;
generating a baseline health model of the individual based on the first sensor information;
obtaining a facial image of the individual;
obtaining an audio file comprising a voice recording of the individual;
determining a facial recognition confidence value associated with whether the individual is sick based on inputting the facial image into a facial recognition machine learning dataset that is individualized for the individual;
determining a voice recognition confidence value associated with whether the individual is sick based on inputting the audio file into a voice recognition machine learning dataset that is individualized for the individual;
determining whether the individual is sick based on the baseline health model, the facial recognition confidence value, and the voice recognition confidence value; and
causing display of a prompt indicating whether the individual is sick.