US 12,136,493 B2
System and method for providing wellness recommendation
Robert Paul Hanlon, Jr., Stroudsburg, PA (US); and Monte Floyd Hancock, Jr., Murray, KY (US)
Assigned to CENTERLINE HOLDINGS, LLC
Filed by CENTERLINE HOLDINGS, LLC, Stroudsburg, PA (US)
Filed on Aug. 4, 2022, as Appl. No. 17/881,518.
Application 17/881,518 is a continuation of application No. 17/464,090, filed on Sep. 1, 2021, granted, now 11,417,429.
Claims priority of provisional application 63/150,402, filed on Feb. 17, 2021.
Claims priority of provisional application 63/074,670, filed on Sep. 4, 2020.
Prior Publication US 2022/0406463 A1, Dec. 22, 2022
Int. Cl. G16H 50/20 (2018.01); A61B 5/00 (2006.01); A61B 5/16 (2006.01); G06Q 40/03 (2023.01); G06Q 40/08 (2012.01); G16H 10/60 (2018.01); G16H 20/70 (2018.01); G16H 40/67 (2018.01); G16H 50/30 (2018.01); G16H 50/70 (2018.01); A61B 5/024 (2006.01)
CPC G16H 50/20 (2018.01) [A61B 5/165 (2013.01); A61B 5/486 (2013.01); G06Q 40/03 (2023.01); G06Q 40/08 (2013.01); G16H 10/60 (2018.01); G16H 20/70 (2018.01); G16H 40/67 (2018.01); G16H 50/30 (2018.01); G16H 50/70 (2018.01); A61B 5/02438 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method for providing a recommended wellness behavior using a machine learning algorithm, the method comprising:
a) receiving, for a user, user data for different wellness components, the user data comprising:
i) device data indicative of a physical health status or a stress status of the user;
ii) financial data for the user; and
iii) psychological data for the user;
b) receiving standards for the different wellness components;
c) for each wellness component, the machine learning algorithm:
i) determining a current user score based on the received user data;
ii) determining a comparison score based on the current user score;
iii) determining a deviation between the comparison score and the standard for the wellness component;
d) the machine learning algorithm identifying at least one recommended behavior to be performed by the user, the identification being based on the determination, for each wellness component, of the deviation between the comparison score and the standard for the wellness component; and
e) outputting data associated with the at least one recommended behavior to a training algorithm;
f) the training algorithm determining modified decision parameters for the machine learning algorithm based on the data associated with the at least one recommended behavior;
g) updating the machine learning algorithm based on the modified decision parameters; and
h) repeating operations c) and d) using the updated machine learning algorithm to generate at least one subsequent recommended behavior;
i) wherein operations a) to h) are performed by one or more processors.