US 12,334,205 B2
Methods and systems for generating lifestyle change recommendations based on biological extractions
Kenneth Neumann, Lakewood, CO (US)
Assigned to KPN INNOVATIONS, LLC, Lakewood, CO (US)
Filed by KPN INNOVATIONS, LLC., Lakewood, CO (US)
Filed on Oct. 31, 2022, as Appl. No. 17/977,231.
Application 17/977,231 is a continuation of application No. 16/824,958, filed on Mar. 20, 2020, granted, now 11,545,250.
Prior Publication US 2023/0116778 A1, Apr. 13, 2023
This patent is subject to a terminal disclaimer.
Int. Cl. G16H 20/30 (2018.01); G16H 20/60 (2018.01)
CPC G16H 20/30 (2018.01) [G16H 20/60 (2018.01)] 19 Claims
OG exemplary drawing
 
1. A system for generating lifestyle change recommendations based on biological extractions, the system comprising a computing device, the computing device designed and configured to:
receive a biological extraction pertaining to a user, wherein the biological extraction comprises user physiological data comprising responses to a questionnaire;
generate a plurality of lifestyle intervention combinations as a function of the biological extraction using a first machine-learning process, wherein generating the plurality of lifestyle intervention combinations further comprises:
training a first machine-learning model using a first training data set and the first machine-learning process, wherein the first training data set comprises entries correlating biological extraction data with lifestyle intervention combinations; and
utilizing the first machine-learning model to output the plurality of lifestyle intervention combinations using the biological extraction as an input;
derive a user inclination enumeration as a function of at least a user input, wherein the user inclination enumeration is a data structure that quantitatively measures a degree of importance a user places on a plurality of intervention elements;
assign, using a second machine-learning process, to each lifestyle intervention combination of the plurality of lifestyle intervention combinations, a projected degree of user adherence as a function of the user inclination enumeration, wherein assigning the degree of projected user adherence comprises:
training a second machine-learning model using a second training data set and the second machine-learning process, wherein the second training data set includes entries correlating lifestyle intervention combinations with adherence data; and
utilizing the second machine-learning model to output the degree of projected user adherence using the plurality of lifestyle intervention combination as an input; and
select, from the plurality of lifestyle intervention combinations, a lifestyle intervention combination as a function of the projected degree of user adherence of the selected lifestyle intervention combination.