US-12620487-B2 - Systems and methods for selecting an intervention based on effective age

Abstract

A system method for selecting an intervention based on effective age, the system including at least a server, the at least a server designed and configured to obtain user data, project an actuarial life expectancy using a user chronological age, determine a telomeric age factor of the user by calculating a variance between the actuarial life expectancy and a projected actual mortality date, calculate an effective age of the user based on the telomeric age factor, and select an intervention from a plurality of configured to reduce the effective age.

Inventors

• Kenneth Neumann

Assignees

• KPN INNOVATIONS LLC

Dates

Publication Date

20260505

Application Date

20240510

Claims (20)

1. 1 . A system for selecting an intervention based on effective age, the system comprising: at least a server, the at least a server designed and configured to: obtain user data; project an actuarial life expectancy using a user chronological age; determine a telomeric age factor of the user by calculating a variance between the actuarial life expectancy and a projected actual mortality date, wherein calculating the variance comprises: receiving training data, wherein the training data correlates an aspect of the user data to the variance between the actuarial life expectancy and a projected actual mortality date; generating, using a supervised machine learning process, a machine learning model, wherein the machine learning model comprises a life phase classifier trained by training data correlating the at least a measure of endocrine function to a user chronological age, wherein the life phase classifier is configured to: receive the at least a measure of endocrine function as input, and the life phase classifier is configured to output an endocrinal life phase; and determining the variance between the actuarial life expectancy and a projected actual mortality date using the machine learning model; calculate an effective age of the user based on the telomeric age factor; generate interventions from a plurality of interventions configured to reduce the effective age; and select an intervention from a plurality of interventions configured to reduce the effective age.
2. 2 . The system of claim 1 , wherein calculating the effective age further comprises generating a life stage age factor by: training a life stage machine learning model with a life stage training set correlating biomarkers to life stage age factor metrics; and outputting, by the life stage machine learning model, the life stage age factor.
3. 3 . The system of claim 2 , wherein training the life stage machine learning model further comprises generating a training dataset correlating biomarkers to gender based metadata.
4. 4 . The system of claim 3 , wherein generating the training dataset comprises receiving data from a best practices platform.
5. 5 . The system of claim 1 , wherein the at least a server is further configured to adjust the effective age based on a user's psychological state.
6. 6 . The system of claim 1 , wherein the server is further configured to calculate an influence score based on the relationship between the user and an individual.
7. 7 . The system of claim 6 , wherein the at least server is further configured to adjust the effective age based on the influence score.
8. 8 . The system of claim 1 , wherein the effective age is based on a stress level of the user.
9. 9 . The system of claim 1 , wherein selecting the intervention comprises generating a nourishment plan.
10. 10 . The system of claim 9 , wherein generating the nourishment plan comprises: training a machine learning model with data correlating user data to nutritional data; imputing biomarkers of the user into the machine learning model; and outputting the nourishment plan using the machine learning model.
11. 11 . A method for selecting an intervention based on effective age, the method comprising: at least a server, the at least a server designed and configured to: obtaining, by a computing device, user data; projecting, by the computing device, an actuarial life expectancy using a user chronological age; determining, by the computing device, a telomeric age factor of the user by calculating a variance between the actuarial life expectancy and a projected actual mortality date, wherein calculating the variance comprises: receiving training data, wherein the training data correlates an aspect of the user data to the variance between the actuarial life expectancy and a projected actual mortality date; generating, using a supervised machine learning process, a machine learning model and, wherein the machine learning model comprises a life phase classifier trained by training data correlating the at least a measure of endocrine function to a user chronological age, wherein the life phase classifier is configured to: receive the at least a measure of endocrine function as input, and the life phase classifier is configured to output an endocrinal life phase; and determining the variance between the actuarial life expectancy and a projected actual mortality date using the machine learning model; calculating, by the computing device, an effective age of the user based on the telomeric age factor; generate, by the computing device, interventions from a plurality of interventions configured to reduce the effective age; and selecting, by the computing device, an intervention from a plurality of interventions configured to reduce the effective age.
12. 12 . The method of claim 11 , wherein calculating the effective age further comprises generating a life stage age factor by: training a life stage machine learning model with a life stage training set correlating biomarkers to life stage age factor metrics; and outputting, by the life stage machine learning model, the life stage age factor.
13. 13 . The method of claim 12 , wherein training the life stage machine learning model further comprises generating a training dataset correlating biomarkers to gender based metadata.
14. 14 . The method of claim 13 , wherein generating the training dataset comprises receiving data from a best practices platform.
15. 15 . The method of claim 11 , further comprising adjusting the effective age based on a user's psychological state.
16. 16 . The method of claim 11 , furthering comprising calculating an influence score based on the relationship between the user and an individual.
17. 17 . The method of claim 16 , further comprising adjusting the effective age based on the influence score.
18. 18 . The method of claim 11 , wherein the effective age is based on a stress level of the user.
19. 19 . The method of claim 11 , wherein selecting the intervention comprises generating a nourishment plan.
20. 20 . The method of claim 19 , wherein generating the nourishment plan comprises: training a machine learning model with data correlating user data to nutritional data; imputing biomarkers of the user into the machine learning model; and outputting the nourishment plan using the machine learning model.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS This application is a continuation-in-part of Non-provisional application Ser. No. 17/541,606 filed on Dec. 3, 2021, and entitled “SYSTEMS AND METHODS FOR SELECTING AN INTERVENTION BASED ON EFFECTIVE AGE”, which is a continuation of Non-provisional application Ser. No. 16/558,502 filed on Sep. 3, 2019, now U.S. Pat. No. 11,227,691, and entitled “SYSTEMS AND METHODS FOR SELECTING AN INTERVENTION BASED ON EFFECTIVE AGE,” both of which are incorporated herein by reference in their entirety. FIELD OF THE INVENTION The present invention generally relates to the field of artificial intelligence. In particular, the present invention is directed to systems and methods for selecting an intervention based on effective age. BACKGROUND Analysis and recommendation generation regarding longevity is currently fraught with imprecision, due to the multiplicity of factors involved. This is further complicated by a lack of quantitative measures indicative of implementation of solutions; statistical soundness of a model is only predictive inasmuch as it reflects genuine feasibility of aggregated outputs. SUMMARY OF THE DISCLOSURE In an aspect, a system for selecting an intervention based on effective age, the system including at least a server, the at least a server designed and configured to obtain user data, project an actuarial life expectancy using a user chronological age, determine a telomeric age factor of the user by calculating a variance between the actuarial life expectancy and a projected actual mortality date, wherein calculating the variance includes receiving training data, wherein the training data correlates an aspect of the user data to the variance between the actuarial life expectancy and a projected actual mortality date, generating, using a supervised machine learning process, a machine learning model and, wherein the machine learning model includes a life phase classifier trained by training data correlating the at least a measure of endocrine function to a user chronological age, wherein the life phase classifier is configured to receive the at least a measure of endocrine function as input, the life phase classifier is configured to output an endocrinal life phase, and determining the variance between the actuarial life expectancy and a projected actual mortality date using the machine learning model, calculate an effective age of the user based on the telomeric age factor and select an intervention from a plurality of configured to reduce the effective age. In another aspect, a method for selecting an intervention based on effective age, the method including using a computing device configured to obtain user data, project an actuarial life expectancy using a user chronological age, determine a telomeric age factor of the user by calculating a variance between the actuarial life expectancy and a projected actual mortality date, wherein calculating the variance includes receiving training data, wherein the training data correlates an aspect of the user data to the variance between the actuarial life expectancy and a projected actual mortality date, generating, using a supervised machine learning process, a machine learning model and, wherein the machine learning model includes a life phase classifier trained by training data correlating the at least a measure of endocrine function to a user chronological age, wherein the life phase classifier is configured to receive the at least a measure of endocrine function as input, the life phase classifier is configured to output an endocrinal life phase, and determining the variance between the actuarial life expectancy and a projected actual mortality date using the machine learning model, calculate an effective age of the user based on the telomeric age factor and select an intervention from a plurality of configured to reduce the effective age. These and other aspects and features of non-limiting embodiments of the present invention will become apparent to those skilled in the art upon review of the following description of specific non-limiting embodiments of the invention in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS For the purpose of illustrating the invention, the drawings show aspects of one or more embodiments of the invention. However, it should be understood that the present invention is not limited to the precise arrangements and instrumentalities shown in the drawings, wherein: FIG. 1 is a block diagram illustrating an exemplary embodiment of a system for selecting an intervention based on effective age; FIG. 2 is a block diagram illustrating an exemplary embodiment of a user database; FIG. 3 is a block diagram illustrating an exemplary embodiment of an expert database; FIG. 4 is a block diagram illustrating an exemplary embodiment of an intervention element database; FIG. 5 is a flow diagram illustrating an exemplary embodiment of a method of selecting an intervention based