Granted Patent B2

US 12,645,913 · App. 17/952,620 · Granted Jun 2, 2026

Inventor: Jeffrey Gladden (Rio Grande, PR)

Assignee: Oceandrive Ventures, LLC

G06N3/04G06N3/08G16H20/30

Abstract

Claims (55)

1 . An apparatus for enhancing longevity, wherein the apparatus comprises:

at least a processor; and

a memory communicatively connected to the at least a processor, the memory containing instructions configuring the at least a processor to:

receive a longevity measurement comprising at least a complete blood count related to a user;

provide a health impact factor, wherein the health impact factor is a function of the longevity measurement, and wherein the longevity measurement further comprises at least a historical longevity parameter;

calculate a longevity parameter as a function of the longevity measurement and the health impact factor;

assign the user a longevity level as a function of the longevity parameter, wherein the longevity level comprises a likelihood that a system of the user will make it to a given age without failure and wherein assigning the user the longevity level further comprises:

training a longevity machine learning model to generate a longevity classifier using a longevity training data, wherein the longevity training data contains a plurality of data entries correlating examples of longevity parameters to examples of longevity levels;

classifying the longevity parameter to the longevity level using the longevity classifier;

assigning the user the longevity level as a function of the classification, wherein assigning the longevity level further comprises utilizing a knowledge-based system (KBS), wherein utilizing the KBS comprises:

classifying the longevity parameter to the longevity level based on an if-then rule format, using an inference engine of the KBS;

updating the KBS based on previous longevity level classifications from the longevity machine learning model; and

classifying an updated longevity parameter to an updated longevity level, using an updated inference engine of the KBS, wherein a knowledge base of the KBS stores rules and longevity data in a subsumption ontology distinct from implicitly embedded procedural code and wherein the inference engine utilizes forward chaining to assert new facts from known facts and backward chaining to determine additional facts required to achieve a goal longevity level; and

generate a longevity plan as a function of the updated longevity parameter and the updated longevity level, wherein generating the longevity plan comprises:

receiving first training data correlating input data, the input data including longevity levels, to longevity plan data;

training a neural network using the first training data, wherein the neural network is configured to output the longevity plan;

outputting the longevity plan to the user;

receiving user feedback regarding effects of the longevity plan;

receiving survey data related to a user's health after monitoring the longevity plan;

updating the longevity plan based on the user feedback and the survey data; and

using the updated longevity plan as training data to train the neural network.

2 . The apparatus of claim 1 , wherein the longevity parameter comprises an age comparison metric for the system of the user.

3 . The apparatus of claim 1 , wherein the longevity parameter comprises a rate of aging.

4 . The apparatus of claim 1 , wherein the longevity measurement comprises a biomarker associated with aging.

5 . The apparatus of claim 1 , wherein the longevity level comprises a predicted life span of a given system.

6 . The apparatus of claim 1 , wherein the longevity level is assigned as a function of a fuzzy inference.

7 . The apparatus of claim 1 , wherein the longevity plan comprises a set of corrective measures configured to improve the user's longevity level.

8 . The apparatus of claim 1 , wherein assigning the user the longevity level is a function of a rate of aging.

9 . The apparatus of claim 1 , wherein receiving the longevity measurement related to the user comprises receiving the longevity measurement from a longevity database.

10 . A method for enhancing longevity, wherein the method comprises:

receiving, using a processor, a longevity measurement comprising at least a complete blood count related to a user;

providing, using the processor, a health impact factor wherein the health impact factor is a function of the longevity measurement, and wherein the longevity measurement comprises at least a historical longevity parameter;

calculating, using the processor, a longevity parameter as a function of the longevity measurement and the health impact factor;

training, using the processor, a longevity machine learning model to generate a longevity classifier using a longevity training data, wherein the longevity training data contains a plurality of data entries correlating examples of longevity parameters to examples of longevity levels;

classifying, using the processor, the longevity parameter to a longevity level using the longevity classifier;

assigning, using the processor, the user the longevity level as a function of the classification, wherein the longevity level comprises a likelihood that a system of the user will make it to a given age without failure;

classifying the longevity parameter to the longevity level based on an if-then rule format, using an inference engine of a knowledge-based system (KBS);

updating the KBS based on previous longevity level classifications from the longevity machine learning model;

classifying an updated longevity parameter to an updated longevity level, using an updated inference engine of the KBS wherein a knowledge base of the KBS stores rules and longevity data in a subsumption ontology distinct from implicitly embedded procedural code and wherein the inference engine utilizes forward chaining to assert new facts from known facts and backward chaining to determine additional facts required to achieve a goal longevity level; and

generating, using the processor, a longevity plan as a function of the updated longevity parameter and the updated longevity level, wherein generating the longevity plan comprises:

receiving first training data correlating input data, the input data including longevity levels, to longevity plan data;

training a neural network using the first training data, wherein the neural network is configured to output the longevity plan;

outputting the longevity plan to the user;

receiving user feedback regarding effects of the longevity plan;

receiving survey data related to a user's health after monitoring the longevity plan;

updating the longevity plan based on the user feedback and the survey data; and

using the updated longevity plan as training data to train the neural network.

11 . The method of claim 10 , wherein the longevity parameter comprises an age comparison metric for the system of the user.

12 . The method of claim 10 , wherein the longevity parameter comprises a rate of aging.

13 . The method of claim 10 , wherein the longevity measurement comprises a biomarker associated with aging.

14 . The method of claim 10 , wherein the longevity level comprises a predicted life span of a given system.

15 . The method of claim 10 , wherein classifying the longevity parameter to the longevity level using the longevity classifier comprises assigning the longevity level as a function of a fuzzy inference.

16 . The method of claim 10 , wherein the longevity plan comprises a set of corrective measures configured to improve the user's longevity level.

17 . The method of claim 10 , wherein assigning the user the longevity level comprises assigning the longevity level as a function of a rate of aging.

18 . The method of claim 10 , wherein receiving the longevity measurement related to a user comprises receiving the longevity measurement from a longevity database.

Continuity (1)

References Cited (18)