Method and systems for simulating a vitality metric

Abstract

A system for simulating a vitality metric, the system comprising a computing device, wherein the computing device is configured to retrieve, from a user, a biotic extraction, generate a vitality metric, using a machine-learning model, wherein generating a vitality metric further comprises training a machine-learning model with training data corresponding to measuring biotic parameters present in the biotic extraction data and determining a metric that is a summation of all individual biotic parameters present in the biotic extraction data. Computing device determines a simulated metric, using a simulation machine-learning process, wherein the simulation perturbs a biotic parameter present in the vitality metric, wherein a biotic parameter is an element of numerical data relating to an element of data present in the at least a user biotic extraction. Computing device provides, to a user, a vitality metric and at least a user effort that resulted in a simulated metric.

What is claimed is: 
     
         1 . An apparatus for simulating a vitality metric, the apparatus comprising:
 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:
 retrieve a biotic extraction pertaining to a user; 
 generate a first vitality metric using a metric machine-learning model and the biotic extraction, wherein generating the first vitality metric further comprises:
 training a metric machine-learning model with training data, the training data containing a plurality of data entries correlating the biotic extraction data to measured biotic parameters pertaining to the user; and 
 generating the first vitality metric, the first vitality metric containing a summation of all individual biotic parameters associated with the biotic extraction data, as a function of the metric machine-learning model; 
 
 determine a simulated metric as a function of the generated first vitality metric of a user, wherein determining the simulated metric further comprises:
 inputting the first vitality metric into a simulation machine-learning process; 
 perturbing a biotic parameter present in the first vitality metric as a function of the simulation machine-learning process; and 
 determining, as a function of the output of the simulation machine-learning process, the simulated metric; and 
 
 provide, to a user, the first vitality metric and at least a user effort that produces the simulated metric. 
   
     
     
         2 . The apparatus of  claim 1 , wherein the biotic extraction further comprises data containing information of a user interaction with an ecological environment. 
     
     
         3 . The apparatus of  claim 1 , wherein perturbing the biotic parameter further comprises selecting a value of the biotic parameter, wherein the value of the biotic parameter is sampled from a range of values in a random manner. 
     
     
         4 . The apparatus of  claim 1 , wherein the simulated metric is an output describing a vitality metric as a function of perturbing a parameter that can be affected by a user effort. 
     
     
         5 . The apparatus of  claim 1 , wherein the simulation machine-learning process further comprises calculating the output for a simulated metric for all values within a range of values corresponding to a parameter in the biotic extraction data. 
     
     
         6 . The apparatus of  claim 1 , wherein the simulation machine-learning process further comprises a computational simulation by randomly perturbing parameters and determining the effect on a vitality metric to determine which parameters result in an outcome that may be the same or different than a first input vitality metric. 
     
     
         7 . The apparatus of  claim 1 , further comprising displaying to a user, a vitality metric and at least a user effort. 
     
     
         8 . The apparatus of  claim 1 , wherein determining the simulated metric further comprises utilizing the generated first vitality metric of the user and a user effort. 
     
     
         9 . The apparatus of  claim 1 , wherein the biotic parameter further comprises data relating to an element in the at least a user biotic extraction. 
     
     
         10 . The system of  claim 1  further comprising:
 receiving an indication from a user that the at least a user effort has been performed; 
 generating a second vitality metric as a function of the at least a user effort using the metric machine-learning model, wherein generating the second vitality metric further comprises determining how the at least a user effort has impacted a numerical parameter corresponding to the first vitality metric; and 
 identifying a numerical difference between the first vitality metric and the second vitality metric, wherein determining the numerical difference includes determining how the at least a user effort impacted the second vitality metric. 
 
     
     
         11 . A method for simulating a vitality metric, the method comprising:
 retrieving, by a processor, a biotic extraction pertaining to a user;   generating, by the processor, a first vitality metric using a metric machine-learning model and the biotic extraction, wherein generating the first vitality metric further comprises:
 training a metric machine-learning model with training data, the training data containing a plurality of data entries correlating the biotic extraction data to measured biotic parameters pertaining to the user; and 
 generating the first vitality metric, the first vitality metric containing a summation of all individual biotic parameters associated with the biotic extraction data, as a function of the metric machine-learning model; 
   determining, by the processor, a simulated metric as a function of the generated first vitality metric of a user, wherein determining the simulated metric further comprises:   inputting the first vitality metric into a simulation machine-learning process;   perturbing a biotic parameter present in the first vitality metric as a function of the simulation machine-learning process; and   determining, as a function of the output of the simulation machine-learning process, the simulated metric; and   providing, by the processor, to a user, the first vitality metric and at least a user effort that produces the simulated metric.   
     
     
         12 . The method of  claim 11 , wherein the biotic extraction further comprises data containing information of a user interaction with an ecological environment. 
     
     
         13 . The method of  claim 11 , wherein perturbing the biotic parameter further comprises selecting a value of the biotic parameter, wherein the value of the biotic parameter is sampled from a range of values in a random manner. 
     
     
         14 . The method of  claim 11 , wherein the simulated metric is an output describing a vitality metric as a function of perturbing a parameter that can be affected by a user effort. 
     
     
         15 . The method of  claim 11 , wherein the simulation machine-learning process further comprises calculating the output for a simulated metric for all values within a range of values corresponding to a parameter in the biotic extraction data. 
     
     
         16 . The method of  claim 11 , wherein the simulation machine-learning process further comprises a computational simulation by randomly perturbing parameters and determining the effect on a vitality metric to determine which parameters result in an outcome that may be the same or different than a first input vitality metric. 
     
     
         17 . The method of  claim 11  further comprising, displaying to the user, a vitality metric and at least a user effort. 
     
     
         18 . The method of  claim 11 , wherein determining the simulated metric further comprises utilizing the generated first vitality metric of user and user effort. 
     
     
         19 . The method of  claim 11 , wherein the biotic parameter further comprises data relating to an element in the at least a user biotic extraction. 
     
     
         20 . The method of  claim 11  further comprising:
 receiving an indication from a user that the at least a user effort has been performed; 
 generating a second vitality metric as a function of the at least a user effort using the metric machine-learning model, wherein generating the second vitality metric further comprises determining how the at least a user effort has impacted a numerical parameter corresponding to the first vitality metric; and 
 identifying a numerical difference between the first vitality metric and the second vitality metric, wherein determining the numerical difference includes determining how the at least a user effort impacted the second vitality metric.