Methods and systems for dietary communications using intelligent systems regarding endocrinal measurements

Abstract

A system for dietary communications using intelligent systems regarding endocrinal measurements includes a computing device designed and configured to obtain a first endocrinal measurement relating to a user; compare the first endocrinal measurement to an endocrinal system effect; generate a body dysfunction label for the first endocrinal measurement as a function of the endocrinal system effect; identify a dietary communication as a function of the body dysfunction label, the first endocrinal measurement, and a first machine learning process, the first machine learning process trained using a first training set relating endocrinal measurements and body dysfunction labels to dietary communications; and present the dietary communication.

What is claimed is: 
     
         1 . An apparatus for dietary communications using intelligent systems regarding endocrinal measurements, 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:
 obtain a first endocrinal measurement relating to a user; 
 compare the first endocrinal measurement to an endocrinal system effect; 
 generate a body dysfunction label for the first endocrinal measurement as a function of the comparing to the endocrinal system effect; 
 identify a dietary communication as a function of the body dysfunction label and the first endocrinal measurement, wherein identifying further comprises:
 training a first machine learning process as a function of a first training set relating inputs containing endocrinal measurements and body dysfunction labels to outputs containing dietary communications; and 
 identifying the dietary communication as a function of the trained first machine learning process; and 
 
 present the dietary communication on the computing device. 
   
     
     
         2 . The apparatus of  claim 1 , wherein the first endocrinal measurement identifies a current endocrinal disorder. 
     
     
         3 . The apparatus of  claim 1 , wherein the first endocrinal measurement identifies a probable endocrinal disorder. 
     
     
         4 . The apparatus of  claim 1 , wherein the at least a processor is further configured to select the endocrinal system effect as a function of a user attribute. 
     
     
         5 . The apparatus of  claim 1  wherein the at least a processor is further configured to:
 train a second machine learning process as a function of a second training set relating inputs containing endocrinal system effects to outputs containing body dysfunction labels; and 
 generate the body dysfunction label as a function of the trained second machine learning process, wherein the body dysfunction label is an output of the trained second machine learning process. 
 
     
     
         6 . The apparatus of  claim 1 , wherein the at least a processor is further configured to:
 choose an individual input as a function of the body dysfunction label;   receive an entry relating to the individual input from the user; and   identify the dietary communications as a function of the individual input.   
     
     
         7 . The apparatus of  claim 6 , wherein the individual input describes a user's fitness patterns. 
     
     
         8 . The apparatus of  claim 1 , wherein the body dysfunction label indicates if the first endocrinal measurement is within normal limits. 
     
     
         9 . The apparatus of  claim 1 , wherein the dietary communication comprises personalized nutritional information. 
     
     
         10 . The apparatus of  claim 1 , wherein the at least a processor is further configured to:
 obtain a second endocrinal measurement relating to the first endocrinal measurement; and   update the dietary communications as a function of the second endocrinal measurement.   
     
     
         11 . A method of dietary communications using intelligent systems regarding endocrinal measurements, the method comprising;
 obtaining, by a processor, a first endocrinal measurement relating to a user;   comparing, by the processor, the first endocrinal measurement to an endocrinal system effect;   generating, by the processor, a body dysfunction label for the first endocrinal measurement as a function of the comparing to the endocrinal system effect;   identifying, by the processor, a dietary communication as a function of the body dysfunction label and the first endocrinal measurement, wherein identifying further comprises:
 training a first machine learning process as a function of a first training set relating inputs containing endocrinal measurements and body dysfunction labels to outputs containing dietary communications; and 
 identifying the dietary communication as a function of the trained first machine learning process; and 
   presenting the dietary communication on the computing device.   
     
     
         12 . The method of  claim 11 , wherein the first endocrinal measurement identifies a current endocrinal disorder. 
     
     
         13 . The method of  claim 11 , wherein the first endocrinal measurement identifies a probable endocrinal disorder. 
     
     
         14 . The method of  claim 11 , wherein the endocrinal system effect is selected as a function of a user attribute. 
     
     
         15 . The method of  claim 11 , wherein generating the body dysfunction label further comprises:
 training a second machine learning process, as a function of a second training set, relating endocrinal system effects to body dysfunction labels; and   generating the body dysfunction label as a function of the trained second machine learning process, wherein the body dysfunction label is an output of the trained second machine learning process.   
     
     
         16 . The method of  claim 11 , wherein identifying the dietary communication further comprises:
 choosing an individual input as a function of the body dysfunction label;   receiving an entry relating to the individual input from the user; and   identifying the dietary communications as a function of the individual input.   
     
     
         17 . The method of  claim 16 , wherein the individual input relates to a user's fitness patterns. 
     
     
         18 . The method of  claim 11 , wherein the body dysfunction label indicates whether the first endocrinal measurement is within normal limits. 
     
     
         19 . The method of  claim 11 , wherein the dietary communication comprises personalized nutritional information. 
     
     
         20 . The method of  claim 11 , wherein identifying the dietary communication further comprises:
 obtaining a second endocrinal measurement relating to the first endocrinal measurement; and   updating the dietary communications as a function of the second endocrinal measurement.