Method and system for data classification to generate a second alimentary provider

Abstract

A method of determining a second alimentary provider is disclosed. The method inputs an order for an alimentary combination from a user. The alimentary combination is prepared by a first alimentary provider. The method classifies a plurality of alimentary providers. The method computes an alimentary provider score for a plurality of second alimentary combinations prepared by the plurality of alimentary providers as a function of a first machine-learning process, the machine learning process trained by training data correlating alimentary provider scores to alimentary combinations. The method selects a second alimentary provider from the plurality of alimentary providers as a function of the alimentary provider score. The method outputs the second alimentary provider to the user. A system of determining a second alimentary provider is also disclosed.

What is claimed is: 
     
         1 . A system of outputting a second alimentary provider, the system comprising:
 a computing device configured to:   input a request for an alimentary combination from a user;   generate a plurality of first alimentary providers based on at least a type of cuisine comprising a dieting method;   determine the alimentary combination is not available at the plurality of first alimentary providers;   classify a plurality of alimentary providers;   compute an alimentary combination score for a plurality of second alimentary combinations prepared by the plurality of alimentary providers as a function of a machine-learning process, wherein each of the plurality of second alimentary combinations comprises at least a replacement for the alimentary combination; and   select a second alimentary provider from the plurality of alimentary providers as a function of the alimentary combination score; and   output the second alimentary provider to the user.   
     
     
         2 . The system of  claim 1 , wherein the dieting method comprises a restricted medical diet. 
     
     
         3 . The system of  claim 1 , wherein classifying the plurality of alimentary providers further comprises utilizing a machine-learning process to generate an alimentary provider classifier. 
     
     
         4 . The system of  claim 3 , wherein utilizing the machine-learning process to generate the alimentary provider classifier comprises:
 receiving alimentary provider training data;   training the alimentary provider classifier as a function of the alimentary provider training data; and   outputting the plurality of alimentary providers as a function of the alimentary provider classifier and the request for the alimentary combination.   
     
     
         5 . The system of  claim 1 , wherein computing the alimentary combination score as a function of the machine-learning process further comprises training a machine-learning model with training data correlating an alimentary combination score to alimentary combinations. 
     
     
         6 . The system of  claim 1 , wherein generating the plurality of first alimentary providers further comprises filtering the plurality of first alimentary providers as a function of user preferences. 
     
     
         7 . The system of  claim 6 , wherein the user preferences include at least a selection of a delivery time. 
     
     
         8 . The system of  claim 1 , wherein the computing device is further configured to:
 train an alimentary combination classifier as a function of second alimentary combination training data; and   identify the second alimentary combination as a function of the alimentary combination classifier and a requested alimentary combination.   
     
     
         9 . The system of  claim 1 , wherein the computing device is further configured to:
 receive a geographical parameter of a first alimentary provider of the plurality of first alimentary providers;   receive geographical parameter training data;   train a geographical parameter classifier as a function of geographical parameter training data, and   identify a second alimentary provider as a function of geographical parameter training data and the first alimentary provider.   
     
     
         10 . The system of  claim 9 , wherein the geographical parameter training data correlates a first zip code of the first alimentary provider and a second zip code of the second alimentary provider. 
     
     
         11 . A method of outputting a second alimentary provider, the method including:
 inputting, by a computing device, a request for an alimentary combination from a user;   generating, by the computing device, a plurality of first alimentary providers based on at least a type of cuisine including a dieting method;   determining, by the computing device, the alimentary combination is not available at the plurality of first alimentary providers;   classifying, by the computing device, a plurality of alimentary providers;   computing, by the computing device, an alimentary combination score for a plurality of second alimentary combinations prepared by the plurality of alimentary providers as a function of a machine-learning process, wherein each of the plurality of second alimentary combinations includes at least a replacement for the alimentary combination; and   selecting, by the computing device, a second alimentary provider from the plurality of alimentary providers as a function of the alimentary combination score; and   outputting, by the computing device, the second alimentary provider to the user.   
     
     
         12 . The method of  claim 11 , wherein the dieting method includes a restricted medical diet. 
     
     
         13 . The method of  claim 11 , wherein classifying, by the computing device, the plurality of alimentary providers further includes utilizing a machine-learning process to generate an alimentary provider classifier. 
     
     
         14 . The method of  claim 13 , wherein utilizing the machine-learning process to generate the alimentary provider classifier includes:
 receiving alimentary provider training data;   training the alimentary provider classifier as a function of the alimentary provider training data; and   outputting the plurality of alimentary providers as a function of the alimentary provider classifier and the request for the alimentary combination.   
     
     
         15 . The method of  claim 11 , wherein computing the alimentary combination score as a function of the machine-learning process further includes training a machine-learning model with training data correlating an alimentary combination score to alimentary combinations. 
     
     
         16 . The method of  claim 11 , wherein generating the plurality of first alimentary providers further includes filtering the plurality of first alimentary providers as a function of user preferences. 
     
     
         17 . The method of  claim 16 , wherein the user preferences includes at least a selection of a delivery time. 
     
     
         18 . The method of  claim 11 , wherein the computing device is further configured to:
 train an alimentary combination classifier as a function of second alimentary combination training data; and   identify the second alimentary combination as a function of the alimentary combination classifier and a requested alimentary combination.   
     
     
         19 . The method of  claim 11 , wherein the computing device is further configured to:
 receive a geographical parameter of a first alimentary provider of the plurality of first alimentary providers;   receive geographical parameter training data;   train a geographical parameter classifier as a function of geographical parameter training data, and   identify a second alimentary provider as a function of geographical parameter training data and the first alimentary provider.   
     
     
         20 . The method of  claim 19 , wherein the geographical parameter training data correlates a first zip code of the first alimentary provider and a second zip code of the second alimentary provider.