Methods and systems for calculating nutritional requirements in a display interface

11823,785

16/919532

July 02, 2020

A system for calculating nutritional requirements in a display interface the system including a computing device configured to initiate a display interface within the computing device; retrieve an input, including an input credential, and wherein the input relates a representative profile to a nutritional requirement; generate a training set using the input; receive a meal option; calculate using a machine-learning process, a nutritional requirement of the meal option using the training set; determine the nutritional requirement of the meal option as a function of the machine-learning process; and display the nutritional requirement within the display interface.

KPN INNOVATIONS, LLC. (Lakewood, CO, US)

KPN INNOVATIONS, LLC. (Lakewood, CO, US)

1. A system for calculating nutritional requirements in a display interface, the system comprising a computing device, the computing device configured to: initiate, a display interface within the computing device; retrieve, an input from an expert, wherein the input comprises an input credential, wherein the input credential is an entry identifying qualifications of the expert that created the input; generate an index classifier, wherein the index classifier comprises a machine-learning model trained by training data comprising a plurality of user physiological data records and a plurality of user cohort labels configured to receive biological extractions as inputs and outputs web search indices, the index classifier further comprising a classification algorithm clustering a plurality of user physiological data records to a plurality of user cohort labels; search, by the web index classifier, wherein a result of the search further comprise at least a classifier wherein the classifier is trained using an index training data set comprising the results of the search wherein the results of the search further comprises the plurality of user cohort labels, wherein the classifier is further configured to: receive at least the result of the search; correlate the at least result of the search to at least an element of physiological data to the plurality of user cohort labels; and classify the plurality of user cohort labels to a representative profile as a function of the results of the search; select a representative profile as a function of the classifier, wherein selecting further comprises: classifying the user to a selected user cohort label of the plurality of user cohort labels; and selecting the representative profile as a function of the selected user cohort label; receive, by a computing device, a user input, wherein the computing device is further configured to identify a food preference as a function of the user input wherein the food preference is further configured to specify the user's allergies, wherein identifying the food preference further comprises: identifying a pattern of eating of the user as a function of the food preference; and displaying a nutritional requirement as a function of the food preference; classify the representative profile to the nutritional requirement as a function of the input; generate, a training set using the input, wherein generating the training set further comprises: receiving the input identifying the representative profile from a plurality of representative profiles; identifying a nutritional requirement contained within the identified representative profile; and generating the training set wherein the training set contains a plurality of nutritional requirements correlated to the identified representative profile; receive a meal option; display a description of the meal option in the display interface; calculate the nutritional requirement of the meal option, wherein calculating comprises: training a machine-learning process as a function of the training set, wherein training the machine-learning process comprises determining a plurality of distances associated with the plurality of nutritional requirements as a function of correlations between the plurality of nutritional requirements and the identified representative profile; and calculating the nutritional requirement, wherein the machine learning model inputs the representative profile and the meal option and outputs the nutritional requirement according the plurality of distances associated with the plurality of nutritional requirements; and display a graphical representation of the nutritional requirement from a group of two or more graphical representations within the display interface with the description of the meal option as a function of the calculated nutritional requirement, wherein the graphical representation of the nutritional requirement comprises at least a character score.

2. The system of claim 1 , wherein the representative profile further comprises a demographic label.

3. The system of claim 1 , wherein the computing device is further configured to: generate a query using the input credential; and authenticate the input credential as a function of the query, wherein the authentication comprises determining that an input credential is valid and has not been revoked or has not expired.

4. The system of claim 1 , wherein the computing device is further configured to: determine an input credential status as a function of an expert credential list; discard the input as a function of the input credential status; and select a subsequent input including a subsequent input credential.

5. The system of claim 4 , wherein the input credential status contains a temporal element.

6. The system of claim 1 , wherein the meal option is selected from a list containing a plurality of meal options within a specified geographical area.

7. The system of claim 1 , wherein the computing device is further configured to: receive a portion size associated with the meal option; and update the graphical representation of the nutritional requirement to another from the group of two or more graphical representations based on the portion size.

8. The system of claim 1 , wherein identifying the food preference further specifies at least a food that the user cannot consume due to at least an intolerance.

9. The system of claim 1 , wherein the nutritional requirement is displayed as a numerical output including a range of values.

10. The system of claim 1 , wherein the computing device is further configured to generate the plurality of user cohort labels using a feature learning algorithm.

11. A method of calculating nutritional requirements in a display interface the method comprising: initiating by a computing device, a display interface within the computing device; retrieving by the computing device, an input from an expert, wherein the input comprises an input credential, wherein the input credential is an entry identifying a qualification of the expert that created the input; generating, by the computing device, an index classifier, wherein the index classifier comprises a machine-learning model trained by training data comprising a plurality of user physiological data records and a plurality of user cohort labels configured to receive biological extractions as inputs and outputs web search indices, the index classifier further comprising a classification algorithm clustering a plurality of user physiological data records to a plurality of user cohort labels; searching, by the web index classifier, wherein a result of the search further comprise at least a classifier wherein the classifier is trained using an index training data set comprising the results of the search wherein the results of the search further comprises the plurality of user cohort labels, wherein the classifier is further configured to: receive at least the result of the search; correlate the at least result of the search to at least an element of physiological data to the plurality of user cohort labels; and classify the plurality of user cohort labels to a representative profile as a function of the results of the search; selecting by computing device, a representative profile as a function of the classifier, wherein selecting further comprises: classifying the user to a selected user cohort label of the plurality of user cohort labels; and selecting the representative profile as a function of the selected user cohort label; receiving, by a computing device, a user input, wherein the computing device is further configured to identify a food preference as a function of the user input wherein the food preference is further configured to specify the user's allergies, wherein identifying the food preference further comprises: identifying a pattern of eating of the user as a function of the food preference; and displaying a nutritional requirement as a function of the food preference; classifying, by the computing device, the representative profile to the nutritional requirement as a function of the input; generating by the computing device, a training set using the input, wherein generating the training set further comprises: receiving the input identifying the representative profile from a plurality of representative profiles; identifying a nutritional requirement contained within the identified representative profile; and generating the training set wherein the training set contains a plurality of nutritional requirements correlated to the identified representative profile; receiving by the computing device, a meal option; displaying, in the display interface, a description of the meal option in the display interface; calculating by the computing device, the nutritional requirement of the meal option, wherein calculating comprises training a machine-learning process as a function of the training set, wherein training the machine-learning process comprises determining a plurality of distances associated with the plurality of nutritional requirements as a function of correlations between the plurality of nutritional requirements and the identified representative profile and wherein the machine-learning process inputs the nutritional requirement and the meal option and outputs the nutritional requirement according the plurality of distances associated with the plurality of nutritional requirements; and displaying by the computing device, a graphical representation of the nutritional requirement from a group of two or more graphical representations within the display interface with the description of the meal option as a function of the calculated nutritional requirement, wherein the graphical representation of the nutritional requirement comprises at least a character score.

12. The method of claim 11 , wherein the representative profile further comprises a demographic label.

13. The method of claim 11 , wherein retrieving the input further comprises: generating a query using the input credential; and authenticating the input credential as a function of the query, wherein authenticating comprises determining that the input credential is valid and has not been revoked or has not expired.

14. The method of claim 11 , wherein retrieving the input further comprises: determining the input credential status as a function of an expert credential list; discarding the input as a function of the input credential status; and selecting a subsequent input including a subsequent input credential.

15. The method of claim 14 , wherein the input credential status contains a temporal element.

16. The method of claim 11 , wherein the meal option is selected from a list containing a plurality of meal options within a specified geographical area.

17. The method of claim 11 , further comprising: receiving a portion size associated with the meal option; and updating the graphical representation of the nutritional requirement to another from the group of two or more graphical representations based on the portion size.

18. The method of claim 11 , wherein displaying the nutritional wherein identifying the food preference further specifies at least a food that the user cannot consume due to at least an intolerance.

19. The method of claim 11 , wherein the nutritional requirement is displayed as a numerical output including a range of values.

20. The method of claim 11 further comprising generating the plurality of user cohort labels using a feature learning algorithm.

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