Methods and systems for using artificial intelligence to select a compatible element

1 . A system for using artificial intelligence to select a compatible element, the system comprising at least a server, wherein the at least a server is designed and configured to:

receive training data, wherein receiving the training data further comprises receiving a first training set comprising a plurality of data entries, each first data entry of the plurality of first data entries comprising at least an element of physiological state data and at least a correlated compatible label;

receive a biological extraction from a user, wherein the biological extraction relates to a medical condition of the user;

filter elements of the training data using a natural language processing algorithm to generate a plurality of filtered training data sets each containing a plurality of data entries categorizing training data elements to categories of biological extractions;

select at least one filtered training data set of the plurality of filtered training data sets as a function of the medical condition of the user using the natural language processing algorithm;

receive a datum of user activity data, wherein the datum of user activity comprises a list of activities and compatible elements associated with the user;

update the selected and filtered at least one training data set with previous user interactions, wherein the previous user interactions comprise a past purchase history of the user;

train a machine-learning model iteratively using the updated, selected and filtered at least one training data set, wherein the machine-learning model is a neural network,

wherein training the machine-learning model further comprises:

selecting a portion of the updated, selected and filtered at least one training data set as a function of a user cohort; and

adjusting weights of relationships between the at least an element of physiological state data and the at least a correlated compatible label using the selected portion of the updated, selected and filtered at least one training data set;

generating a multidimensional feature vector representation of the physiological state data and propagating the multidimensional feature vector representation through at least one hidden layer of the neural network to compute an output activation corresponding to the correlated compatible label;

generate, using the trained machine-learning model, a compatible element index, wherein the compatible element index is a value assigned to a compatible element based on previous user interactions;

select, using the trained machine-learning model, at least a compatible element as a function of the compatible element index generated using the neural network, the biological extraction, and the selected portion of the updated, selected and filtered at least one training data set; and

transmit the at least a compatible element to a user client device, wherein the user client device comprises a display in communication with the at least a server.

2 . The system of claim 1 , wherein selecting the at least a compatible element comprises:

selecting a first compatible element of the at least a compatible element using the trained machine-learning model; and

determining a second compatible element of the at least a compatible element as a function of the compatible element index.

3 . The system of claim 1 , wherein selecting the at least a compatible element comprises:

selecting a first compatible element of the at least a compatible element using the machine-learning model; and

determining a second compatible element of the at least a compatible element as a function of a physiological index value.

4 . The system of claim 1 , wherein the at least a server is further configured to generate, using a second machine learning model, a diagnostic output as a function of the training data and the biological extraction, wherein the diagnostic output comprises at least a prognostic label and at least an ameliorative process label.

5 . The system of claim 1 , wherein the at least a server is further configured to retrieve the compatible element index from a compatible index value database.

6 . The system of claim 5 , wherein retrieving the compatible element index from the compatible index value database comprises retrieving the compatible element index from a beauty table of the compatible index value database.

7 . The system of claim 5 , wherein retrieving the compatible element index from the compatible index value database comprises retrieving the compatible element index from a grocery and gourmet foods table of the compatible index value database.

8 . The system of claim 1 , wherein the at least a server is further configured to transmit the at least a compatible element to a user client device.

9 . The system of claim 1 , wherein the user activity data comprises the past purchase history of the user.

10 . The system of claim 1 , wherein:

the user activity data comprises a past browsing history of the user; and

the compatible element index is a function of the past browsing history of the user.

11 . A method for using artificial intelligence to select a compatible element, the method comprising:

receiving, by at least a server, training data, wherein receiving the training data further comprises receiving a first training set including a plurality of first data entries, each first data entry of the plurality of first data entries comprising at least an element of physiological state data and at least a correlated compatible label;

receiving, by the at least a server, a biological extraction from a user, wherein the biological extraction relates to a medical condition of the user;

filtering, by the at least a server, elements of the training data using a natural language processing algorithm to generate a plurality of filtered training data sets each containing a plurality of data entries categorizing training data elements to categories of biological extractions;

selecting, by the at least a server, at least one filtered training data set of the plurality of filtered training data sets as a function of the medical condition of the user using the natural language processing algorithm;

receiving, by the at least a server, a datum of user activity data, wherein the datum of user activity comprises a list of activities and compatible elements associated with the user;

updating, by the at least a server, the selected and filtered at least one training data set with previous user interactions, wherein the previous user interactions comprise a past purchase history of the user;

training, by the at least a server, a machine-learning model iteratively using the updated, selected and filtered at least one training data set, wherein the machine-learning model is a neural network, wherein training the machine-learning model further comprises:

selecting a portion of the updated, selected and filtered at least one training data set as a function of a user cohort;

adjusting weights of relationships between the at least an element of physiological state data and the at least a correlated compatible label using the selected portion of the updated, selected and filtered at least one training data set; and

generating a multidimensional feature vector representation of the physiological state data and propagating the multidimensional feature vector representation through at least one hidden layer of the neural network to compute an output activation corresponding to the correlated compatible label;

generating, using the trained machine-learning model, a compatible element index,

wherein the compatible element index is a value assigned to a compatible element based on previous user interactions;

selecting, by the at least a server using the trained machine-learning model, at least a compatible element as a function of the compatible element index generated using the neural network, the biological extraction, and the selected portion of the updated, selected and filtered at least one training data set; and

transmit the at least a compatible element to a user client device wherein the user client device comprises a display in communication with the at least a server.

12 . The method of claim 11 , wherein selecting the at least a compatible element comprises:

selecting a first compatible element of the at least a compatible element using the machine-learning model; and

determining a second compatible element of the at least a compatible element as a function of the compatible element index.

13 . The method of claim 11 , wherein selecting the at least a compatible element comprises:

selecting a first compatible element of the at least a compatible element using the machine-learning model; and

determining a second compatible element of the at least a compatible element as a function of a physiological index value.

14 . The method of claim 11 , further comprising generating, by the at least a server using a second machine learning model, a diagnostic output as a function of the training data and the biological extraction, wherein the diagnostic output comprises at least a prognostic label and at least an ameliorative process label.

15 . The method of claim 11 , further comprising retrieving, by the at least a server, the compatible element index from a compatible index value database.

16 . The method of claim 15 , wherein retrieving the compatible element index from the compatible index value database comprises retrieving the compatible element index from a beauty table of the compatible index value database.

17 . The method of claim 15 , wherein retrieving the compatible element index from the compatible index value database comprises retrieving the compatible element index from a grocery and gourmet foods table of the compatible index value database.

18 . The method of claim 11 , further comprising transmitting, by the at least a server, the at least a compatible element to a user client device.

19 . The method of claim 11 , wherein the user activity data comprises the past purchase history of the user.

20 . The method of claim 11 , wherein:

the user activity data comprises a past browsing history of the user; and

the compatible element index is a function of the past browsing history of the user.