METHODS AND SYSTEMS FOR MAKING A COVERAGE DETERMINATION

1 . A system for making a coverage determination, the system comprising a computing device the computing device designed and configured to:

receive from a remote device a coverage

request; record a user biological extraction;

calculate a user effective age utilizing a user chronological age and the user biological extraction;

determine a user behavior pattern;

identify a user danger profile;

produce a user coverage profile wherein the user coverage profile further comprises the user biological extraction, the user effective age, the user behavior pattern, and the user danger profile;

select a coverage machine-learning model as a function of the coverage request;

generate the selected coverage machine-learning model wherein the machine-learning model is trained by training data, the training data correlating a plurality of biological extractions with a plurality of coverage options, and wherein the coverage machine-learning model utilizes a user coverage profile as an input and outputs a plurality of coverage options; and

output a plurality of coverage options as a function of generating the selected coverage machine-learning model.

2 . The system of claim 1 , wherein calculating the user effective age further comprises: calculating a positive effective age score, wherein calculating the positive effective age score

further comprises aggregating a telomer length factor, an endocrinal factor, and a histone variance factor;

calculating a negative effective age score, wherein calculating the negative effective age score further comprises aggregating the user behavior pattern to the user danger profile; and

adjusting a user chronological age to produce a user effective age utilizing the positive effective age score and the negative effective age score.

3 . The system of claim 1 , wherein determining the user behavior pattern further comprises:

generating a behavior machine-learning model utilizing behavior training data wherein the behavior machine-learning model utilizes a biological extraction as an input and outputs behavior patterns; and

calculating a behavior pattern output utilizing the behavior machine-learning model identifying the behavior pattern as a function of calculating the behavior output.

4 . The system of claim 1 , wherein identifying the user danger profile further comprises: generating a danger machine-learning model utilizing danger training data wherein the danger training data further comprises a plurality of data entries containing a plurality of biological extractions and a plurality of correlated danger profiles;

calculating a danger profile output utilizing a danger machine-learning model wherein the danger machine-learning model utilizes a biological extraction as an input and outputs danger profiles; and

selecting a danger profile as a function of generating the danger machine-learning model.

5 . The system of claim 1 , wherein producing the user coverage profile further comprises identifying a user stability profile and a user community profile.

6 . The system of claim 1 , wherein selecting the coverage model further comprises:

generating a classification algorithm wherein the classification algorithm utilizes coverage requests and user biological extractions as inputs and outputs coverage machine-learning models; and

selecting, using the classification algorithm, a coverage machine-learning model.

7 . The system of claim 1 , wherein selecting the coverage model further comprises: extracting from the coverage request a coverage category; and

selecting the coverage machine-learning model intended for the coverage category.

8 . The system of claim 1 , wherein outputting the plurality of coverage options further comprises:

generating a loss function utilizing the plurality of coverage options; minimizing the loss function; and

selecting a coverage plan from the plurality of coverage options as a function of minimizing the loss function.

9 . The system of claim 8 , wherein generating the loss function further comprises:

receiving from the remote device a coverage variable pertaining to the coverage request; and minimizing the loss function as a function of the plurality of coverage options and the coverage variable.

10 . (canceled)

11 . A method of making a coverage determination, the method comprising: receiving by a computing device a coverage request from a remote device; recording by the computing device a user biological extraction;

calculating by the computing device a user effective age utilizing a user chronological age and the user biological extraction;

determining by the computing device a user behavior pattern;

identifying by the computing device a user danger profile;

producing by the computing device a user coverage profile wherein the user coverage profile further comprises the user biological extraction, the user effective age, the user behavior pattern, and the user danger profile;

selecting by the computing device a coverage machine-learning model as a function of the coverage request;

generating by the computing device the selected coverage machine-learning model wherein the machine-learning model is trained by training data, the training data correlating a plurality of biological extractions with a plurality of coverage options, and wherein the coverage machine-learning model utilizes a user coverage profile as an input and outputs a plurality of coverage options; and

outputting by the computing device a plurality of coverage options as a function of generating the selected coverage machine-learning model.

12 . The method of claim 11 , wherein calculating the user effective age further comprises:

calculating a positive effective age score, wherein calculating the positive effective age score further comprises aggregating a telomer length factor, an endocrinal factor, and a histone variance factor;

calculating a negative effective age score, wherein calculating the negative effective age score further comprises aggregating the user behavior pattern to the user danger profile; and

adjusting a user chronological age to produce a user effective age utilizing the positive effective age score and the negative effective age score.

13 . The method of claim 11 , wherein determining the user behavior pattern further comprises:

generating a behavior machine-learning model utilizing behavior training data wherein the behavior training data contains a plurality of data entries containing a plurality of biological extractions and a plurality of correlated behavior patterns;

calculating a behavior pattern output utilizing the behavior machine-learning model wherein the behavior machine-learning model utilizes a biological extraction as an input and outputs behavior patterns; and

identifying the behavior pattern as a function of calculating the behavior output.

14 . The method of claim 11 , wherein identifying the user danger profile further comprises:

generating a danger machine-learning model utilizing danger training data wherein the danger training data further comprises a plurality of data entries containing a plurality of biological extractions and a plurality of correlated danger profiles;

calculating a danger profile output utilizing a danger machine-learning model wherein the danger machine-learning model utilizes a biological extraction as an input and outputs danger profiles; and

selecting a danger profile as a function of generating the danger machine-learning model.

15 . The method of claim 11 , wherein producing the user coverage profile further comprises identifying a user stability profile and a user community profile.

16 . The method of claim 11 , wherein selecting the coverage model further comprises:

generating a classification algorithm wherein the classification algorithm utilizes coverage requests and user biological extractions as inputs and outputs coverage machine-learning models; and

selecting, using the classification algorithm, a coverage machine-learning model.

17 . The method of claim 11 , wherein selecting the coverage model further comprises:

extracting from the coverage request a coverage category; and

selecting the coverage machine-learning model intended for the coverage category.

18 . The method of claim 11 , wherein outputting the plurality of coverage options further comprises:

generating a loss function utilizing the plurality of coverage options;

minimizing the loss function; and

selecting a coverage plan from the plurality of coverage options

as a function of minimizing the loss function.

19 . The method of claim 18 , wherein generating the loss function further comprises:

receiving from the remote device a coverage variable pertaining to the coverage request; and

minimizing the loss function as a function of the plurality of coverage options and the coverage variable.

20 . (canceled)