Methods and systems for making a coverage determination

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, wherein:
• the user biological extraction comprises physiological data comprising physiological state data;
• and
• the recordation comprises a user's tendency for developing at least a disease as a function of the physiological state;
• calculating, by the computing device, a user effective age utilizing a user chronological age and the user's tendency for developing the at least a disease;
• determining, by the computing device and using a trained behavior machine-learning model, a user behavior pattern, wherein determining the user behavior pattern comprises:
• displaying, using a graphical user interface (GUI) and as a function of the user biological extraction, one or more questionnaires to a user;
• receiving, by the computing device, a response to the one or more questionnaires from the user;
• and
• processing the response using the trained behavior machine-learning model to output the behavior pattern as a numerical model inference result;
• identifying by the computing device a user danger profile as a function of the received response;
• producing, by the computing device, a user coverage profile wherein the user coverage profile 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 from a plurality of coverage machine-learning models, wherein each coverage machine-learning model of the plurality of coverage machine-learning models is optimized for a specific coverage type, as a function of the coverage request, wherein the method is further configured to:
• extract textual data from the coverage request;
• parse the textual data into a plurality of tokens;
• generate positive and negative statistical correlations for the plurality of tokens;
• classify the plurality of tokens into at least a coverage category as a function of a category classifier and the statistical correlations;
• and
• select the coverage machine-learning model as a function of the at least a coverage category;
• generating, by the computing device, the selected coverage machine-learning model using an artificial neural network, wherein the coverage machine-learning model comprises an input layer of nodes, at least one intermediate layer, and an output layer of nodes, wherein a connection between the nodes is created, wherein the connections between the nodes in adjacent layers are adjusted to produce desired values at the output nodes, wherein the machine-learning model is iteratively 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;
• inputting into the selected coverage machine-learning model, using the computing device, the user coverage profile and the coverage request, wherein inputting the user coverage profile and the coverage request into the selected coverage machine-learning model causes the selected coverage machine-learning model to:
• combine a structured set of normalized feature values of the user coverage profile with features extracted from the coverage request to generate a unified internal feature representation;
• propagate the unified internal feature representation through a plurality of computational layers of the selected coverage machine-learning model to produce an intermediate numerical inference state;
• and
• store the intermediate numerical inference state for use in generating model outputs;
• and
• outputting, by the computing device and from the selected coverage machine-learning model, a plurality of coverage options, wherein outputting the plurality of coverage options further comprises:
• generating a loss function utilizing the plurality of coverage options, wherein the loss function comprises a plurality of coverage variables;
• displaying each coverage variable of the plurality of coverage as a user-adjustable slider on the GUI;
• receiving user input that adjusts each slider of the plurality of coverage variables on the GUI;
• minimizing the loss function in response to receiving an adjustment of one or more sliders of each slider of the plurality of coverage variables;
• and
• selecting a coverage plan from the plurality of coverage options as a function of minimizing the loss function.