| CPC G06Q 10/0631 (2013.01) | 20 Claims |
|
1. An apparatus for predicting a resource growth pattern, the apparatus comprising:
at least a processor;
a memory connected to the at least a processor, the memory containing instructions configuring the at least a processor to:
receive a first datum from a user device, wherein the first datum describes a first activity pattern of the user device;
receive a second datum from a client device, wherein the second datum describes a second activity pattern of the user device;
retrieve a third datum, wherein the third datum describes a prioritization value of the first activity pattern relative to the second activity pattern;
classify the third datum to a label selected from a plurality of labels based on the prioritization value, wherein classifying the third datum further comprises:
generating a representation of the third datum in a first space having a first number of dimensions, wherein generating the representation comprises using a first machine-learning process comprising a representation machine-learning model and further comprising:
receiving representation training data, wherein the representation training data comprises:
applying an input layer of nodes comprising a plurality of user data, one or more intermediate layers of nodes, and an output layer of nodes comprising a plurality of first space data;
adjusting one or more connections and one or more weights between nodes in adjacent layers of the representation machine-learning model;
detecting correlations between the output layer of nodes and the input layer of nodes;
training, iteratively, the representation machine-learning model using the representation training data, wherein training the representation machine-learning model includes retraining the representation machine-learning model using a simulated annealing algorithm, the detected correlations between the output layer of nodes and the input layer of nodes, and user inputs indicating a sub-optimal performance received by the at least processor by performing an auditing process configured to compare outputs of the representation machine-learning model to a convergence test to reconfigure a network of nodes; and
generating the representation of the third datum using the trained representation machine-learning model; and
projecting the representation of the third datum generated by the trained representation machine-learning model to a second space having a second number of dimensions, wherein projecting the representation comprises using a second machine-learning process and results in a projected representation of a second number of dimensions; and
generate an interface query data structure, wherein the interface query data structure configures a remote display device to:
display an input field;
receive at least a user-input datum into the input field, wherein the user-input datum describes updating the prioritization value; and
display a resource growth pattern including displaying the representation based on the user-input datum.
|