APPARATUS AND METHODS FOR DETERMINING A RESOURCE GROWTH PATTERN

1 . An apparatus for predicting a resource growth pattern, the apparatus comprising:

at least a processor;

a memory connected to the 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 identifying a first election to spend resources;

receive a second datum from a client device, wherein the second datum describes a second activity pattern of the user device identifying a second election to spend resources;

retrieve a third datum, wherein the third datum describes a desired scarce resource allocation of the first election to spend resources relative to the second election to spend resources;

classify the third datum to a label selected from a plurality of labels based on a prioritization value, wherein classifying further comprises:

generating a representation of the third datum in a first space having a first number of dimensions, wherein generating at least the representation comprises using a first machine-learning process; and

projecting the representation of the third datum to a second space having a second number of dimensions, wherein projecting at least 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 data structure, wherein the interface data structure configures a remote display device to display the resource growth pattern including displaying the representation based on a user-input datum, the first datum, and the second datum.

2 . The apparatus of claim 1 , wherein generating the interface data structure further comprises:

retrieving data describing attributes of a user from a database communicatively connected to the processor;

displaying a representation of at least a first label and a second label selected from a plurality of labels in a grid; and

generating the interface data structure based on the data describing attributes of the user, wherein generating an interface query data structure further comprises:

determining at least a vector from the representation of the at least a first label to the second label; and

configuring the remote display device to display the at least a vector.

3 . The apparatus of claim 2 , wherein determining the at least a vector from the at least a first label to the second label further comprises generating the vector including an angle value and a distance value, wherein the angle value and the distance value describe at least a divergence value between the first datum and the second datum.

4 . The apparatus of claim 1 , wherein generating the third datum further comprises retrieving data describing current preferences of the user device between a minimum value and a maximum value from a database communicatively connected to the processor, wherein retrieving data further comprises receiving at least a form element input into an input field.

5 . The apparatus of claim 1 , further comprising generating at least an additional input field based on a divergence value that describes divergence between the first datum and the second datum.

6 . The apparatus of claim 1 , further comprising:

classifying at least an instance of the first datum to the third datum;

determining a proximity of the at least an instance of the first datum to the third datum based on the first activity pattern; and

adjusting the third datum to reduce the proximity.

7 . The apparatus of claim 1 , further comprising:

classifying the second datum to the third datum, wherein classifying the second datum further comprises comparing the second datum to the third datum; and

determining a parity value based on comparison of the second datum to the third datum, wherein the parity value is included within the resource growth pattern.

8 . The apparatus of claim 5 , further comprising:

determining a pattern, wherein the pattern describes a user interaction;

classifying at least an element of the pattern to the divergence value; and

adjusting the pattern based on a magnitude of the divergence value.

9 . The apparatus of claim 1 , further configured to evaluate a user-input datum comprising:

classifying one or more new instances of a user-input datum to the third datum;

generating at least a divergence value based on the classification; and

displaying the at least a divergence value hierarchically based on magnitude of divergence.

10 . The apparatus of claim 1 , wherein classifying the at least a first datum to the label further comprises:

organizing at least some labels based on their respective proximity to a minimal output type and a maximum output type;

aggregating at least an instance of the first datum based on the classification; and

classifying aggregated first data to the label having a closest proximity to the maximum output type.

11 . A method for predicting a resource growth pattern, the method comprising:

receiving, by a computing device, a first datum from a user device, wherein the first datum describes a first activity pattern of the user device identifying a first election to spend resources;

receiving, by the computing device, a second datum from a client device, wherein the second datum describes a second activity pattern of the user device identifying a second election to spend resources;

receiving, by the computing device, a third datum from a database communicatively connected to the computing device, wherein the third datum describes a desired scarce resource allocation of the first election to spend resources relative to the second election to spend resources;

classifying, by the computing device, at least the third datum to a label selected from a plurality of labels based on a prioritization value, wherein classifying further comprises:

generating a representation of the third datum in a first space having a first number of dimensions, wherein generating at least the representation comprises using a first machine-learning process; and

projecting the representation of the third datum to a second space having a second number of dimensions, wherein projecting at least the representation comprises using a second machine-learning process and results in a projected representation of a second number of dimensions; and

generating, by the computing device, an interface query data structure including an input field, wherein the interface query data structure configures a remote display device to: display the input field, the first datum, and the second datum

receive at least a user-input datum into the input field, wherein the user-input datum describes updating the prioritization value; and

display the resource growth pattern including displaying the representation based on the user-input datum.

12 . The method of claim 11 , wherein generating the interface query data structure further comprises:

retrieving data describing attributes of a user from a database communicatively connected to the computing device;

displaying a representation of at least a first label and a second label selected from a plurality of labels in a grid;

generating the interface query data structure based on the data describing attributes of the user, wherein generating the interface query data structure further comprises:

determining at least a vector from the representation of at least the first label to the second label; and

configuring the remote display device to display the vector.

13 . The method of claim 12 , wherein determining the at least the vector from at least the first label to the second label further comprises generating the vector including an angle value and a distance value, wherein:

the angle value and the distance value describe at least a divergence value between the first datum and the second datum.

14 . The method of claim 11 , wherein generating the third datum further comprises:

retrieving data describing current preferences of the user device between a minimum value and a maximum value from a database communicatively connected to the computing device, wherein retrieving data further comprises receiving at least a form element input into the input field.

15 . The method of claim 11 , further comprising generating at least an additional input field based on a divergence value, which describes divergence between the first datum and the second datum.

16 . The method of claim 11 , further comprising:

classifying at least an instance of the first datum to the third datum;

determining a proximity of a respective first datum to the third datum based on the first activity pattern; and

adjusting the third datum to reduce the proximity.

17 . The method of claim 11 , further comprising:

classifying the second datum to the third datum, wherein classifying the second datum further comprises:

comparing the second datum to the third datum; and

determining a parity value based on comparison of the second datum to the third datum, wherein the parity value is included within the resource growth pattern.

18 . The method of claim 15 , further comprising:

determining a pattern, wherein the pattern describes user interaction with the database;

classifying at least an element of the pattern to the divergence value; and

adjusting the pattern based on a magnitude of the divergence value.

19 . The method of claim 11 , further configured to evaluate the user-input datum comprising:

classifying one or more new instances of the user-input datum to at least the third datum;

generating at least a divergence value based on the classification; and

displaying the at least a divergence value hierarchically based on magnitude of divergence.

20 . The method of claim 11 , wherein classifying the first datum to the label further comprises:

organizing at least some labels based on their respective proximity to a minimal output type and a maximum output type;

aggregating at least an instance of the first datum based on the classification; and

classifying aggregated first data to the label having a closest proximity to the maximum output type.