Apparatus and methods for determining a resource distribution

1. An apparatus for determining a resource distribution, the apparatus comprising:

a processor;

a memory connected to the processor, the memory containing instructions configuring the 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 from the memory, wherein the third datum describes a prioritization value of the first activity pattern relative to the second activity pattern for adjusting the first activity pattern to match a threshold value, wherein the first activity pattern comprises at least a user income-generating activity, wherein the prioritization value comprises data indicating an amount of time allocated to the at least a user income-generating activity, wherein the time allocated correlates to a motivation level of the user;

classify, using a machine-learning model including a classifier, the first datum, the second datum and the third datum to a label selected from a plurality of labels based on the prioritization value, wherein classifying further comprises:

training the machine-learning model using the classifier with a training data set describing an optimized activity pattern of the user device; and

predicting adjustments to at least the first activity pattern based on the trained machine-learning model to match the first activity pattern with the optimized activity pattern;

modify a sequence of activities in the second activity pattern based on classification of the second datum to the label; and

generate an interface data structure including an input field, wherein the interface data structure configures a remote display device to:

display the input field;

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

display the resource distribution including displaying an updated prioritization value based on the user-input datum.

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

retrieving data describing attributes of a user;

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

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

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

configuring the remote display device to display the vector.

3. The apparatus of claim 2 , wherein determining the vector from 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 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 a form element input into the input field.

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

6. The apparatus of claim 1 , further comprising:

classifying 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 sequence of activities in 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 distribution.

8. The apparatus of claim 5 , further comprising:

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

classifying 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 the user-input datum comprising:

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

generating a divergence value based on the classification; and

displaying the divergence value hierarchically based on magnitude of divergence.

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

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

aggregating 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 determining a resource distribution, 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;

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;

retrieving, by the computing device, a third datum from the memory, wherein the third datum describes a prioritization value of the first activity pattern relative to the second activity pattern for adjusting the first activity pattern to match a threshold value, wherein the first activity pattern comprises at least a user income-generating activity, wherein the prioritization value comprises data indicating an amount of time allocated to the at least a user income-generating activity, wherein the time allocated correlates to a motivation level of the user;

classifying, by the computing device, using a machine-learning model including a classifier, the first datum, the second datum and the third datum to a label selected from a plurality of labels based on the prioritization value, wherein classifying further comprises:

training, by the computing device, the machine-learning model using the classifier with a training data set describing an optimized activity pattern of the user device; and

predicting, by the computing device, adjustments to at least the first activity pattern based on the trained machine-learning model to match the first activity pattern with the optimized activity pattern;

modifying, by the computing device, a sequence of activities in the second activity pattern based on classification of the second datum to the label; and

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

display the input field;

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

display the resource distribution including displaying an updated prioritization value based on the user-input datum.

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

retrieving data describing attributes of a user from a database;

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

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

determining a vector from the representation of 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 vector from 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 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 a form element input into the input field.

15. The method of claim 11 , further comprising generating 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 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 sequence of activities in 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 distribution.

18. The method of claim 15 , further comprising:

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

classifying 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 the third datum;

generating a divergence value based on the classification; and

displaying the 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 some labels based on their respective proximity to a minimal output type and a maximum output type;

aggregating 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.