Systems and methods for determining contextual rules

1. An apparatus for determining a contextual rule, the apparatus comprising:

at least a processor; and

a memory communicatively connected to the at least processor, the memory containing instructions configuring the at least processor to:

receiving a user datum, wherein the user datum comprises a plurality of data elements describing at least one planned interaction;

identify a context datum by processing the received user datum using a plurality of processing models, wherein identifying the context datum comprises:

determine at least two identical data elements of the plurality of data elements using the plurality of processing model; and

identify the context datum as a function of the at least two identical data elements of the plurality of data elements;

identify a first interaction datum;

determine a first interaction feature as a function of the first interaction datum, using an interaction machine learning model;

determine a first reaction datum as a function of the first interaction datum, using a reaction machine learning model;

determine a contextual rule as a function of the context datum, the first interaction feature, and the first reaction datum; and

determine a visual element of a visual element data structure as a function of the data structure, wherein the visual element data structure comprises:

a plurality of rules governing timing of a display and formatting of the visual element; and

a degree of confidence related to the contextual rule; and

display the visual element to a user by formatting the visual element based on a rule of the plurality of rules related to the degree of confidence.

2. The apparatus of claim 1 , wherein identifying a first interaction datum comprises:

identifying a first interaction sequence; and

identifying a first interaction datum as a single communication from a single communicator in the first interaction sequence.

3. The apparatus of claim 1 , wherein the first interaction datum comprises a recording of human speech.

4. The apparatus of claim 1 , wherein the interaction machine learning model is configured to categorize inputs into discrete categories.

5. The apparatus of claim 1 , wherein the reaction machine learning model is configured to output a datum on a continuous scale.

6. The apparatus of claim 1 , wherein the memory contains instructions configuring the at least processor to:

identify a second interaction datum;

determine a second interaction feature as a function of the second interaction datum, using the interaction machine learning model;

determine a second reaction datum as a function of the second interaction datum, using the reaction machine learning model; and

determine a contextual rule as a function of the context datum, the first interaction feature, the second interaction feature, the first reaction datum, and the second reaction datum.

7. The apparatus of claim 6 , wherein identifying a second interaction datum comprises:

identifying a second interaction sequence; and

identifying a second interaction datum as a single communication from a single communicator in the second interaction sequence.

8. The apparatus of claim 6 , wherein the first interaction datum and the second interaction datum each comprise a recording of human speech.

9. A method of determining a contextual rule, the method comprising:

using at least a processor, receiving a user datum comprising a plurality of data elements describing at least one planned interaction;

using at least a processor, identifying a context datum by processing the received user datum using a plurality of processing models, wherein identifying the context datum comprises:

determining at least two identical data elements of the plurality of data elements using the plurality of processing model; and

identifying the context datum as a function of the at least two identical data elements of the plurality of data elements;

using the at least a processor, identifying a first interaction datum;

using the at least a processor, determining a first interaction feature as a function of the first interaction datum, using an interaction machine learning model;

using the at least a processor, determining a first reaction datum as a function of the first interaction datum, using a reaction machine learning model;

using the at least a processor, determining a contextual rule as a function of the context datum, the first interaction feature, and the first reaction datum; and

using the at least a processor, determining a visual element of a visual element data structure as a function of the data structure, wherein the visual element data structure comprises:

a plurality of rules governing timing of a display and formatting of the visual element; and

a degree of confidence related to the contextual rule; and

using the at least a processor, displaying the visual element to a user by formatting the visual element based on a rule of the plurality of rules related to the degree of confidence.

10. The method of claim 9 , wherein identifying a first interaction datum comprises:

identifying a first interaction sequence; and

identifying a first interaction datum as a single communication from a single communicator in the first interaction sequence.

11. The method of claim 9 , wherein the first interaction datum comprises a recording of human speech.

12. The method of claim 9 , wherein the interaction machine learning model is configured to categorize inputs into discrete categories.

13. The method of claim 9 , wherein the reaction machine learning model is configured to output a datum on a continuous scale.

14. The method of claim 9 , further comprising:

identifying a second interaction datum;

determining a second interaction feature as a function of the second interaction datum, using the interaction machine learning model;

determining a second reaction datum as a function of the second interaction datum, using the reaction machine learning model; and

determining a contextual rule as a function of the context datum, the first interaction feature, the second interaction feature, the first reaction datum, and the second reaction datum.

15. The method of claim 14 , wherein identifying a second interaction datum comprises:

identifying a second interaction sequence; and

identifying a second interaction datum as a single communication from a single communicator in the second interaction sequence.

16. The method of claim 14 , wherein the first interaction datum and the second interaction datum each comprise a recording of human speech.