Apparatus and method for answering a user communication
An apparatus and method for answering a user communication, wherein the apparatus includes at least a processor, a computer-readable storage medium communicatively connected to the at least a processor, wherein the computer-readable storage medium contains instructions configuring the at least processor to receive a user communication comprising contextual data, classify the user communication to a contact profile, classify the contextual data to an action table within a repository, and answer the user communication.
1. An apparatus for answering a user communication, wherein the apparatus comprises:
at least a processor;
a computer-readable storage medium communicatively connected to the at least a processor, wherein the computer-readable storage medium contains instructions configuring the at least processor to:
receive a user communication comprising contextual data;
classify the user communication to a contact profile, wherein classifying the user communication to a contact profile comprises:
preprocessing the user communication;
extracting, using a feature extraction algorithm, features from the contextual data;
training a contact classifier with historical data correlating contextual data to features of a plurality of contact profiles;
inputting the preprocessed user communication into the contact classifier, and
outputting, by the contact classifier, the contact profile;
classify the contextual data to an action table within a repository;
identify a third party by training an identification machine learning model with training data comprising examples of user communications correlated to third parties and outputting the third party; and
answer the user communication by generating an outbound communication, wherein answering the user communication further comprises transmitting a request for an input from the third party in relation to the user communication.
2. The apparatus of claim 1 , wherein:
the action table comprises an action rule comprising a protocol for the least a processor to execute based on the contextual data; and
the computer-readable storage medium contains instructions configuring the at least a processor to execute the protocol.
3. The apparatus of claim 1 , wherein classifying the contextual data to an action table further comprises ranking contextual data based on a priority.
4. The apparatus of claim 3 , wherein ranking the contextual data based on priority comprises:
training a machine learning model with training data correlating contextual data and contact profiles to urgency levels;
inputting the contextual data into the machine learning model; and
outputting an urgency level related to the priority using the machine learning model.
5. The apparatus of claim 1 , wherein answering the user communication comprises selecting a machine learning model from a collection of machine learning models to perform an action based on the action table.
6. The apparatus of claim 1 , wherein generating the outbound communication comprises generating an executable code.
7. The apparatus of claim 1 , wherein generating the outbound communication comprises generating a receipt indicating a status of a requested action of the user communication.
8. A method for answering a user communication, wherein the method comprises:
receiving, by a computing device, a user communication comprising contextual data;
classifying, by the computing device, the user communication to a contact profile, wherein classifying the user communication to a contact profile comprises:
preprocessing the user communication;
extracting, using a feature extraction algorithm, features from the contextual data;
training a contact classifier with historical data correlating contextual data to features of a plurality of contact profiles;
inputting the preprocessed user communication into the contact classifier, and outputting, by the contact classifier, the contact profile;
classifying, by the computing device, the contextual data to an action table within a repository;
identifying, by the computing device, a third party by training an identification machine learning model with training data comprising examples of user communications correlated to third parties and outputting the third party; and
answering, by the computing device, the user communication by generating an outbound communication, wherein answering the user communication further comprises transmitting a request for an input from the third party in relation to the user communication.
9. The method of claim 8 , wherein:
the action table comprises an action rule comprising a protocol for the computing device to execute based on the contextual data; and
to the method comprises, using the computing device, executing the protocol.
10. The method of claim 8 , wherein classifying the contextual data to an action table further comprises ranking contextual data based on a priority.
11. The method of claim 10 , wherein ranking the contextual data based on priority comprises:
training a machine learning model with training data correlating contextual data and contact profiles to urgency levels;
inputting the contextual data into the machine learning model; and
outputting an urgency level related to the priority using the machine learning model.
12. The method of claim 8 , wherein answering the user communication comprises selecting a machine learning model from a collection of machine learning models to perform an action based on the action table.
13. The method of claim 8 , wherein generating the outbound communication comprises generating an executable code.
14. The method of claim 8 , wherein generating the outbound communication comprises generating a receipt indicating a status of a requested action of the user communication.