Artificial intelligence advisory systems and methods for behavioral pattern matching and language generation

1. An artificial intelligence system for behavioral pattern matching and language generation, the system comprising:

at least a server;

a behavior modification module operating on the at least a server, wherein the behavior modification module is designed and configured to:

receive at least a request for a behavior modification;

receive at least an expert quality; and

generate a behavior modification model as a function of the at least a request for behavior modification;

an artificial intelligence advisor operating on the at least a server, wherein the artificial intelligence advisor comprises at least an expert module, wherein the artificial intelligence advisor is configured to:

receive at least a user input from a user client device;

generate at least a textual output using the behavior modification model and the at least a user input;

generate an expert list as a function of the at least an expert quality and the at least a request for a behavior modification; and

transmit the at least a textual output to the user client device; and

a consultation initiator operating on the at least a server, wherein the consultation initiator is configured to:

generate a consultation module as a function of the textual output, wherein generating the consultation module comprises training the consultation module using a consultation training set;

detect a consultation event as a function of the consultation module and the textual output;

generate at least a query using the at least a user input as a function of a language processing module;

determine that the at least a query includes a conversational language query;

generate a conversational response using the conversational language query; and

select at least an expert from the expert list as a function of the consultation event.

2. The artificial intelligence system of claim 1 , wherein the at least a user input further comprises a textual input.

3. The artificial intelligence system of claim 1 , wherein the at least user input further comprises at least an element of metadata.

4. The artificial intelligence system of claim 1 , wherein the input analysis module further comprises a language processing module configured to map the at least a user input to the at least a query.

5. The artificial intelligence system of claim 1 , wherein the processing module is further configured to:

retrieve at least a datum from a default response database using the conversational language query; and

generate the conversational response using the at least a datum.

6. The artificial intelligence system of claim 1 , wherein the processing module further includes a user communication learner configured to generate the at least a conversational response using the conversational language query.

7. The artificial intelligence system of claim 1 , wherein the processing module is further configured to:

determine that the at least a query includes an informational query; and

generate an informational response using the informational query.

8. The artificial intelligence system of claim 7 , wherein the processing module is further configured to:

retrieve at least a datum from the behavior modification model using the informational query; and

generate the informational response using the at least a datum.

9. An artificial intelligence method of behavioral pattern matching and language generation, the method comprising:

receiving, by at least a server, at least a request for a behavior modification;

generating, by the at least a server, a behavior modification model as a function of the at least a request for behavior modification;

receiving, by the at least a server, at least an expert quality;

generating, by the at least a server, an expert list as a function of the at least an expert quality and the at least a request for a behavior modification

receiving, by the at least a server, at least a user input from a user client device;

generating, by the at least a server, at least a textual output using the behavior modification model and the at least a user input;

transmitting, by the at least a server, the at least a textual output to the user client device;

generating, by the at least a server, a consultation module as a function of the textual output;

detecting, by the at least a server, a consultation event as a function of the consultation module and the textual output;

generating at least a query using the at least a user input;

determining that the at least a query includes a conversational language query;

generating a conversational response using the conversational language query; and

selecting, by the at least a server, at least an expert from the expert list as a function of the consultation event.

10. The method of claim 9 , wherein the at least a user input further comprises a textual input.

11. The method of claim 9 , wherein the at least user input further comprises at least an element of metadata.

12. The method of claim 9 , further comprising mapping, using a language processing module, the at least a user input to the at least a query.

13. The method of claim 9 further comprising:

retrieving at least a datum from a default response database using the conversational language query; and

generating the conversational response using the at least a datum.

14. The method of claim 9 further comprising generating the at least a conversational response using a user communication learner.

15. The method of claim 9 , further comprising:

determining that the at least a query includes an informational query; and generating an informational response using the informational query.

16. The method of claim 15 further comprising:

retrieving at least a datum from the behavior modification model using the informational query; and

generating the informational response using the at least a datum.