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

1 . A system for providing a consultation, the system comprising:

a processor; and

a memory containing instructions, the instructions configuring the processor to:

extract one or more elements from a user input to detect a user query, wherein the user query comprises at least one of a conversational query and an informational query;

train a machine learning model, wherein training the machine learning model comprises:

receiving a consultation training data set, wherein the consultation training data set comprises a plurality of example word data and a plurality of example phrase data correlated to example consultation event categories;

training, iteratively, the machine learning model using the consultation training data set by continuously performing an unsupervised machine-learning to detect correlations between words from a corpus that a supervised learning module has associated with consultation events and other words from the corpus; and

updating the machine learning model to add the other words as additional keywords that trigger the consultation events based on the detected correlations;

detect a consultation event reference by matching one or more elements within the user query to a consultation action utilizing a consultation initiator, wherein the consultation initiator is generated as a function of the trained machine learning model, wherein the consultation initiator is configured to automatically place an emergency call for the detected consultation event;

identify the consultation event reference as a function of the trained machine learning model and the user query;

determine a prognostic schedule as a function of the consultation event reference;

display the prognostic schedule to a first user;

receive a prognostic schedule feedback datum; and

schedule a first consultation event as a function of the prognostic schedule feedback datum.

2 . The system of claim 1 , wherein the memory contains instructions configuring the processor to display the prognostic schedule to the first user in a natural language format.

3 . The system of claim 1 , wherein the memory contains instructions configuring the processor to:

generate a behavior modification prompt;

display the behavior modification prompt to the first user;

receive a behavior modification input; and

determine the prognostic schedule as a function of the behavior modification input.

4 . The system of claim 1 , wherein the memory contains instructions configuring the processor to display a notification to a second user as a function of detection of the consultation event reference.

5 . The system of claim 1 , wherein scheduling the consultation event comprises scheduling an automatically recurring consultation event.

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

identify a treatment lapse datum; and

schedule a second consultation event as a function of the treatment lapse datum.

7 . The system of claim 6 , wherein the memory contains instructions configuring the processor to display a notification to a second user as a function of the treatment lapse datum.

8 . The system of claim 1 , wherein the first consultation event comprises an asynchronous consultation event.

9 . The system of claim 8 , wherein the memory contains instructions configuring the processor to:

receive an asynchronous consultation event datum;

transmit the asynchronous consultation event datum to a remote device operated by a medical professional; and

receive from the remote device an asynchronous consultation event authorization datum.

10 . The system of claim 8 , wherein the memory contains instructions configuring the processor to:

receive an asynchronous consultation event datum; and

using a language model, determine an asynchronous consultation event authorization datum as a function of the asynchronous consultation event datum.

11 . A method of providing a consultation, the method comprising:

using at least a processor, extracting one or more elements from a user input to detect a user query, wherein the user query comprises at least one of a conversational query and an informational query;

using the at least a processor, training a machine learning model, wherein training the machine learning model comprises:

receiving a consultation training data set, wherein the consultation training data set comprises a plurality of example word data and a plurality of example phrase data correlated to example consultation event categories;

training, iteratively, the machine learning model using the consultation training data set by continuously performing an unsupervised machine-learning to detect correlations between words from a corpus that a supervised learning module has associated with consultation events and other words from the corpus; and

updating the machine learning model to add the other words as additional keywords that trigger the consultation events based on the detected correlations;

using the at least a processor, detecting a consultation event reference by matching one or more elements within the user query to a consultation action utilizing a consultation initiator, wherein the consultation initiator is generated as a function of the trained machine learning model, wherein the consultation initiator is configured to automatically place an emergency call for the detected consultation event;

identifying the consultation event reference as a function of the trained machine learning model and the user query;

using the at least a processor, determining a prognostic schedule as a function of the consultation event reference;

using the at least a processor, displaying the prognostic schedule to a first user;

using the at least a processor, receiving a prognostic schedule feedback datum; and

using the at least a processor, schedule a first consultation event as a function of the prognostic schedule feedback datum.

12 . The method of claim 11 , wherein the prognostic schedule is displayed to the first user in a natural language format.

13 . The method of claim 11 , wherein the method further comprises:

using the at least a processor, generating a behavior modification prompt;

using the at least a processor, displaying the behavior modification prompt to the first user;

using the at least a processor, receiving a behavior modification input; and

using the at least a processor, determining the prognostic schedule as a function of the behavior modification input.

14 . The method of claim 11 , wherein the method further comprises, using the at least a processor, displaying a notification to a second user as a function of detection of the consultation event reference.

15 . The method of claim 11 , wherein scheduling the consultation event comprises scheduling an automatically recurring consultation event.

16 . The method of claim 11 , wherein the method further comprises:

using the at least a processor, identifying a treatment lapse datum; and

using the at least a processor, scheduling a second consultation event as a function of the treatment lapse datum.

17 . The method of claim 16 , wherein the method further comprises, using the at least a processor, displaying a notification to a second user as a function of the treatment lapse datum.

18 . The method of claim 11 , wherein the first consultation event comprises an asynchronous consultation event.

19 . The method of claim 18 , wherein the method further comprises:

using the at least a processor, receiving an asynchronous consultation event datum;

using the at least a processor, transmitting the asynchronous consultation event datum to a remote device operated by a medical professional; and

using the at least a processor, receiving from the remote device an asynchronous consultation event authorization datum.

20 . The method of claim 18 , wherein the method further comprises:

using the at least a processor, receiving an asynchronous consultation event datum; and

using the at least a processor and a language model, determining an asynchronous consultation event authorization datum as a function of the asynchronous consultation event datum.