Artificial intelligence systems and methods for vibrant constitutional guidance

1. An artificial intelligence advisory system for vibrant constitutional guidance, the artificial intelligence advisory system comprising:

at least a server;

a diagnostic engine operating on the at least a server, wherein the diagnostic engine is configured to:

receive at least a biological extraction from a user; and

generate a diagnostic output based on the at least a biological extraction, the diagnostic output including a plurality of prognostic labels and at least an ameliorative process label;

a plan generation module operating on the at least a server, the plan generation module designed and configured to generate, based on the diagnostic output, a comprehensive instruction set associated with the user, wherein the comprehensive instruction set includes at least a current prognostic descriptor and at least an ameliorative process descriptor, wherein generating the comprehensive instruction set further includes:

adding a prognostic category label to the comprehensive instruction set, wherein adding the prognostic category label further comprises:

generating a query using a first prognostic label of the plurality of prognostic labels and a second prognostic label of the plurality of prognostic labels;

querying a prognostic label classification database using the query;

receiving, as a result of the query, a first category of symptoms relating to the first prognostic label and a second category of symptoms relating to the second prognostic label;

determining, using the first category of symptoms and the second category of symptoms, that the second prognostic label and the third prognostic label belong to a shared category;

adding a category label associated with a shared category to the comprehensive instruction set; and

adding a first prognostic narrative language element and a second prognostic narrative language element to the comprehensive instruction set, wherein adding the first prognostic narrative language element and the second prognostic narrative language element further comprises:

determining a first element of narrative language including a phrase associated with the first prognostic label, wherein determining further comprises detecting, within a corpus of documents received from at least an expert and using a first hidden Markov model, a relationship between the phrase associated with the first prognostic label and the first prognostic label;

determining a second element of narrative language including a phrase associated with the second prognostic label wherein determining further comprises detecting, within the corpus of documents and using a second hidden Markov model, a relationship between the phrase associated with the second prognostic label and the second prognostic label; and

adding the first element of narrative language and the second element of narrative language to the comprehensive instruction set;

a client-interface module, the client-interface module designed and configured to transmit the comprehensive instruction set to at least a user client device associated with the user; and

at least an advisory module, the at least an advisory module designed and configured to:

generate at least an advisory output as a function of the comprehensive instruction set; and

transmit the advisory output to at least an advisor client device.

2. The artificial intelligence advisory system of claim 1 , wherein the plan generation module is further configured to:

determine that a first prognostic label of the at least a prognostic label is a duplicate of a second prognostic label of the at least a prognostic label; and

eliminate the first prognostic label.

3. The artificial intelligence advisory system of claim 1 , wherein the plan generation module is further configured to:

determine that a first prognostic label of the at least a prognostic label and a second prognostic label of the at least a prognostic label belong to a shared category; and

add a category label associated with the shared category to the comprehensive instruction set.

4. The artificial intelligence advisory system of claim 1 , wherein the plan generation module is further configured to:

determine that a first ameliorative label of the at least an ameliorative label is a duplicate of a second ameliorative label of the at least an ameliorative label; and

eliminate the first ameliorative label.

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

determine that a first ameliorative label of the at least an ameliorative label and a second ameliorative label of the at least an ameliorative label belong to a shared category; and

add a category label associated with the shared category to the comprehensive instruction set.

6. The artificial intelligence advisory system of claim 1 , wherein the plan generation module is further configured to:

receive at least an element of user data; and

filter the diagnostic output using the at least an element of user data.

7. The artificial intelligence advisory system of claim 6 , wherein the at least an element of user data further comprises a constitutional restriction.

8. The artificial intelligence advisory system of claim 6 , wherein the at least an element of user data further comprises a user preference.

9. The artificial intelligence advisory system of claim 1 , wherein the client-interface module is further configured to receive at least an element of user data from the user client device.

10. The artificial intelligence advisory system of claim 1 , wherein the client-interface module is further configured to receive at least an element of user data from the advisor client device.

11. The artificial intelligence advisory system of claim 1 , wherein the advisory module is configured to generate an advisor instruction set as a function of the diagnostic output.

12. The artificial intelligence advisory system of claim 1 , wherein the advisory module is configured to receive at least an advisory input from the advisor client device.

13. The artificial intelligence advisory system of claim 1 , wherein the advisory module further comprises an artificial intelligence advisor configured to perform a user textual conversation with the user client device.

14. The artificial intelligence advisory system of claim 13 , wherein the artificial intelligence advisor further comprises a consultation initiator configured to:

detect a consultation event in the user textual conversation; and

initiate a consultation with an informed advisor as a function of the consultation event.

15. A method of vibrant constitutional guidance using artificial intelligence, the method comprising:

receiving, by a diagnostic engine operating on at least a server, at least a biological extraction from a user;

generating, by the diagnostic engine, a diagnostic output based on the at least a biological extraction, the diagnostic output including at least a prognostic label and at least an ameliorative process label;

generating, by a plan generation module operating on the at least a server, and based on the diagnostic output, a comprehensive instruction set associated with the user, the comprehensive instruction set including at least an ameliorative process descriptor and at least an ameliorative process descriptor, wherein generating the comprehensive instruction set further includes:

adding a prognostic category label to the comprehensive instruction set, wherein adding the prognostic category label further comprises:

generating a query using a first prognostic label of the plurality of prognostic labels and a second prognostic label of the plurality of prognostic labels;

querying a prognostic label classification database using the query;

receiving, as a result of the query, a first category of symptoms relating to the first prognostic label and a second category of symptoms relating to the second prognostic label;

determining, using the first category of symptoms and the second category of symptoms, that the second prognostic label and the third prognostic label belong to a shared category;

adding a category label associated with a shared category to the comprehensive instruction set; and

adding a first prognostic narrative language element and a second prognostic narrative language element to the comprehensive instruction set, wherein adding the first prognostic narrative language element and the second prognostic narrative language element further comprises:

determining a first element of narrative language including a phrase associated with the first prognostic label, wherein determining further comprises detecting, within a corpus of documents received from at least an expert and using a first hidden Markov model, a relationship between the phrase associated with the first prognostic label and the first prognostic label;

determining a second element of narrative language including a phrase associated with the second prognostic label wherein determining further comprises detecting, within the corpus of documents and using a second hidden Markov model, a relationship between the phrase associated with the second prognostic label and the second prognostic label; and

adding the first element of narrative language and the second element of narrative language to the comprehensive instruction set;

transmitting, by a client-interface module operating on the at least a server, the comprehensive instruction set to at least a user client device associated with the user; and

generating, by at least an advisory module, at least an advisory output as a function of the comprehensive instruction set.

16. The method of claim 15 , wherein generating the comprehensive instruction set further comprises:

determining that a first prognostic label of the at least a prognostic label is a duplicate of a second prognostic label of the at least a prognostic label; and

eliminating the first prognostic label.

17. The method of claim 15 , wherein generating the comprehensive instruction set further comprises:

determining that a first prognostic label of the at least a prognostic label and a second prognostic label of the at least a prognostic label belong to a shared category; and

adding a category label associated with the shared category to the comprehensive instruction set.

18. The method of claim 15 , wherein generating the comprehensive instruction set further comprises:

determining that a first ameliorative label of the at least an ameliorative label is a duplicate of a second ameliorative label of the at least an ameliorative label; and

eliminating the first ameliorative label.

19. The method of claim 15 , wherein generating the comprehensive instruction set further comprises:

determining that a first ameliorative label of the at least an ameliorative label and a second ameliorative label of the at least an ameliorative label belong to a shared category; and

adding a category label associated with the shared category to the comprehensive instruction set.

20. The method of claim 15 , wherein generating the comprehensive instruction set further comprises:

receiving at least an element of user data; and

filtering the diagnostic output using the at least an element of user data.