Methods and systems for an artificial intelligence support network for vibrant constituional guidance

• US 10,559,386 B1
• Filed: 04/02/2019
• Issued: 02/11/2020
• Est. Priority Date: 04/02/2019
• Status: Active Grant

• Current Assignee: KPN Innovations, LLC
• Original Assignee: KPN Innovations, LLC
• Inventors: Neumann, Kenneth
• Primary Examiner(s): Tomaszewski, Michael
• Assistant Examiner(s): Muqueeth, Mohmad
• Application Number:
  US16/372,562
• Time in Patent Office: 315 Days
• CPC Class Codes: G06F 16/906 Clustering; Classification
• CPC Class Codes: G06N 20/10 using kernel methods, e.g. support vector machines [SVM]
• CPC Class Codes: G06N 20/20 Ensemble learning
• CPC Class Codes: G06N 3/045 Combinations of networks
• CPC Class Codes: G06N 3/08 Learning methods
• CPC Class Codes: G06N 5/01 Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound
• CPC Class Codes: G06N 5/02 Knowledge representation; Symbolic representation
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks
• CPC Class Codes: G16B 20/00 ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
• CPC Class Codes: G16B 40/20 Supervised data analysis
• CPC Class Codes: G16B 50/20 Heterogeneous data integration
• CPC Class Codes: G16B 50/30 Data warehousing; Computing architectures
• CPC Class Codes: G16H 10/60 for patient-specific data, e.g. for electronic patient records
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/70 for mining of medical data, e.g. analysing previous cases of other patients
• CPC Class Codes: Y02A 90/10 Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Abstract

A system for an artificial intelligence support network for vibrant constitutional guidance includes a diagnostic engine operating on at least a server and 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 system includes at least an advisor module configured to receive at least a request for an advisory input, generate at least an advisory output using the at least a request for an advisory input and at least a diagnostic output, select at least an informed advisor as a function of the at least a request for an advisory input, and transmit the at least an advisor output to the at least a selected informed advisor.

Claims

1. A system for an artificial intelligence support network for vibrant constitutional guidance, the system comprising:

at least a server;

at least a diagnostic engine including a prognostic label learner machine learning process and an ameliorative process label learner machine learning process that both operate on the diagnostic engine, wherein the diagnostic engine is designed and configured to;

receive a first training data set including a plurality of first data entries, each first data entry of the plurality of first data entries including at least an element of physiological state data and at least a correlated first prognostic label;

receive a second training data set including a plurality of second data entries, each second data entry of the plurality of second data entries including at least a second prognostic label and at least a correlated ameliorative process label; and

receive at least a biological extraction from a user;

wherein the prognostic label learner is designed and configured to;

generate at least a prognostic output by executing a lazy learning algorithm as a function of the first training set and the at least a biological extraction;

wherein the ameliorative process label learner is designed and configured to;

generate at least an ameliorative output by executing a supervised machine learning algorithm as a function of the second training set and the at least a prognostic output;

wherein the diagnostic module is designed and configured to generate a diagnostic output including the at least a prognostic output and the at least an ameliorative output; and

an advisory module designed and configured to;

receive at least a request for an advisory input;

generate at least an advisory output using the at least a request for an advisory input and the at least a diagnostic output;

select at least an informed advisor client device as a function of the at least a request for an advisory input, wherein the advisory module is configured to select the at least an informed advisor client device using a user-requested category of at least an informed advisor; and

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

2. The system of claim 1, wherein the at least a biological extraction further comprises a physically extracted sample.

3. The system of claim 1, wherein the diagnostic engine is further designed and configured to generate at least a request for an advisory input as a function of the diagnostic output.

4. The system of claim 1, wherein the advisory module is configured to receive the at least a request for an advisory input from a user client device.

5. The system of claim 1, wherein the advisory module is configured to receive the at least a request for an advisory input from an advisory client device.

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

7. The system of claim 1, wherein the advisory module is configured to select the at least an informed advisor client device by matching the at least a request for an advisory input to a category of at least an informed advisor.

8. The system of claim 1, wherein the advisory module is configured to select the at least an informed advisor client device as a function of the at least a diagnostic output.

9. The system of claim 1, wherein the advisory output further comprises a specialized instruction set.

11. The method of claim 9, wherein receiving the at least a biological extraction from a user further comprises receiving a physically extracted sample.

12. The method of claim 9, wherein the advisory module receives the at least a request for an advisory input from the diagnostic output generated by the diagnostic engine.

13. The method of claim 9, wherein the advisory module receives the at least a request for an advisory input generated from a user client device.

14. The method of claim 9, wherein the advisory module receives the at least a request for an advisory input generated from an advisory client device.

15. The method of claim 9, wherein generating the at least an advisory output further comprises generating an advisor instruction set as the function of the diagnostic output.

16. The method of claim 9, wherein selecting the at least an informed advisor client device further comprises matching the at least a request for an advisory input to a category of at least an informed advisor.

17. The method of claim 9, wherein selecting the at least an informed advisor client device as a function of the at least a request for an advisory input further comprises selecting the at least an informed client device as a function of the at least a diagnostic output.

18. The method of claim 9, wherein transmitting the at least an advisor output further comprises transmitting a specialized instruction set.

10. A method of an artificial intelligence support network for vibrant constitutional guidance, the method comprising:

receiving by a diagnostic engine including a prognostic label learner machine learning process and an ameliorative process label learner machine learning process that both operate on the diagnostic engine, the diagnostic engine operating on at least a server;

at least a biological extraction from a user;

a first training data set including a plurality of first data entries, each first data entry of the plurality of first data entries including at least an element of physiological state data and at least a correlated first prognostic label; and

a second training data set including a plurality of second data entries, each second data entry of the plurality of second data entries including at least a second prognostic label and at least a correlated ameliorative process label;

generating, by the prognostic label learner, at least a prognostic output by executing a lazy learning algorithm as a function of the first training set and the at least a biological extraction;

generating, by the ameliorative process label learner, at least an ameliorative output by executing a supervised machine learning algorithm as a function of the second training set and the at least a prognostic output;

generating by the diagnostic engine, a diagnostic output including the at least a prognostic output and the at least an ameliorative output;

receiving by an advisory module operating on the at least a server, at least a request for an advisory input;

generating at least an advisory output using the at least a request for advisory input and the at least a diagnostic output;

selecting at least an informed advisor client device as a function of the at least a request for an advisory input, wherein selecting the at least an informed advisor client device further comprises selecting the at least an informed advisor client device using a user-requested category of at least an informed advisor; and

transmitting the at least an advisor output to the at least an informed advisor client device.