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1. WO2020222908 - METHODS AND SYSTEMS FOR CLASSIFICATION USING EXPERT DATA

Note: Text based on automatic Optical Character Recognition processes. Please use the PDF version for legal matters

[ EN ]

What is claimed is:

1. A system for classification using expert data, the system comprising:

a processor;

an expert submission processing module operating on the processor, the expert submission processing module designed and configured to receive submission relating constitutional data to ameliorative recommendation data, wherein the expert submission is associated with an expert;

a model generator operating on the processor, the model generator designed and configured to convert the expert submission into training data;

an expert learner operating on the processor, wherein the expert learner is configured to

generate, using a machine learning process, an ameliorative output as a function of the training data and a constitutional inquiry; and

a client-interface module operating on the processor, the client-interface module designed and configured to:

receive, from a user client device, the constitutional inquiry; and

transmit, to the user client device, the ameliorative output.

2. The system of claim 1, wherein the expert submission processing module further comprises an expert user interface designed and configured to:

configure an expert client device to display one or more data entry fields prompting an expert to input expert submission data; and

receive, from the expert client device, at least a datum entered in the one or more data entry fields by the expert.

3. The system of claim 2, wherein the expert submission processing module is further

configured to:

acquire an identity of the expert;

query a listing of experts using the identity; and

validate the expert based on a result of the query.

4. The system of claim 1, wherein the expert submission processing module is further

configured to:

receive at least a textual submission; and

extract the expert submission from the at least a textual submission.

5. The system of claim 4, wherein the receiving the at least a textual submission further comprises receiving the at least a textual submission from an expert client device.

6. The system of claim 4, wherein receiving the at least a textual submission further comprises: acquiring an identity of at least an expert;

identifying, in a document datastore, a textual submission authored by the at least an expert, using the identity of the at least an expert; and

retrieving the textual submission from the document datastore.

7. The system of claim 1, wherein the expert submission processing module further comprises a language processing module designed and configured to convert the at least a textual submission into expert submission.

8. The system of claim 1, wherein the model generator is configured to generate the

ameliorative output by:

ranking a plurality of ameliorative outputs described in the ameliorative data; and selecting the ameliorative output from the plurality of ameliorative outputs as a function of the ranking.

9. The system of claim 1, wherein the expert learner is configured to generate at least an expert model as a function of the training data.

10. The system of claim 1, wherein the expert learner is configured to generate the at least an ameliorative output using a lazy learning protocol as a function of the training data and the constitutional inquiry.

11. A method of classification using expert data, the method comprising:

receiving, by a processor, an expert submission relating constitutional data to ameliorative recommendation data and a constitutional inquiry, wherein the expert submission is associated with an expert;

converting, by the processor, the expert submission into training data;

generating, by the processor, using machine learning, an ameliorative output as a function of the constitutional inquiry and the training data; and

transmitting, by the at least a server and to a user client device, the ameliorative output.

12. The method of claim 11, wherein receiving the expert submission further comprises:

configuring an expert client device to display one or more data entry fields prompting an expert to input expert submission data; and

receiving, from the expert client device, at least a datum entered in the one or more data entry fields by the expert.

13. The system of claim 12 further comprising:

acquiring an identity of the expert;

querying a listing of experts using the identity; and

validating the expert based on a result of the query.

14. The method of claim 11, wherein receiving the expert submission further comprises:

receiving at least a textual submission; and

extracting the expert submission from the at least a textual submission.

15. The method of claim 14, wherein the receiving the at least a textual submission further

comprises receiving the at least a textual submission from an expert client device.

16. The method of claim 14, wherein receiving the at least a textual submission further

comprises:

acquiring an identity of at least an expert;

identifying, in a document datastore, a textual submission authored by the at least an expert, using the identity of the at least an expert; and

retrieving the textual submission from the document datastore.

17. The method of claim 14, further comprising converting, using a language processing module, the at least a textual submission into expert submission.

18. The method of claim 11, wherein generating the ameliorative output further comprises:

ranking a plurality of ameliorative outputs described in the ameliorative data; and

selecting the ameliorative output from the plurality of ameliorative outputs as a function of the ranking.

19. The method of claim 11, wherein generating the at least an ameliorative output further

comprises generating at least an expert model as a function of the training data.

20. The method of claim 11 wherein generating the at least an ameliorative output further

comprises generating the at least an ameliorative output using a lazy learning protocol as a function of the training data and the constitutional inquiry.