Artificial intelligence based document processor

1. A document processing and automation system, the system comprising:

one or more processors; and

a non-transitory data storage comprising processor-executable instructions that cause the one or more processors to:

receive a request,

the request comprising information related to a process, and

the process to be automatically executed;

extract the information,

the extracted information used to facilitate automatic execution of the process,

the extracted information being from at least two documents,

the two documents having different formats and the two documents are associated with the process, and

the information being extracted based on a domain model;

identify an intent associated with the information,

the intent used for identifying the process, and

the intent being determined based on:

a textual processing technique, and

a natural language processing (NLP) technique;

obtain one or more required fields,

the required fields used for the automatic execution of the process from the at least two documents;

determine if one or more discrepancies exist in the required fields,

the determining based on comparing one or more values contained in the required fields;

build an internal master document based on validating the one or more required fields,

the internal master document enabling automatic execution of the process, and

the building an internal master document performed when no discrepancies are detected; and

determine a reason for the discrepancy based on one or more of:

the intent,

the domain model, and

the at least two documents;

receive data from an external knowledge base,

resolve the discrepancy based on the received data from the external knowledge base; and

transform the required fields into the internal master document configured for automatically executing the process.

2. The document processing system of claim 1 , the processor-executable instructions for extracting the information from the request include instructions that cause the processor to:

parse and tokenize textual content of the request; and

extract at least one document pertaining to the process from one or more of the request and the external knowledge base.

3. The document processing system of claim 2 , the processor-executable instructions for extracting the information from the request include instructions that cause the processor to:

extract the documents from the external knowledge base based on the intent.

4. The document processing system of claim 3 , the processor-executable instructions for extracting the documents comprise instructions that cause the processor to:

employ trained document classifiers for the extraction of the documents from the external knowledge sources.

5. The document processing system of claim 3 , the processor-executable instructions for identifying an intent comprise instructions that cause the processor to:

compare the information from the request with domain-specific terminology included in the domain model, wherein the domain model is customized to the process.

6. The document processing system of claim 1 , wherein the domain model includes the required fields to be employed in the process, domain-specific terminology, definitions of the fields and types of the fields.

7. The document processing system of claim 1 , wherein instructions for building the internal master document comprise further instructions that cause the processor to:

concatenate the required fields extracted from the at least two documents wherein the required fields include one or more name-value pairs.

8. The document processing system of claim 1 , wherein instructions for building the internal master document comprise further instructions that cause the processor to:

determine a format of the internal master document based on the process to be automatically executed.

9. The document processing system of claim 1 , comprising further instructions that cause the processor to:

enable display of the required fields from the internal master document in one or more output graphical user interfaces (GUIs) in user-editable formats.

10. The document processing system of claim 9 , the processor-executable instructions for resolving the discrepancy comprise instructions that cause the processor to:

receive user edits to at least one of the required fields;

upload the user edits to the external knowledge base; and

provide the user edits as training data to the document processing system.

11. A document processing method comprising:

receiving a request including an intent pertaining to an automated execution of a billing task, the request includes at least one document required for the automated execution of the task;

determining the intent of the request by:

parsing and tokenizing the request and the at least one document;

identifying from the tokens, data indicative of the intent for the automated execution of the task;

obtaining additional pertinent information required for the automated execution of the task, the additional information being obtained from an external knowledge base using a billing domain model, the billing domain model including terms related to medical billing domain;

identifying at least one discrepancy in the document and the additional pertinent information based on process rules;

automatically resolving the discrepancy using at least the billing domain model, and

if the discrepancy cannot be automatically resolved,

manually resolving the discrepancy;

generating an internal master document that collates information relevant to the task from the document, the billing domain model and the external knowledge base;

providing the internal master document for review and approval to a user; and

enabling the automatic execution of the task upon the user review and approval of the internal master document.

12. The method of claim 11 , wherein the automatic execution of the task further comprises:

generating another request for the automatic execution of the task in accordance with the process rules and based at least on the internal master document.

13. The method of claim 11 , wherein generating the internal master document further comprises:

generating the internal master document as a text file including the collated information.

14. The method of claim 11 , wherein providing the internal master document for review and approval to the user further comprises:

providing, via an output graphical user interface (GUI), portions of the internal master document for the user review and approval.

15. The method of claim 14 , wherein providing the portions of the internal master document for the user review and approval further comprises:

providing, a respective switch for each of the portions, wherein a state of the switch is automatically toggled upon the approval of the portion by the user.

16. The method of claim 11 , wherein the billing domain model is based on neural networks.

17. A non-transitory storage medium comprising machine-readable instructions that cause at least one processor to:

receive a request with information related to a process to be automatically executed;

extract the information for the automatic execution of the process,

the information being extracted from at least two documents of different document formats and associated with the process, and

the information being extracted based on a domain model;

identify an intent conveyed within the information,

the intent enabling identification of the process, and

the intent being determined based at least on textual processing techniques and natural language processing (NLP);

obtain one or more required fields for automatic execution of the process from the at least two documents;

determine if one or more discrepancies exist in the required fields based on a comparison of values contained in the required fields;

build an internal master document based on validation of the one or more required fields, and the internal master document enabling automatic execution of the process if no discrepancies are detected;

resolve the discrepancy if at least one discrepancy is detected;

generate an internal master document with at least one false positive value;

present the internal master document to a user for review and approval; and

generate a report based on the user's review of the internal master document.

18. The non-transitory storage medium of claim 17 , wherein the machine-readable instructions for generating the report further comprise instructions that cause at least one processor to:

receive an erroneous response in the user's review of the internal master document; and

generate a report for further training of the user based on a comparison of the erroneous user response with responses from other users to the false positive value.

19. The non-transitory storage medium of claim 17 , wherein the machine-readable instructions for generating the report further comprise instructions that cause at least one processor to:

receive an erroneous response in the user's review of the internal master document; and

generate a report for further changes to the internal master document based on a comparison of the erroneous user response with responses from other users to the false positive value.

20. The non-transitory storage medium of claim 17 , wherein the machine-readable instructions for generating the internal master document further comprise instructions that cause at least one processor to:

include the extracted information in the internal master document; and

generate the internal master document as a text file.