Patent ID: 12259873

DETAILED DESCRIPTION OF THE DISCLOSURE

Organizations maintain databases containing vital information to the organization's operations. Submissions from various entities may build these databases. These submissions may be in the form of documents. Documents may contain data and metadata. Entities may submit the documents to the organization by a variety of ways including ways that allow for extraction of the data and metadata from the documents to populate the databases. However, some entity submissions may not be compatible with the database.

In instances where a document submission to an organization by an entity is not compatible with the organization's database, the incompatibility may be manifest such that a processor is not able to extract data and metadata from the document or is not able to use extracted data and metadata to populate a database. The described incompatibility may be due to the format of the submitted document. For example, the document format might be that of a scanned image or a picture. The processor, for example, may need for the data to be in a textual form for data and metadata to be extractable for updating the database.

In one aspect of the current disclosure, described are systems and methods for digitizing a first format, such as a scan or a picture, into a digital format. A processor may further convert the document into another format thereby facilitating extractions of the document's data and metadata.

Another aspect of the current disclosure may include, for example, a collection of documents submitted to an organization over time. The organization may have a current system that accumulates submitted documents and archives them without extracting data and metadata. The organization may desire to implement a more advanced system where a processor extracts data and metadata from the documents and uses the data and metadata to populate a database. The submitted documents may be in a format that the processor does not recognize or cannot extra data and metadata to populate the database. In such instances, the current disclosure describes systems and methods for digitizing a first format, such as a scan or a picture, into a digital format. The processor may extract data and metadata from the digital format of the document or from a further reformat of the document.

A further aspect of the current disclosure includes utilizing a populated database, such as the one described herein, to train and implement a statistical model. The statistical model may, for example, utilize an artificial intelligence (AI) tool, such as a machine learning AI system or a deep learning AI system. The statistical model may be located on a computer server, for example, the computer server of an organization. The statistical model may make various predictions based on historical data and metadata, for example, from documents submitted by various entities.

For example, the processor running a statistical model may upload data and metadata from a document received from an entity. The processor running a statistical model may determine a document type. In an iterative way, classifying documents into distinct types may facilitate further training and utilizing the statistical model. For example, instead of just utilizing data from a specific entity or from every entity in developing a statistical model, classification allows the use of subsets of documents. The data and metadata from these documents may provide for an enhanced accuracy of the statistical model as the set of data used to train the model may be greater.

As another example, when receiving a request from an entity to complete a document, the processor running a statistical model may pre-populate all or portions of the fields in the requested document. The processor running a statistical model may provide a document to the requesting entity to verify pre-populated fields and to complete the fields that were not pre-populated.

The processor may ascertain the confidence level of the statistical model. For example, the processor may ascertain a pre-population confidence level. It may ascertain a post-population confidence level. A pre-population confidence level refers to a measure in the confidence of the statistical model before a field of a current document is pre-populated by using the statistical model. The processor may ascertain a pre-population confidence level, for example, by testing statistical model predictions against existing documents stored on a computer server used to generate the model. Achieving the pre-population confidence level may occur when predictions by the statistical model match documents stored on the computer. Such an assessment may lead to utilizing the statistical model to pre-populate a document when an entity makes a request for the document from the organization. Failing the pre-population confidence level may occur when predictions by the statistical model do not match documents stored on the computer. For example, a document submission by an entity may be subsequently received and used to update the database and statistical model. The additional data and metadata from the document may facilitate further training the model to bring it into compliance with a confidence level requirement. In another aspect, adjusting the statistical model may improve it and thereby achieve the pre-population confidence level.

A post-population confidence level refers to a measure in the confidence of the statistical model after the processor utilizing the statistical model pre-populates a field of a current document. The processor measures the accuracy of the pre-population of a document to a document completed by an entity. A confidence level represents less differences between the pre-population and completed document by the entity. Less differences lead to a higher confidence level. In reaction to differences, the processor reaches out to the entity to confirm that the change was intentional.

When the statistical model makes the error, the processor may keep count of the unsuccessful pre-populated field entry attempts. When the count exceeds a threshold, a post-population confidence level may fall out of compliance indicating a low confidence level in the statistical model. Dealt with further in the description are instances where an entity made the error.

If the post-population confidence level falls out of compliance, the processor may implement one of the following options. For example, the processor may add data and metadata from the received completed document to further train the model. The processor may use additional completed documents to extract data and metadata for the purposes of updating the statistical model. The additional data and metadata from the document may facilitate further training the model to bring it into compliance with a confidence level requirement. In another aspect, the processor or a programmer may adjust the statistical model may improve to make it achieve the pre-population confidence level.

An additional aspect of this disclosure includes error detection for submissions from an entity. When an entity requests a document, a processor may utilize a statistical model to provide a pre-populated field in the document provided to the entity. The entity may then check for the accuracy of the pre-populated fields and may add information to any of the other fields which were not pre-populated. The processor may compare the document sent to the entity to the document received. When there is a discrepancy in the pre-populated fields, as described previously, the processor may communicate the changed field or fields to the entity to confirm that the change was intentional. This action may give the entity an extrinsic way to check its work for potential errors.

When referring to data in this description, the reference may be to data from a document. The reference may be to metadata from a document. The reference may be to data and metadata from a document.

When referring to data and metadata in this description, the reference may be to both data and metadata from a document. The reference may be to either metadata or data from a document.

A further aspect of this disclosure includes using a statistical model to assist an organization in fraud detection. For example, when a submission of a document from an entity is received by an organization and the document is not compatible with the organization's database, the incompatibility may be manifest such that a processor is not able to extract data and metadata from the document or is not able to use extracted data and metadata to populate a database. The processor may digitize the document into a digital format. The processor may add data and metadata from the digitized document to a database on a computer server. In another aspect, the processor may convert the document from a digital format to another format, and then add the data and metadata to the database on the computer server.

Another aspect of this disclosure includes using a processor to build a statistical model from documents stored at a computer server. The statistical model references data, metadata, or data and metadata extracted from the documents and stored on a database at the computer server. In one aspect, the format of the documents is not compatible with the format of the database. The processor may digitize the document into a digital format. The processor may add data and metadata from the digitized document to a database on a computer server. In another aspect, the processor may convert the document from a digital format to another format, and then add the data and metadata to the database on the computer server.

By referencing the populated database, when an entity submits a document, a statistical model on a computer server may compare the submitted document with similar prior submissions. The processor may build the statistical model utilizing the data and metadata in a database found on a computer server. The processor may identify specific entity identifier information that facilitates locations of relevant data and metadata of potential relevance to the entity. The processor may use the entity identifier information to use a statistical model to determine expected inputs by the entity in the submitted document. When there is a difference between data and metadata in the submitted document from an entity and what the statistical model predicted the entity would enter, the processor may submit the document for further analysis. For example, the processor implements human review of the document. The processor may implement a specialized algorithm for assessing potential threats of fraud. Furthermore, the processor may enter the results of the human review or the specialized algorithm review into the database as data to assist in training and updating the statistical model to manage future document submissions by this entity and other entities.

Another aspect of fraud detection may be looking for anomalous patterns in an entity's behavior. The processor may utilize a statistical model to identify unusual behavior by an entity. Examples of anomalous behavior may include overly frequent submissions, submissions of a similar nature in a shorter than expected timeframe, changes in personal information such as identification numbers, name, phone numbers and email addresses. When crossing a threshold of concern, the processor flags the matter for further review, such as review by a human.

A further aspect of fraud detection may include designating an entity as a trusted entity upon presentation of proper credentials. When designating an entity as a trusted entity, the processor may look for potential errors instead of fraudulent submissions. The processor may provide the potential errors to the entity for confirmation.

The processor may relax fraud detection when the submission comes from a trusted device. For example, an entity might give its credentials to use the device. Therefore, the processor may view submissions from that device as safe. Instead, the processor will view anomalies as potential errors. The processor may provide the entity with the potential error for confirmation.

A further aspect of the disclosure includes automating a process for submitting compliance forms to a regulatory body. There may be instances where the organization needs to submit a received document in whole or in part to a regulatory body. When documents are not compatible or are poorly compatible with a computer server, obtaining data and metadata from the document for use in the submission of compliance forms may be difficult. In one aspect, the disclosure may include a processor for digitizing the document into a digital format. In a further aspect, the processor may extract data and metadata from the digital format for populating a compliance form. In a further aspect, the processor may use the digital format to populate a database. The processor may use the database as a source of information for populating a compliance form. In a further aspect, the processor may convert the document from a digital format into another format. The processor may then populate the database with data and metadata from this document format. The processor may utilize the updated database as a source of information for populating a compliance form.

The processor may utilize a statistical model on a computer server to assist in populating and submitting a compliance form. The processor may identify specific entity identifier information that facilitates locations of relevant data and metadata of potential relevance to the entity. The processor may use the entity identifier information to use a statistical model to determine expected inputs by the entity in the submitted document. When there is a difference between data and metadata in the submitted document from an entity and what the statistical model predicted the entity would enter, the processor may submit the document back to the entity for confirmation of the entity's submission. Once clarified, the processor may prepare a compliance form for submission to a regulatory body.

An entity may be an individual such as a person or a group of people. An entity may be an establishment such as a corporation. The corporation may be a for-profit corporation or a non-profit corporation. An entity may be a government organization, school, college, university, hospital, or the like.

An organization may be a corporation. The corporation may be a for-profit corporation or a non-profit corporation. The entity and the organization are distinct and separate from one another.

The processor may use the statistical model to determine an appropriate compliance form based on the data and metadata found in a submitted document from an entity. The statistical model based on data and metadata found in a database on a computer server may auto-populate some or all the fields on a compliance form based on submitted documents.

The statistical model on computer servers described in this specification may operate as a form of artificial intelligence (AI). One type of AI is a machine learning AI system. Another type of AI is a deep learning AI system.

One may program a machine learning AI system to identify patterns in data sets and make decisions based on the patterns. One typically uses machine learning algorithms to predict future behavior based on historical patterns. By providing new and updated data, machine learning AI systems may improve their predictions. However, even though machine learning AI systems may improve their predictions, such systems only detect patterns based on how they are pre-programmed to review provided data. Machine learning systems do not adapt on their own to look at data in unusual ways or flag patterns in the data they were not pre-programmed to search for.

On the other hand, deep learning AI systems adapt when exposed to different patterns of data. Deep learning AI systems may uncover features or patterns in data that they were never specifically programmed to find or search for. Deep learning AI systems are typically based on neural networks. A neural network takes an input and passes the input through a network of neurons—called layers—and provides an output. The more layers of neurons which are part of the network, the “deeper” the network. A neural network learns from outputs flagged as erroneous and “adapts” its neuron connections such that the next time it receives a particular input it generates a relevant output.

Neural networks must undergo training by analyzing data sets. Neural networks learn from the training data sets provided to them and rearrange interconnections between the neurons in response to training data. The strength or weight of connections between the neurons or layers can vary. A connection between two or more layers can be strong, weak, or anywhere in between. When a neural network self-adapts it adjusts the strength of the connections among its neurons to generate more accurate outputs.

To get a neural network to provide accurate outputs, neuron connections are adjusted by repeatedly training the network by exposing it to training data sets. There can be thousands and millions of neurons or layers in a network and adjusting their connections between the layers is a compute-intensive matrix-based mathematical procedure.

Typically, training of deep learning AI systems is performed using Graphics Processing Unit (“GPU”) clusters of servers. A GPU is hardware capable of performing math computations over a huge amount of data at the same time. GPU's do not operate as fast as central processing units (“CPUs”). However, GPUs are capable of processing larger quantities of data per unit of time. Thus, even though each individual GPU operation may execute more slowly, applying computational operations to more data in parallel exceeds CPU performance, allowing AI system to be fully trained faster.

The training phase may go on for multiple iterations until the outputs of the AI system are satisfactory and accurate. Once that happens, the trained neural network is released to production on less powerful hardware. Data processed after the AI system is in production may be fed back into the neural network to correct it or enhance output according to the latest trends detected in newly acquired data sets. Therefore, the process of training and retraining a neural network can occur iteratively over time. Retraining a neural network is important for maintaining its accuracy with respect to new data sets.

In one aspect, a processor may implement a statistical model in various systems and methods throughout this specification, and that statistical model may be a machine learning AI system.

In another aspect, a processor may implement a statistical model in various systems and methods throughout this specification, and that statistical model may be a deep learning AI system. In one aspect, the deep learning AI system may be a neural network. In a further aspect, the neural network may be a deep neural network.

In further aspect, the processor may implement more than one statistical model in various systems and methods taught throughout this specification, and the more than one statistical model may be machine learning AI systems. In another aspect, the more than one statistical model may be deep learning AI systems. These deep learning AI systems may be neural networks. In another aspect, the more than one statistical model may be a combination of machine learning AI systems and deep learning AI systems.

Provided are systems and methods to meet the above outlined objects of the invention.

Methods for enhancing compatibility of a document of an entity with an organization's database on a computer server to facilitate detection of a fraudulent submission. Methods may include receiving the document already populated in a first format from the entity. Methods may include using a processor to digitize the document into a digital format, such as bytes.

Methods may include using the processor to convert the document from a digital format into a second format, where the first format may not be compatible with the database and the second format may be compatible with the database. Methods may include using the processor to determine an entity identifier.

Methods may include using the processor to run a statistical model based on data stored in the database and reference the entity identifier to predict a value of a data field in the document. Methods may include using the processor to determine whether there is a difference between the value in the predicted data field and a value in a corresponding data field in the populated document.

Methods may include implementing a corrective action when there is a difference. Methods may include raising a fraud alert directed to the organization as the corrective action. Methods may include contacting the entity as a corrective action to verify the accuracy of the document as received from the entity.

Methods may further include using optical character recognition (OCR) to digitize the populated document in the first format to a digital format.

When determining an entity identifier, methods may include where the identifier is a type of form of the populated document. The type of form might be an intake form for obtaining a product or service. The entity identifier may be the entity's name. When the entity is an individual, it may be the individual's name such as their first name and last name. When the entity is an establishment, the entity name may be the name of the establishment. The entity identifier may be metadata associated with the document.

Methods may further include using the entity identifier to identify information for contacting the entity from information found in the database. For example, the information found in the database may be preexisting information found in the database before receipt of the entity's populated second document or information found in the database may be known independently of the entity's populated second document.

Methods may further include using the entity identifier to identify information for contacting the entity from preexisting information found in the database before receipt or independent of receipt of the entity's populated document.

Methods further include, when resolving the corrective action, updating the statistical model based on data in a correct version of the document. The correct version of the document may be the populated version received from the entity. The correct version of the document may be a corrected version received from the entity. The corrected version may reflect a value of a data field predicted by the processor running the statistical model. The method may further include updating the statistical model based on metadata in the correct version of the document. The method may further include updating the statistical model based on data and metadata in the correct version of the document.

Methods may include populating the database with data from previous documents. Methods may further include populating the database with metadata from previous documents. Methods may include populating the database with data and metadata from previous documents.

Methods may include using a machine learning artificial intelligence system as the statistical model. Methods may include using a deep learning artificial intelligence system as the statistical model. In one example, the deep learning artificial intelligence system uses neural networks.

A system may include a processor and a non-transitory computer-readable storage medium, the latter storing processor-executable instructions. This system, when executed by the processor, may cause the processor to perform the above-described methods.

In a different aspect of the disclosure, systems for enhancing compatibility of a document with a database of an organization to facilitate use of a statistical model, where the computer server stores the database and the contents of the database enable the statistical model to detect a fraudulent submission. Systems may include the processor and the non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by the processor, may cause the processor to perform the following steps.

Systems may cause the processor to digitize a document in a first format into a digital format, such as bytes, where the first format may not be compatible with the database or may be poorly compatible with the database. Systems may further cause the processor to convert the document from a digital format into a second format, where the second format of the document may be compatible with the organization's database.

Systems may cause the processor to populate the database on the computer server with data from the document in the second format. Systems may cause the processor to populate the database on the computer server with metadata from the document in the second format. Systems may cause the processor to populate the database on the computer server with data and metadata from the document in the second format. Systems may further cause the processor to update the statistical model based on data from the document.

Systems may receive another document populated with data from the entity. For reference, this document shall be referred to as the second document, and the previously described document as the first document. Systems may include receipt of the second document from the same entity as the first document. Systems may include receipt of the second document from a source other than the entity which provides the first document.

Systems may determine an entity identifier using a processor which utilizes data associated with the second document. Systems may determine a predicted value of a data field in the second document using a processor to run a statistical model based on data stored in the database and the entity identifier. Systems may determine, using the processor, if there is a difference between the predicted value of the data field and a correlating value for a data field in the second document populated by the entity.

Systems may implement a corrective action when there is a difference between the predicted and calculated values. Systems may raise a fraud alert at the organization as the corrective action. Systems may contact the entity as a corrective action to verify the accuracy of the second document as received from the entity. Systems may use the entity identifiers to identify information for contacting the entity from preexisting information found in the database before receipt or independent of receipt of the entity's populated second document.

Systems may further include the use of optical character recognition (OCR) to digitize the populated document in the first format to a digital format.

An example of an entity identifier is a type of form of the populated second document. The type of form might be an intake form for obtaining a product or service. Another example of the entity identifiers is entity's name. When the entity is an individual, it may be the individual's name such as their first name and last name. When the entity is an establishment, the entity name may be the name of the establishment. The entity identifier may be metadata associated with the second document.

When resolving the corrective action, systems update the statistical model based on data in a correct version of the second document. The correct version of the second document may be the populated version received from the entity. The correct version of the second document may be a corrected version received from the entity. The corrected version may reflect values of data fields predicted by a processor running the statistical model. The system may update the statistical model based on metadata in the correct version of the second document. The system may update the statistical model based on data and metadata in the correct version of the second document.

Systems may populate the database with data from previous documents. Systems may populate the database with metadata from previous documents. Systems may populate the database with data and metadata from previous documents.

Systems may use a machine learning artificial intelligence system as the statistical model. Systems may use a deep learning artificial intelligence system as the statistical model. In one example, the deep learning artificial intelligence system uses neural networks.

Described are illustrative embodiments of apparatus and methods in accordance with the principles of the invention with reference to the accompanying illustrative drawings, which form a part hereof. Utilizing other embodiments, and structural, functional, and procedural modifications may stay within the scope and spirit of the present invention.

The drawings show illustrative features of apparatus and methods in accordance with the principles of the invention. The drawings show illustrative features in the context of selected embodiments. One may practice, in accordance with the principles of the invention, one of the embodiments along with features shown in connection with another of the embodiments.

Apparatus and methods described herein are illustrative. Apparatus and methods of the invention may involve some or all the features of the illustrative apparatus and/or some or all the steps of the illustrative methods. One may perform the steps of the methods in an order other than the order shown or described herein. Some embodiments may omit steps shown or described in connection with the illustrative methods. Some embodiments may include steps that are not shown or described in connection with the illustrative methods but shown or described in a different portion of the specification.

One of ordinary skill in the art will appreciate that one may perform the steps shown and described herein in other than the recited order and that one or more steps illustrated may be optional. The methods of the above-referenced embodiments may involve the use of any suitable elements, steps, computer-executable instructions, or computer-readable data structures. In this regard, disclosed are other embodiments as well that can be partially or wholly implemented on a computer-readable medium, for example, by storing computer-executable instructions or modules or by utilizing computer-readable data structures.

FIG.1shows an illustrative block diagram of system100that includes computer101. One may refer to Computer101as an “engine,” “server” or “computing device.” Computer101may be a workstation, desktop, laptop, tablet, smart phone, or any other suitable computing device. One may use elements of system100, including computer101, to implement various aspects of the systems and methods disclosed herein. (Each of the apparatus illustrated inFIGS.3-5and9-11and/or described herewith, including Hash Table302, Edge Computing Device304, hashing algorithm processor404and centralized data repository930, may include some or all the elements and apparatus of system100.)

Computer101may have processor103for controlling operation of the device and its associated components, and may include RAM105, ROM107, input/output module109, and non-transitory/non-volatile machine-readable/writeable memory115. One may configure machine-readable/writeable memory to store information in machine-readable/writeable data structures. Processor103may also execute all software running on the computer—e.g., an operating system and/or voice recognition software. Other components commonly used for computers, such as EEPROM or Flash memory or any other suitable components, may also be part of computer101.

Memory115may be comprised of any suitable permanent storage technology—e.g., a hard drive. Memory115may store software including operating system117and application program(s)119along with any data111needed for operation of system100. Memory115may also store videos, text, and/or audio assistance files. One may store data in memory115, in cache memory, or in any other suitable memory.

Input/output (“I/O”) module109may include connectivity to a microphone, keyboard, touch screen, mouse, and/or stylus. One may provide input into computer101through these I/O modules. The input may include input relating to cursor movement. I/O109may also include one or more speakers for providing audio output and a video display device for providing textual, audio, audiovisual, and/or graphical output. The input and/or output may be related to computer application functionality.

One may connect System100to other systems via local area network (LAN) interface (or adapter)113. System100may operate in a networked environment supporting connections to one or more remote computers, such as terminals141and151. Terminals141and151may be personal computers or servers that include many or all the elements described above relative to system100. Network connections depicted inFIG.1include a local area network (LAN)125and a wide area network (WAN)129but may also include other networks. One may connect computer101to LAN125through LAN interface (or adapter)113when using a LAN networking environment. When used in a WAN networking environment, computer101may include modem127or other means for establishing communications over WAN129, such as Internet131.

One appreciates that the network connections shown are illustrative. One may use other means of establishing a communications link between computers. One may presume the existence of various well-known protocols such as TCP/IP, Ethernet, FTP, HTTP, and the like. One may operate the system in a client-server configuration to permit retrieval of data from a web-based server or API. One may understand that web-based, for the purposes of this application, includes a cloud-based system. The web-based server may transmit data to any other suitable computer system. The web-based server may also send computer-readable instructions, together with data, to any suitable computer system. The computer-readable instructions may be to store data in cache memory, the hard drive, secondary memory, or any other suitable memory.

Additionally, one may use application program(s)119on computer101. These programs may include computer executable instructions for invoking functionality related to communication, such as e-mail, Short Message Service (SMS), and voice input and speech recognition applications. One may refer to application program(s)119(alternatively, “plugins,” “applications,” or “apps”) to include computer executable instructions for invoking functionality related to performing various tasks. Application program(s)119may utilize one or more algorithms that process received executable instructions, perform power management routines or other suitable tasks. Application program(s)119may utilize one or more decisioning processes for the processing of calls received from calling sources as detailed herein.

Application program(s)119may include computer executable instructions (alternatively referred to as “programs”). Embodied in hardware or firmware (not shown) may be the computer executable instructions. Computer101may execute the instructions embodied by the application program(s)119to perform various functions.

Application program(s)119may utilize the computer-executable instructions executed by a processor. Programs include routines, programs, objects, components, data structures, etc. that perform tasks or implement abstract data types. A computing system may be operational with distributed computing environments. Remote processing may perform tasks on devices linked through a communications network. In a distributed computing environment, a program may be in both local and remote computer storage media including memory storage devices. Computing systems may rely on a network of remote servers hosted on the Internet to store, manage, and process data (e.g., “cloud computing” and/or “fog computing”).

Stored in memory115is any information described above in connection with database111, and any other suitable information. One or more of application program(s)119may include one or more algorithms used to add data and metadata to a database, identify a type of form being used, predict fields in a document, identify changes between documents, provide changes to an entity to ascertain if an error is present, identify fraud concerns, communicate fraud concerns to interested parties within an organization, and provide documents for providing to a regulatory authority.

One may describe the invention in the context of computer-executable instructions, such as application program(s)119, for execution by a computer. Programs may include routines, programs, objects, components, and data structures, which perform tasks or implement data types. One may practice the invention in distributed computing environments. One may perform tasks by remote processing devices, linked through a communications network. In a distributed computing environment, programs may be in both local and remote computer storage media including memory storage devices. One may consider such programs, for the purposes of this application, as engines with respect to the performance of the program-assigned tasks.

Computer101and/or terminals141and151may also include various other components, such as a battery, speaker, and/or antennas (not shown). One may link components of computer system101by a system bus, wirelessly or by other suitable interconnections. Components of computer system101may be present on one or more circuit boards. In some embodiments, the components may be integrated into a single chip. The chip may be silicon-based.

Terminal151and/or terminal141may be portable devices such as a laptop, cell phone, Blackberry™, tablet, smartphone, or any other computing system for receiving, storing, transmitting and/or displaying relevant information. Terminal151and/or terminal141may be one or more user devices. Terminals151and141may be identical to system100or different. The differences may be related to hardware components and/or software components.

The invention may be operational with numerous other general purpose or special purpose computing system environments or configurations. Examples of well-known computing systems, environments, and/or configurations that may be suitable for use with the invention include, but are not limited to, personal computers, server computers, hand-held or laptop devices, tablets, mobile phones, smart phones and/or other personal digital assistants (“PDAs”), multiprocessor systems, microprocessor-based systems, cloud-based systems, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like.

FIG.2shows an illustrative block diagram of apparatus200. One may configure apparatus200in accordance with the principles of the disclosure. Apparatus200may be a computing device. (Apparatus200may include one or more features of the apparatus shown inFIGS.3-5and9-11and/or described herewith, including Hash Table302, Edge Computing Device304, hashing algorithm processor404and centralized data repository930, and may include some or all the elements and apparatus of system100shown inFIG.1.) Apparatus200may include chip module202, which may include one or more integrated circuits, and which may include logic configured to perform any other suitable logical operations.

Apparatus200may include one or more of the following components: I/O circuitry204, which may include a transmitter device and a receiver device and may interface with fiber optic cable, coaxial cable, telephone lines, wireless devices, PHY layer hardware, a keypad/display control device or any other suitable media or devices; peripheral devices206, which may include counter timers, real-time timers, power-on reset generators or any other suitable peripheral devices; logical processing device208, which may compute data structural information and structural parameters of data; and machine-readable/writeable memory210.

One may configure machine-readable/writeable memory210to store information in machine-readable/writeable data structures, such as: machine executable instructions (for example, “computer instructions” or “computer code”); applications, signals; and/or any other suitable information or data structures.

One may couple together components202,204,206,208and210by system bus (or other interconnections)212and may be present on one or more than one circuit board220. In some embodiments, the components may be integrated into a single chip. The chip may be silicon-based.

FIG.3shows illustrative flowchart300of method steps for enhancing compatibility of a document of an entity with a database of an organization on a computer server.

Depicted is flowchart300as starting at step302. Received at step302is a document in a first format from the entity, at the computer server. At step304, it may be determined if the document in the first format is compatible with the database on the computer server.

If the document in the first format is compatible with the database, then at step320, at least one computer hardware processor populates the database with data from the document in the first format. The at least one computer hardware processor may populate the database with metadata from the document in the first format. The at least one computer hardware processor may populate the database with data and metadata from the document in the first format. At step322, the at least one computer hardware processor stores the populated database, and the document in the first format at the computer server.

If the document in the first format is not compatible with the database, then at step306, at least one computer hardware processor digitizes the document into a digital format such as bytes.

At step308, at least one computer hardware processor converts the document from the digital format into a second format, where the second format of the document may be compatible with the computer server.

At step310, at least one computer hardware processor may extract data, metadata, or both data and metadata from the document in the second format.

At step312, at least one computer hardware processor may populate at least one field in a database using the extracted data, metadata, or data and metadata from the document.

At step314, the computer servers store the populated database and the document in the second format.

FIG.4shows illustrative flowchart400of method steps for harvesting data from one or more documents stored on a computer server for populating a database of an organization. The computer servers may store the database. The one or more documents may be a legacy system. The one or more documents may not be compatible with the database. The method may enhance the compatibility of one or more documents.

Depicted is flowchart400, starting at step402. At step402, at least one computer hardware processor determines the compatibility of a document stored in a first format with a database stored on a computer server.

If the document in the first format is compatible with the database, then at step420, at least one computer hardware processor populates the database with data, metadata, or both data and metadata from the document in the first format. At step422, the computer servers store the populated database, and the document in the first format.

If the document in the first format is not compatible with the database, then at step404, at least one computer hardware processor digitizes the document into a digital format, such as bytes.

At step406, at least one computer hardware processor may convert the document from the digital format into a second format, where the second format of the document may be compatible with the computer server.

At step408, at least one computer hardware processor extracts data, metadata, or data and metadata from the document in the second format.

At step410, at least one computer hardware processor may extract data, metadata, or data and metadata from the document and populate at least one field in a database.

At step412, at least one computer hardware processor may populate the database and the document in the second format at the computer server.

FIG.5Ashows illustrative flowchart500of method steps for harvesting data and metadata from one or more documents stored on a computer server for populating a database of an organization. At least one computer hardware processor may update a statistical model with data, metadata, or data and metadata from the populated database. The method may include pre-populating a document requested by using the statistic model. The method may include verifying with an entity if a change is correct between the pre-populated document and a completed document received back from the entity.

Depicted is a flowchart500, starting at step502. At step502, at least one computer hardware processor may digitize a document into a digital format, such as bytes.

At step504, the at least one computer hardware processor may convert the document from the digital format into a second format, using the at least one computer hardware processor, where the second format of the document may be compatible with the computer server.

At step506, at least one computer hardware processor may extract data, metadata, or data and metadata from the document in the second format.

At step508, the at least one computer hardware processor may populate at least one field in a database with data, metadata, or data and metadata extracted from the document.

At step510, at least one computer hardware processor may update a statistical model based on data, metadata, or data and metadata from the first document.

Received at step512is a request from an entity to complete a second document.

At step514, the at least one computer hardware processor, using the statistical model based on the data, metadata, or data and metadata stored in the database, pre-populates at least one data field in the second document.

Transmitted, at step516, is the second document, with at least one pre-populated data field, to the entity.

Received, at step518, is the second document from the entity in completed form. The method continues inFIG.5Bwith step522.

FIG.5Bis a continuation of the flowchart begun inFIG.5A.FIG.5Bshows illustrative flowchart500of method steps.

At step522, it may be determined if one or more fields changed between the pre-populated and completed second form.

At step542, with no field changed between the pre-populated and completed second form, the computer servers may: store the second document; update the database, at step544, with data, metadata, or data and metadate from the second document; update the statistical model, at step546, based on data, metadata, or data and metadata from the second document; and/or determine the correctness of at least one field change between the pre-populated and completed second form at step524.

When assuming that one or more changes are correct, the servers may implement steps542,544and546, as previously described.

When not assuming that one or more changes are correct, the servers may seek verification from the entity; provide the entity, at step526, with the one or more changed fields identified between the pre-populated second document provided to the entity and the completed second document received from the entity; provide the entity, at step528, with an inquiry as to whether the one or more changed fields in the completed second document is correct, or if the pre-populated field in the second document provided to the entity is correct and/or follow steps542,544and546if the entity provides feedback that the one or more changed fields are correct.

At step530, the servers may correct the second document in accordance with the feedback from the entity, at step532, store the corrected form of the second document on the computer server, at step534, update the database with data, metadata, or data and metadata from the second document in corrected form and/or, at step536, update the statistical model based on data, metadata, or data and metadata from the second document in corrected form.

FIG.6Ashows illustrative flowchart600for harvesting data and metadata from one or more documents stored on a computer server for populating a database of an organization. At least one computer hardware processor may update the database and a statistical model based on data and metadata from similar documents. The method may include pre-populating a document requested by using the statistic model. The method may include verifying with an entity if a change is correct between the pre-populated document and a completed document received back from the entity.

The method may further include at least one computer hardware processor to verify sufficient training of the statistical model. One may then use at least one computer hardware processor to implement the statistical model. For example, the at least one computer hardware processor may set and monitory a confidence threshold, based on a range of factors, and implement the statistic model after reaching the threshold, or may implement more training of the model before reaching the threshold.

Further, methods include at least one computer hardware processor determining a count of unsuccessful attempts to pre-populate a document. When exceeding a threshold, the method may provide for gathering more data and metadata from one or more documents completed by one or more entities. At least one computer hardware processor uses the additional data and metadata to train and update the statistical model. Once accomplishing an amount of training of the statistical model, the method may allow for utilizing the statistical method for subsequent requests to complete a document by an entity. Upon reaching a confidence threshold, such as the method described previously, or other measure of confidence, at least one computer hardware processor implements the statistical model.

Depicted is a flowchart600, starting at step602. At step602, at least one computer hardware processor may digitize a document into a digital format, such as bytes.

At step604, the at least one computer hardware processor may convert the document from the digital format into a second format, using the at least one computer hardware processor, where the second format of the document may be compatible with the computer server.

At step606, at least one computer hardware processor may extract data, metadata, or data and metadata from the document in the second format.

At step608, at least one computer hardware processor may populate at least one field in a database with data, metadata, or data and metadata extracted from the document.

At step610, at least one computer hardware processor may update a statistical model based on data, metadata, or data and metadata from the first document.

Received at step612is a request from an entity to complete a second document.

Once accomplishing an amount of training of the statistical model, the method may allow for utilizing the statistical method for subsequent requests to complete a document by an entity.

At step614, upon reaching a confidence threshold, such as the method described previously, or other measure of confidence, at least one computer hardware processor implements the statistical model.

At step614, at least one computer hardware processor determines the confidence value for the statistical model. When the confidence value does not exceed a confidence threshold, then, at step630, at least one computer hardware processor transmits the second document to the entity without any fields pre-populated.

At least one computer hardware processor receives, at step632, the completed second document. The method continues inFIG.6Bwith step662.

When the confident value exceeds a confidence threshold, then, at step616, the at least one computer hardware processor, using the statistical model based on the data, metadata, or data and metadata stored in the database, pre-populates at least one data field in the second document.

Transmitted, at step618, is the second document, with at least one pre-populated data field, to the entity.

Received, at step620, is the second document from the entity in completed form. The method continues inFIG.6Bwith step642.

FIG.6Bis a continuation of the method begun inFIG.6A.FIG.6Bshows illustrative flowchart600of method steps.

Continuing from step632inFIG.6A, the method continues inFIG.6Bwith step662. At step662, at least one computer hardware processor stores the second document on the computer server.

At step664, at least one computer hardware processor updates the database with data, metadata, or data and metadata from the second document.

At step666, at least one computer hardware processor updates the statistical model based on data, metadata, or data and metadata from the second document.

Continuing from step620inFIG.6A, the method continues inFIG.6Bwith step642. At step642, at least one computer hardware processor determines differences in the data fields between the pre-populated and completed second document.

At step662, at least one computer hardware processor stores the second document on the computer server when the fields are similar between the pre-populated and completed second form. At step664, at least one computer hardware processor updates the database with data, metadata, or data and metadate from the second document. At step666, at least one computer hardware processor updates the statistical model based on data, metadata, or data and metadate from the second document.

When there is at least one change, one may determine the correctness of at least one field changed between the pre-populated and completed second form at step644.

When assuming that one or more changes are correct, implement steps662,664, and666, as previously described.

When assuming that one or more changes are correct, seek verification from the entity. Provide the entity, at step646, with the one or more changed fields identified between the pre-populated second document provided to the entity and the completed second document received from the entity.

Provide the entity, at step648, with an inquiry as to whether the one or more changed fields in the completed second document is correct, or if the pre-populated field in the second document provided to the entity is correct.

Follow steps662,664and666if the entity provides feedback that the one or more changed fields are correct.

At step650, correct the second document in accordance with the feedback from the entity.

At step652, store the corrected form of the second document on the computer server.

At step654, update the database with data, metadata, or data and metadata from the second document in corrected form. The method may continue inFIG.6Cwith step656.

FIG.6Cis a continuation of the method begun inFIG.6B.FIG.6Cshows illustrative flowchart600of method steps.

At step656, update the statistical model based on data, metadata, or data and metadata from the second document in corrected form

Received, at step670, is a request indicating that an entity seeks to complete a third document. Determined, at step672, is a count of unsuccessful pre-populated document attempts.

If the count of unsuccessful pre-populated documents does not exceed a threshold, then, at step686, the method may continue with step616, except implementing the method for a third document instead of a second document.

If the count of unsuccessful pre-populated documents does exceed a threshold, then, at step674, at least one computer hardware processor transmits the third document to the entity without any fields pre-populated.

Received from the entity, at step676, is the third document in completed form.

At step678, at least one computer hardware processor may update the database with data, metadata, or data and metadata from the third document.

At step680, at least one computer hardware processor may update the statistical model based on data from the third document.

FIG.7shows illustrative flowchart700of method steps for enhancing compatibility of a document of an entity with a database of an organization to facilitate detection of a fraudulent submission, where storage of the database is on a computer server.

Depicted is flowchart700as starting at step702. Receive at step702at the computer server a document in a first format from the entity. The first format can be compatible with the database. At step704, a processor can digitize the document into a digital format such as bytes.

At step706, the processor can convert the document from the digital format into a second format, where the second format of the document can be compatible with the computer server. At step708, the processor can determine an entity identifier based on data associated with the document. At step710, the processor can run a statistical model based on data stored in the database and use the entity identifier to predict a value of a data field in the document.

At step712, the processor can determine if there is a difference between a predicted value of the data field and a correlating value for the data field in the document populated by the entity. At step714, the processor can implement a corrective action when there is a difference, wherein the corrective action is to raise a fraud alert at the organization.

FIG.8shows illustrative flowchart800of method steps for enhancing compatibility of a document with a database of an organization to facilitate use of a statistical model, where the computer server can store a database and the database can provide content to enable the statistical model. A processor may use the statistical model to detect a fraudulent submission.

Depicted is flowchart800as starting at step802. At step802, a processor can digitize a first document in a first format into a digital format, where the first format is not compatible with a database of an organization or is poorly compatible with a database of an organization, and the digital format of the first document can be as bytes. At step804, the processor can convert the document from the digital format into a second format, where the second format of the document can be compatible with the computer server.

At step806, the processor can populate a date field in a database using extracted data from the first document in the second format. At step808, update a statistical model based on data from the first document. At step810, receive a second document from the entity populated with data. At step812, determine, using the processor, an entity identifier, which can be based on data associated with the second document.

At step814, the processor can run a statistical model based on data stored in the database and the identifier to predict a value of a data field in the second document. At step816, the processor can determine if there is a difference between the predicted value of the data field and a correlating value for the data field in the second document populated by the entity. At step818, the processor can implement a corrective action when there is a difference, wherein the corrective action is to contact the entity to verify the accuracy of the second document as received from the entity. The mention of data, herein, may refer to data, metadata, or both data and metadata.

Presented herein are systems and methods for enhancing compatibility of a document to facilitate addition of data from the document to a database and use of the database to power a statistical model to identify fraudulent behavior. Persons skilled in the art may practice the present invention in other than the described embodiments. Presented are embodiments for purposes of illustration rather than of limitation. Only the claims that follow limit the present invention.