Patent Description:
The present invention generally relates to robotic process automation (RPA), and more specifically, to a document processing framework (DPF) for RPA.

Most commercially available electronic document processing tools perform document processing on a single platform. Also, most vendors in the document processing space offer a black box solution in which components cannot be interchanged with third party offerings. Due to this limitation, customers who wish to use more than one vendor need to set up two or more parallel integrations, train employees in using two or more different tools, and provide respective user interfaces for performing these duties. This issue is present in solutions that aim to integrate multiple techniques for document processing, and is not necessarily RPA-specific. Indeed, a separate workflow is required for each vendor.

Furthermore, users who wish to use their own custom document processing algorithms need to build an entire infrastructure themselves to have them integrated and usable in an end-to-end process. Such document processing algorithms include, but are not limited to, handling digitization using multiple OCR vendors, handling taxonomy mappings across different solutions, integration and configuration of document processing algorithms from different vendors, aggregating results from multiple vendors under a unified data format, etc. Given the above, an improved document processing framework solution may be beneficial.

<CIT> concerns a robotic automation platform which is used for handling customer requests relating to insurances, e.g. free-form email requests. At first, another platform, the so-called AI orchestration platform, classifies the requests according to their intent. Based on the indication of intent, an entity identifier is extracted from the request. Both, the identified intent and entity identifier are transmitted to the robotic automation platform for further processing. The system facilitates the provision of accurate responses to the customer request.

Certain embodiments of the present invention may provide solutions to the problems and needs in the art that have not yet been fully identified, appreciated, or solved by current computer-based document processing techniques. For example, some embodiments of the present invention pertain to a DPF for RPA.

In order that the advantages of certain embodiments of the invention will be readily understood, a more particular description of the invention briefly described above will be rendered by reference to specific embodiments that are illustrated in the appended drawings. While it should be understood that these drawings depict only typical embodiments of the invention and are not therefore to be considered to be limiting of its scope, the invention will be described and explained with additional specificity and detail through the use of the accompanying drawings, in which:.

Some embodiments pertain to a document processing framework (DPF) for robotic process automation (RPA). In some embodiments, the DPF allows plug-and-play use of different vendor products on the same platform, where users can setup a basic schema for document processing and for a document understanding workflow. The DPF may thus allow users to process documents through the entire flow using in-application or third-party algorithms, for tasks such as, but not limited to, digitization, document classification and data extraction. This provides a unified experience and avoids vendor lock-in. The DPF of some embodiments, with this plug-and-play approach, supports combining, replacing, prioritizing, and filtering of document processing technologies based on the needs of each use case, thus offering flexibility in choosing the appropriate solution for each particular problem to be solved.

In some embodiments, the DPF allows users to: (<NUM>) define a taxonomy (i.e., a collection of document types); (<NUM>) digitize a file by processing the file to obtain machine-readable text; (<NUM>) classify the file into one or more document types by assigning a document type and a range of pages to each classification result using an available combination of algorithms for document classification; (<NUM>) validate the classification via a tool through which humans can validate/correct/manually perform file breakdown and classification; (<NUM>) extract data by assigning identified values from within a document or range of pages to a requested field using an available combination of algorithms for data extraction; (<NUM>) validate the extraction via a tool through which humans can validate/correct/manually perform data extraction; (<NUM>) train classifiers by ensuring that the classification algorithms are configured to receive the human feedback and trigger feedback loops where applicable; and/or (<NUM>) train extractors by ensuring that the extraction algorithms are configured to receive the human feedback and trigger feedback loops where applicable.

The DPF of some embodiments also includes a public package that can be used by software developers (e.g., developers from third-party companies or individual contributors) to manage the DPF and build their own document processing components including, but not limited to, classifier and extractor components. The public package in some embodiments may include, but are not limited to: (<NUM>) taxonomy, document, and extraction results data structures, along with functionality to assist in managing these structures; (<NUM>) abstract classes and interfaces to be used for building custom classifier and extractor components; and/or (<NUM>) data extraction and classification input and output data structures and contracts to be used when reporting classification or data extraction outputs. This public package may ensure that the entire DPF is fully scalable, open, and usable by third parties to develop use case-specific or solution-specific components in a unified manner that are fully compatible with the DPF.

<FIG> is an architectural diagram illustrating an RPA system <NUM>, according to an embodiment of the present invention. RPA system <NUM> includes a designer <NUM> that allows a developer to design and implement workflows. Designer <NUM> may provide a solution for application integration, as well as automating third-party applications, administrative Information Technology (IT) tasks, and business IT processes. Designer <NUM> may facilitate development of an automation project, which is a graphical representation of a business process. Simply put, designer <NUM> facilitates the development and deployment of workflows and robots.

The automation project enables automation of rule-based processes by giving the developer control of the execution order and the relationship between a custom set of steps developed in a workflow, defined herein as "activities. " One commercial example of an embodiment of designer <NUM> is UiPath Studio™. Each activity may include an action, such as clicking a button, reading a file, writing to a log panel, etc. In some embodiments, workflows may be nested or embedded.

Some types of workflows may include, but are not limited to, sequences, flowcharts, Finite State Machines (FSMs), and/or global exception handlers. Sequences may be particularly suitable for linear processes, enabling flow from one activity to another without cluttering a workflow. Flowcharts may be particularly suitable to more complex business logic, enabling integration of decisions and connection of activities in a more diverse manner through multiple branching logic operators. FSMs may be particularly suitable for large workflows. FSMs may use a finite number of states in their execution, which are triggered by a condition (i.e., transition) or an activity. Global exception handlers may be particularly suitable for determining workflow behavior when encountering an execution error and for debugging processes.

Once a workflow is developed in designer <NUM>, execution of business processes is orchestrated by conductor <NUM>, which orchestrates one or more robots <NUM> that execute the workflows developed in designer <NUM>. One commercial example of an embodiment of conductor <NUM> is UiPath Orchestrator™. Conductor <NUM> facilitates management of the creation, monitoring, and deployment of resources in an environment. Conductor <NUM> may act as an integration point with third-party solutions and applications.

Conductor <NUM> may manage a fleet of robots <NUM>, connecting and executing robots <NUM> from a centralized point. Types of robots <NUM> that may be managed include, but are not limited to, attended robots <NUM>, unattended robots <NUM>, development robots (similar to unattended robots <NUM>, but used for development and testing purposes), and nonproduction robots (similar to attended robots <NUM>, but used for development and testing purposes). Attended robots <NUM> are triggered by user events and operate alongside a human on the same computing system. Attended robots <NUM> may be used with conductor <NUM> for a centralized process deployment and logging medium. Attended robots <NUM> may help the human user accomplish various tasks, and may be triggered by user events. In some embodiments, processes cannot be started from conductor <NUM> on this type of robot and/or they cannot run under a locked screen. In certain embodiments, attended robots <NUM> can only be started from a robot tray or from a command prompt. Attended robots <NUM> should run under human supervision in some embodiments.

Unattended robots <NUM> run unattended in virtual environments and can automate many processes. Unattended robots <NUM> may be responsible for remote execution, monitoring, scheduling, and providing support for work queues. Debugging for all robot types may be run in designer <NUM> in some embodiments. Both attended and unattended robots may automate various systems and applications including, but not limited to, mainframes, web applications, VMs, enterprise applications (e.g., those produced by SAP®, SalesForce®, Oracle®, etc.), and computing system applications (e.g., desktop and laptop applications, mobile device applications, wearable computer applications, etc.).

Conductor <NUM> may have various capabilities including, but not limited to, provisioning, deployment, configuration, queueing, monitoring, logging, and/or providing interconnectivity. Provisioning may include creating and maintenance of connections between robots <NUM> and conductor <NUM> (e.g., a web application). Deployment may include assuring the correct delivery of package versions to assigned robots <NUM> for execution. Configuration may include maintenance and delivery of robot environments and process configurations. Queueing may include providing management of queues and queue items. Monitoring may include keeping track of robot identification data and maintaining user permissions. Logging may include storing and indexing logs to a database (e.g., an SQL database) and/or another storage mechanism (e.g., ElasticSearch®, which provides the ability to store and quickly query large datasets). Conductor <NUM> may provide interconnectivity by acting as the centralized point of communication for third-party solutions and/or applications.

Robots <NUM> are execution agents that run workflows built in designer <NUM>. One commercial example of some embodiments of robot(s) <NUM> is UiPath Robots™. In some embodiments, robots <NUM> install the Microsoft Windows® Service Control Manager (SCM)-managed service by default. As a result, such robots <NUM> can open interactive Windows® sessions under the local system account, and have the rights of a Windows® service.

In some embodiments, robots <NUM> can be installed in a user mode. For such robots <NUM>, this means they have the same rights as the user under which a given robot <NUM> has been installed. This feature may also be available for High Density (HD) robots, which ensure full utilization of each machine at its maximum potential. In some embodiments, any type of robot <NUM> may be configured in an HD environment.

Robots <NUM> in some embodiments are split into several components, each being dedicated to a particular automation task. The robot components in some embodiments include, but are not limited to, SCM-managed robot services, user mode robot services, executors, agents, and command line. SCM-managed robot services manage and monitor Windows® sessions and act as a proxy between conductor <NUM> and the execution hosts (i.e., the computing systems on which robots <NUM> are executed). These services are trusted with and manage the credentials for robots <NUM>. A console application is launched by the SCM under the local system.

User mode robot services in some embodiments manage and monitor Windows® sessions and act as a proxy between conductor <NUM> and the execution hosts. User mode robot services may be trusted with and manage the credentials for robots <NUM>. A Windows® application may automatically be launched if the SCM-managed robot service is not installed.

Executors may run given jobs under a Windows® session (i.e., they may execute workflows. Executors may be aware of per-monitor dots per inch (DPI) settings. Agents may be Windows® Presentation Foundation (WPF) applications that display the available jobs in the system tray window. Agents may be a client of the service. Agents may request to start or stop jobs and change settings. The command line is a client of the service. The command line is a console application that can request to start jobs and waits for their output.

Having components of robots <NUM> split as explained above helps developers, support users, and computing systems more easily run, identify, and track what each component is executing. Special behaviors may be configured per component this way, such as setting up different firewall rules for the executor and the service. The executor may always be aware of DPI settings per monitor in some embodiments. As a result, workflows may be executed at any DPI, regardless of the configuration of the computing system on which they were created. Projects from designer <NUM> may also be independent of browser zoom level in some embodiments. For applications that are DPI-unaware or intentionally marked as unaware, DPI may be disabled in some embodiments.

<FIG> is an architectural diagram illustrating a deployed RPA system <NUM>, according to an embodiment of the present invention. In some embodiments, RPA system <NUM> may be, or may be a part of, RPA system <NUM> of <FIG>. It should be noted that the client side, the server side, or both, may include any desired number of computing systems without deviating from the scope of the invention. On the client side, a robot application <NUM> includes executors <NUM>, an agent <NUM>, and a designer <NUM>. However, in some embodiments, designer <NUM> may not be running on computing system <NUM>. Executors <NUM> are running processes. Several business projects may run simultaneously, as shown in <FIG>. Agent <NUM> (e.g., a Windows® service) is the single point of contact for all executors <NUM> in this embodiment. All messages in this embodiment are logged into conductor <NUM>, which processes them further via database server <NUM>, indexer server <NUM>, or both. As discussed above with respect to <FIG>, executors <NUM> may be robot components.

In some embodiments, a robot represents an association between a machine name and a username. The robot may manage multiple executors at the same time. On computing systems that support multiple interactive sessions running simultaneously (e.g., Windows® Server <NUM>), there may be multiple robots running at the same time, each in a separate Windows® session using a unique username. This is referred to as HD robots above.

Agent <NUM> is also responsible for sending the status of the robot (e.g., periodically sending a "heartbeat" message indicating that the robot is still functioning) and downloading the required version of the package to be executed. The communication between agent <NUM> and conductor <NUM> is always initiated by agent <NUM> in some embodiments. In the notification scenario, agent <NUM> may open a WebSocket channel that is later used by conductor <NUM> to send commands to the robot (e.g., start, stop, etc.).

On the server side, a presentation layer (web application <NUM>, Open Data Protocol (OData) Representative State Transfer (REST) Application Programming Interface (API) endpoints <NUM>, and notification and monitoring <NUM>), a service layer (API implementation / business logic <NUM>), and a persistence layer (database server <NUM> and indexer server <NUM>) are included. Conductor <NUM> includes web application <NUM>, OData REST API endpoints <NUM>, notification and monitoring <NUM>, and API implementation / business logic <NUM>. In some embodiments, most actions that a user performs in the interface of conductor <NUM> (e.g., via browser <NUM>) are performed by calling various APIs. Such actions may include, but are not limited to, starting jobs on robots, adding/removing data in queues, scheduling jobs to run unattended, etc. without deviating from the scope of the invention. Web application <NUM> is the visual layer of the server platform. In this embodiment, web application <NUM> uses Hypertext Markup Language (HTML) and JavaScript (JS). However, any desired markup languages, script languages, or any other formats may be used without deviating from the scope of the invention. The user interacts with web pages from web application <NUM> via browser <NUM> in this embodiment in order to perform various actions to control conductor <NUM>. For instance, the user may create robot groups, assign packages to the robots, analyze logs per robot and/or per process, start and stop robots, etc..

In addition to web application <NUM>, conductor <NUM> also includes service layer that exposes OData REST API endpoints <NUM>. However, other endpoints may be included without deviating from the scope of the invention. The REST API is consumed by both web application <NUM> and agent <NUM>. Agent <NUM> is the supervisor of one or more robots on the client computer in this embodiment.

The REST API in this embodiment covers configuration, logging, monitoring, and queueing functionality. The configuration endpoints may be used to define and configure application users, permissions, robots, assets, releases, and environments in some embodiments. Logging REST endpoints may be used to log different information, such as errors, explicit messages sent by the robots, and other environment-specific information, for instance. Deployment REST endpoints may be used by the robots to query the package version that should be executed if the start job command is used in conductor <NUM>. Queueing REST endpoints may be responsible for queues and queue item management, such as adding data to a queue, obtaining a transaction from the queue, setting the status of a transaction, etc..

Monitoring REST endpoints may monitor web application <NUM> and agent <NUM>. Notification and monitoring API <NUM> may be REST endpoints that are used for registering agent <NUM>, delivering configuration settings to agent <NUM>, and for sending/receiving notifications from the server and agent <NUM>. Notification and monitoring API <NUM> may also use WebSocket communication in some embodiments.

The persistence layer includes a pair of servers in this embodiment - database server <NUM> (e.g., a SQL server) and indexer server <NUM>. Database server <NUM> in this embodiment stores the configurations of the robots, robot groups, associated processes, users, roles, schedules, etc. This information is managed through web application <NUM> in some embodiments. Database server <NUM> may manages queues and queue items. In some embodiments, database server <NUM> may store messages logged by the robots (in addition to or in lieu of indexer server <NUM>).

Indexer server <NUM>, which is optional in some embodiments, stores and indexes the information logged by the robots. In certain embodiments, indexer server <NUM> may be disabled through configuration settings. In some embodiments, indexer server <NUM> uses ElasticSearch®, which is an open source project full-text search engine. Messages logged by robots (e.g., using activities like log message or write line) may be sent through the logging REST endpoint(s) to indexer server <NUM>, where they are indexed for future utilization.

<FIG> is an architectural diagram illustrating the relationship <NUM> between a designer <NUM>, activities <NUM>, <NUM>, and drivers <NUM>, according to an embodiment of the present invention. Per the above, a developer uses designer <NUM> to develop workflows that are executed by robots. Workflows may include user-defined activities <NUM> and UI automation activities <NUM>. Some embodiments are able to identify non-textual visual components in an image, which is called computer vision (CV) herein. Some CV activities pertaining to such components may include, but are not limited to, click, type, get text, hover, element exists, refresh scope, highlight, etc. Click in some embodiments identifies an element using CV, optical character recognition (OCR), fuzzy text matching, and multi-anchor, for example, and clicks it. Type may identify an element using the above and types in the element. Get text may identify the location of specific text and scan it using OCR. Hover may identify an element and hover over it. Element exists may check whether an element exists on the screen using the techniques described above. In some embodiments, there may be hundreds or even thousands of activities that can be implemented in designer <NUM>. However, any number and/or type of activities may be available without deviating from the scope of the invention.

UI automation activities <NUM> are a subset of special, lower level activities that are written in lower level code (e.g., CV activities) and facilitate interactions with the screen. UI automation activities <NUM> facilitate these interactions via drivers <NUM> that allow the robot to interact with the desired software. For instance, drivers <NUM> may include OS drivers <NUM>, browser drivers <NUM>, VM drivers <NUM>, enterprise application drivers <NUM>, etc..

Drivers <NUM> may interact with the OS at a low level looking for hooks, monitoring for keys, etc. They may facilitate integration with Chrome®, IE®, Citrix®, SAP®, etc. For instance, the "click" activity performs the same role in these different applications via drivers <NUM>.

<FIG> is an architectural diagram illustrating an RPA system <NUM>, according to an embodiment of the present invention. In some embodiments, RPA system <NUM> may be or include RPA systems <NUM> and/or <NUM> of <FIG> and/or <NUM>. RPA system <NUM> includes multiple client computing systems <NUM> running robots. Computing systems <NUM> are able to communicate with a conductor computing system <NUM> via a web application running thereon. Conductor computing system <NUM>, in turn, is able to communicate with a database server <NUM> and an optional indexer server <NUM>.

With respect to <FIG> and <FIG>, it should be noted that while a web application is used in these embodiments, any suitable client/server software may be used without deviating from the scope of the invention. For instance, the conductor may run a server-side application that communicates with non-web-based client software applications on the client computing systems.

<FIG> is an architectural diagram illustrating a computing system <NUM> configured to provide a DPF for RPA, according to an embodiment of the present invention. In some embodiments, computing system <NUM> may be one or more of the computing systems depicted and/or described herein. Computing system <NUM> includes a bus <NUM> or other communication mechanism for communicating information, and processor(s) <NUM> coupled to bus <NUM> for processing information. Processor(s) <NUM> may be any type of general or specific purpose processor, including a Central Processing Unit (CPU), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA), a Graphics Processing Unit (GPU), multiple instances thereof, and/or any combination thereof. Processor(s) <NUM> may also have multiple processing cores, and at least some of the cores may be configured to perform specific functions. Multi-parallel processing may be used in some embodiments. In certain embodiments, at least one of processor(s) <NUM> may be a neuromorphic circuit that includes processing elements that mimic biological neurons. In some embodiments, neuromorphic circuits may not require the typical components of a Von Neumann computing architecture.

Computing system <NUM> further includes a memory <NUM> for storing information and instructions to be executed by processor(s) <NUM>. Memory <NUM> can be comprised of any combination of Random Access Memory (RAM), Read Only Memory (ROM), flash memory, cache, static storage such as a magnetic or optical disk, or any other types of non-transitory computer-readable media or combinations thereof. Non-transitory computer-readable media may be any available media that can be accessed by processor(s) <NUM> and may include volatile media, non-volatile media, or both. The media may also be removable, non-removable, or both.

Additionally, computing system <NUM> includes a communication device <NUM>, such as a transceiver, to provide access to a communications network via a wireless and/or wired connection. In some embodiments, communication device <NUM> may be configured to use Frequency Division Multiple Access (FDMA), Single Carrier FDMA (SC-FDMA), Time Division Multiple Access (TDMA), Code Division Multiple Access (CDMA), Orthogonal Frequency Division Multiplexing (OFDM), Orthogonal Frequency Division Multiple Access (OFDMA), Global System for Mobile (GSM) communications, General Packet Radio Service (GPRS), Universal Mobile Telecommunications System (UMTS), cdma2000, Wideband CDMA (W-CDMA), High-Speed Downlink Packet Access (HSDPA), High-Speed Uplink Packet Access (HSUPA), High-Speed Packet Access (HSPA), Long Term Evolution (LTE), LTE Advanced (LTE-A), <NUM>. 11x, Wi-Fi, Zigbee, Ultra-WideBand (UWB), <NUM>. 16x, <NUM>, Home Node-B (HnB), Bluetooth, Radio Frequency Identification (RFID), Infrared Data Association (IrDA), Near-Field Communications (NFC), fifth generation (<NUM>), New Radio (NR), any combination thereof, and/or any other currently existing or future-implemented communications standard and/or protocol without deviating from the scope of the invention. In some embodiments, communication device <NUM> may include one or more antennas that are singular, arrayed, phased, switched, beamforming, beamsteering, a combination thereof, and or any other antenna configuration without deviating from the scope of the invention.

Processor(s) <NUM> are further coupled via bus <NUM> to a display <NUM>, such as a plasma display, a Liquid Crystal Display (LCD), a Light Emitting Diode (LED) display, a Field Emission Display (FED), an Organic Light Emitting Diode (OLED) display, a flexible OLED display, a flexible substrate display, a projection display, a <NUM> display, a high definition display, a Retina® display, an In-Plane Switching (IPS) display, or any other suitable display for displaying information to a user. Display <NUM> may be configured as a touch (haptic) display, a three dimensional (3D) touch display, a multi-input touch display, a multi-touch display, etc. using resistive, capacitive, surface-acoustic wave (SAW) capacitive, infrared, optical imaging, dispersive signal technology, acoustic pulse recognition, frustrated total internal reflection, etc. Any suitable display device and haptic I/O may be used without deviating from the scope of the invention.

A keyboard <NUM> and a cursor control device <NUM>, such as a computer mouse, a touchpad, etc., are further coupled to bus <NUM> to enable a user to interface with computing system. However, in certain embodiments, a physical keyboard and mouse may not be present, and the user may interact with the device solely through display <NUM> and/or a touchpad (not shown). Any type and combination of input devices may be used as a matter of design choice. In certain embodiments, no physical input device and/or display is present. For instance, the user may interact with computing system <NUM> remotely via another computing system in communication therewith, or computing system <NUM> may operate autonomously.

Memory <NUM> stores software modules that provide functionality when executed by processor(s) <NUM>. The modules include an operating system <NUM> for computing system <NUM>. The modules further include a DPF module <NUM> that is configured to perform all or part of the processes described herein or derivatives thereof. Computing system <NUM> may include one or more additional functional modules <NUM> that include additional functionality.

One skilled in the art will appreciate that a "system" could be embodied as a server, an embedded computing system, a personal computer, a console, a personal digital assistant (PDA), a cell phone, a tablet computing device, a quantum computing system, or any other suitable computing device, or combination of devices without deviating from the scope of the invention. Presenting the above-described functions as being performed by a "system" is not intended to limit the scope of the present invention in any way, but is intended to provide one example of the many embodiments of the present invention. Indeed, methods, systems, and apparatuses disclosed herein may be implemented in localized and distributed forms consistent with computing technology, including cloud computing systems.

It should be noted that some of the system features described in this specification have been presented as modules, in order to more particularly emphasize their implementation independence. For example, a module may be implemented as a hardware circuit comprising custom very large scale integration (VLSI) circuits or gate arrays, off-the-shelf semiconductors such as logic chips, transistors, or other discrete components. A module may also be implemented in programmable hardware devices such as field programmable gate arrays, programmable array logic, programmable logic devices, graphics processing units, or the like.

A module may also be at least partially implemented in software for execution by various types of processors. An identified unit of executable code may, for instance, include one or more physical or logical blocks of computer instructions that may, for instance, be organized as an object, procedure, or function. Nevertheless, the executables of an identified module need not be physically located together, but may include disparate instructions stored in different locations that, when joined logically together, comprise the module and achieve the stated purpose for the module. Further, modules may be stored on a computer-readable medium, which may be, for instance, a hard disk drive, flash device, RAM, tape, and/or any other such non-transitory computer-readable medium used to store data without deviating from the scope of the invention.

Indeed, a module of executable code could be a single instruction, or many instructions, and may even be distributed over several different code segments, among different programs, and across several memory devices. The operational data may be collected as a single data set, or may be distributed over different locations including over different storage devices, and may exist, at least partially, merely as electronic signals on a system or network.

Before designing the workflow for document processing, the DPF of some embodiments allows users to define a list of document types targeted for classification and data extraction, along with associated fields for each of the document types (i.e., information to be searched for in the documents and associated types). This may be accomplished using a taxonomy manager in some embodiments. The defined taxonomy information may be stored as a master taxonomy in a public proprietary data structure, defined as a class in a public package, that can be used programmatically by both software developers and RPA developers to manipulate taxonomy objects. In some embodiments, this class may be DocumentTaxonomy in UiPath Studio™, for example.

<FIG> are screenshots illustrating a taxonomy manager <NUM>, according to an embodiment of the present invention. Taxonomy manager <NUM> includes a document types definition interface <NUM> (see also <FIG>), a document type details interface <NUM> (see also <FIG>), and an edit field interface <NUM> (see also <FIG>). Collectively, interfaces <NUM>, <NUM>, <NUM> allow a user to define a list of document types, provide document type details, and edit the fields the document types.

Document types definition interface <NUM> allows the user to add new document types or edit or remove existing document types. The interface also provides functionality to select groups and categories, as well as to search for groups and categories. Groups and categories are used in this embodiment to better organize the collection of document types in the taxonomy. Group and category filtering may be available based on these settings in interface <NUM> (see also <FIG>). Each document type can be assigned to a group and a category via interface <NUM> (see also <FIG>). Taxonomy organization (e.g., by groups, categories, document types, fields, etc.) may change over time while retaining the same purpose of capturing document processing metadata. Furthermore, the data structures, user interfaces, and public contracts may also evolve in design, packaging, content, functionality, etc..

Document type details interface <NUM> allows the user to provide a name for a document type, as well as to select a group and category for the document type. A document type code may also be optionally specified. Fields associated with the document type are shown, and the user can add, edit, or remove fields as well.

Edit field interface <NUM> allows the user to edit the properties of a given field. Available properties may vary based on multiple factors that may include, but are not limited to, field type, category, group, etc..

In some embodiments, digitization of a file may be performed using a digitization activity <NUM> in a workflow of an RPA tool <NUM> (e.g., UiPath Studio™) that outputs a text version of the processed file, along with a Document Object Model (DOM). See <FIG> and <FIG>. The DOM may be captured in a proprietary data structure of the RPA tool that may be defined as a class in a public package. This class may be used programmatically by both software developers and RPA developers to manipulate DOM objects. DOM captures information about what a file contains for each page, with typed sections (e.g., paragraphs, tables, columns, vertical text, headers, headings, footers, etc.), typed word groups (e.g., sentences, table cells, etc.), and word level information (e.g., absolute positioning on each page, OCR confidence, content, etc.), all enhanced with rotation, skew, relative width and height information, etc..

The file digitization component may be built with a plug-and-play functionality related to the OCR engine to be used, such that any suitable OCR engine implementation respecting the defined contract enforced by the document processing framework can be used. The digitization component may also be enhanced to allow the usage of multiple OCR engines and implement a voting system to report the best combined result of all engines used. For instance, the result that the most OCR engines agree on may be selected. In certain embodiments, such results may be weighted based on the accuracy of a given OCR system. However, it should be noted that in some embodiments, the voting/OCR result aggregation algorithm may employ any type of metric or confidence computation, based or not on human feedback, to determine and report the best available textual output without deviating from the scope of the invention.

This functionality allows a user to use one or more desired classifier algorithms for document classification. Examples of classification algorithms include, but are not limited to, layout-based classification, sentiment-based classification, feature-based classification, natural language processing (NLP)-based classification, machine learning (ML)-based classification, deep learning-based classification, image-based classification, keyword-based classification, and color-based classification. The user can add any number of classifier implementations as plug-ins, delete, change, interchange, and change the order of the classifiers in some embodiments. In certain embodiments, multiple different classifiers of the same type (e.g., multiple image-based classifiers) may be used. In some embodiments, the user can define acceptance criteria based on minimum confidence thresholds for each classifier that is used.

In some embodiments, the user can perform a taxonomy mapping from a master taxonomy (e.g., one designed and defined using the taxonomy manager in some embodiments) and any internal taxonomy for a given classifier for the purpose of bringing all data to a common denominator in a unified manner. This functionality may be used when a given classifier reports an internal set of "classes" (e.g., document types) as a result of classification that should be mapped (translated) to the master taxonomy. For example, a classifier may report class "INV" when an incoming document is an invoice, while the master taxonomy may contain a document type called "Expense Invoice. " This functionality allows the specification that "INV" reported from the classifier corresponds to an "Expense Invoice" defined in the master taxonomy.

In certain embodiments, the user can decide on the priority of results from each classifier component that is used. In some embodiments, the user can also decide which classifier components are allowed to resolve certain document types and which are restricted from doing so. This functionality allows fine tuning of document classification, given the known features and limitations of each classifier implementation (i.e., component) used in a specific use case.

The document classification functionality may also ensure the same type of output regardless of the classifier(s) used based on the digitized file and the taxonomy. The output of the document classification functionality may be captured in a proprietary data structure that is defined as a class in a public package. The class may be used programmatically by both software developers and RPA developers to manipulate classification results objects. In some embodiments, these classes may be ClassifierResult and ClassificationResult defined in the public data contracts package in UiPath Studio™. Certain embodiments may include the implementation of more complex results brokerage systems between different classifier components to automatically decide which results are better, as well as a system for combining results from different classifier components for different page ranges. This functionality may be used for file splitting into multiple logical documents. For example, in the case where a single file contains a package of documents where the documents are scanned one after the other within the same PDF file.

<FIG> is a screenshot illustrating a classify document scope workflow <NUM>. Workflow <NUM> includes a keyword-based classifier activity <NUM> and a fixed form classifier activity <NUM>. A user can configure the classifiers in workflow <NUM> by clicking on a configure classifiers link <NUM>. The classify document scope component can be configured with any desired number of classifiers in some embodiments.

Clicking configure classifiers link <NUM> opens classifier configuration interface <NUM> of <FIG>, which provides a list of document types <NUM> that may be processed in the first column of the configuration table, while the following columns each correspond to one classifier used in the scope in the order in which they are configured in the scope component. In this example, a keyword-based classifier configuration pane <NUM> and a fixed form classifier configuration pane <NUM> allow a user to set a minimum confidence <NUM>, <NUM> for each. Classification results from each classifier may be filtered based on the minimum confidence threshold set for each classifier individually. The user can also select whether to apply one, both, or no classifiers to each document type via checkboxes in the classifier configuration (here, keyword-based classifier configuration pane <NUM> and fixed form classifier configuration pane <NUM>). The user can also optionally enter internal taxonomy unique IDs for document types where each classifier is using an internal taxonomy in the text fields next to the checkboxes.

In some embodiments, the classify document scope activity may allow setting of minimum confidence thresholds not only per classifier, but also at a classifier and document type level. In certain embodiments, the classify document scope activity, the classifier configuration, the prioritization algorithm, the user interfaces, the data structures, and the public contracts may experience changes and adjustments while retaining the same purpose of facilitating document classification in an open and extensible framework.

In some embodiments, a classification validation module offers a user interface for reviewing, correcting, and/or manually processing files for classification. Users may be able to review automatic classification by viewing ranges of pages from a processed file and the automatically associated document type, perform corrections at both the page range and document type level, delete or add new sections with associated document types, and/or fully manually process documents if desired.

The user interface of some embodiments is straightforward to use and offers a visual approach to understanding and making judgements about each individual page in a file. The user interface in some embodiments is available for usage independent of the classification components used in the previous automatic classification phase, providing a single, unified, state-of-the-art user interface for document classification review and processing. This approach may eliminate the need for humans to learn and use different screens for document classification dependent on the classification techniques that are employed, thus unifying the overall experience and decreasing the learning curve and overall time required for document processing.

The automatic data extraction functionality allows extractors in the workflow to be used interchangeably. Such embodiments may ensure standard inputs, standard outputs, brokerage between the extractors, and fallback mechanisms. Some examples of extractors may include, but are not limited to, template-based extractors, layout-based extractors, keyword-based extractors, regular expression-based extractors, context-based extractors, label/anchor-based extractors, pattern-based extractors, natural language processing-based extractors, machine learning extractors, metadata-based extractors, etc..

In some embodiments, the user can add any desired number of extractor implementations as plug-ins. In certain embodiments, the user may be able to delete, change, interchange, and change the order of the extractors. In some embodiments, the user can define acceptance criteria based on minimum confidence thresholds for each extractor that is used. In some embodiments, the user can define acceptance criteria based on minimum confidence thresholds for each field/extractor combination.

In some embodiments, the user can perform a taxonomy mapping from the master taxonomy (e.g., one designed and defined using the taxonomy manager in some embodiments) and one or more extractor internal taxonomies for the purpose of bringing all data to a common denominator in a unified manner. This functionality may be used when a given extractor reports an internal set of "capabilities" (i.e., fields the extractor can extract) as a result of data extraction that should be mapped (translated) to the master taxonomy. The user may decide the priority of the results from each extractor that is used. For example, an extractor may report values for a field called "INV-NO" when processing invoices, while the master taxonomy may contain a field called "Invoice Number. " This functionality allows the specification that "INV-NO" reported from the extractor corresponds to an "Invoice Number" defined in the master taxonomy.

The user may also decide which extractor components are allowed to resolve certain fields and which are restricted from doing so. These functionalities allow fine tuning of document extraction given the known features and limitations of each extractor implementation (i.e., component) used in a specific use case. The extractors may be implemented as activities in an RPA workflow in some embodiments.

<FIG> is a screenshot illustrating a data extraction scope workflow <NUM>. Workflow <NUM> includes a FlexiCapture™ extractor activity <NUM> and a fixed form extractor activity <NUM>. A user can configure the extractors in workflow <NUM> by clicking on a configure extractors link <NUM>.

Clicking configure extractors link <NUM> opens extractor configuration interface <NUM> of <FIG>. Document types and fields from the master taxonomy can be viewed in a document types and fields pane <NUM> (see also <FIG>), where fields (e.g., "InvoiceNumber") are grouped under their respective document type (e.g., "Invoice"). Extractors are applied on a per-field basis by selecting corresponding checkboxes in each extractor's pane (e.g., a FlexiCapture™ extractor pane <NUM> in this example (see also <FIG>)) and a fixed form extractor pane <NUM> (see also <FIG>). The minimum confidence percentage can also be specified via text fields <NUM>, <NUM> in the extractor-specific panes (e.g., FlexiCapture™ extractor pane <NUM> and fixed form extractor pane <NUM>, respectively).

The document extraction functionality in this embodiment ensures the same type of output irrespective of the extractor used based on the digitized file and the taxonomy. For example, FlexiCapture™ is a third party tool that has its own internal taxonomy. In order to bring every extractor to a common denominator, aliases can be added in this embodiment. For example, as shown below, the InvoiceNumber field in FlexiCapture™ extractor pane <NUM> is equal to "inv-no" in fixed form extractor pane <NUM>. This may only need to be configured once, and the output of data extraction scope may then always extract data according to the master taxonomy that the developer has defined. The output of the document extraction functionality may be captured in a proprietary data structure defined as a class in a public package that can be used programmatically by both software developers and RPA developers to manipulate extraction result objects.

In some embodiments, these classes may be ExtractorResult and ExtractionResult defined in the public data contracts package of UiPath Studio™, for example. Certain embodiments may include the implementation of more complex results brokerage systems between different extractor components to automatically decide which results are better, as well as a system for combining or chaining extractor processing from different extractor components for the same field. This functionality may be used for fine-grain data identification from larger identified sections. For example, in some embodiments, a first extractor may extract an entire sentence, while a chained second extractor may target the identification of a specific information from the sentence identified by the first extractor.

In short, the DPF of some embodiments takes a file and a classification result with page bounds, attempts to identify data for each associated field, uses the extraction algorithms included in the workflow, collects the results, and reports the best result for each field based on confidence, ordering, and extractor chaining. In the example of <FIG>, two extractors are used - FlexiCapture™ extractor <NUM> and fixed form extractor <NUM>. The document extraction module in this embodiment gives priority to a result returned by FlexiCapture™ extractor <NUM> over fixed form extractor <NUM> due to the ordering selected by the RPA developer between these two plugins in workflow <NUM>. The document extraction module may not accept any reported extraction that is below a set confidence value (e.g. <NUM>% for results reported by FlexiCapture™ extractor <NUM> if the user sets this value in minimum confidence field <NUM>), while it will accept any classification reported by fixed form extractor <NUM> if minimum confidence field <NUM> is set to <NUM>%. The document extraction module may look for an acceptable extraction performed by FlexiCapture™ extractor <NUM> and report that result, if acceptable. If no acceptable extraction is reported by FlexiCapture™ extractor <NUM>, document extraction module may look for an acceptable extraction performed by fixed form extractor <NUM>.

In some embodiments, the extraction results may be further enhanced with business-specific information or validation, specific formatting, and/or other post-processing. In certain embodiments, the output of the automatic data extraction component may be checked/corrected/enhanced using any other RPA workflow activities available in any desired combination. These activities may be used to manipulate the output of the automatic data extraction and may add, remove, or change certain values contained in the result. This may be used for complex use cases in which extracted data should be enhanced with information from a database or other sources external to a singular file processed at that time, for example.

In some embodiments, a data extraction validation module provides a user interface for reviewing, correcting, and/or manually processing data points (i.e., extracted information) reported by the data extraction module. Users may be able to review results from the automatic data extraction by: (<NUM>) viewing values, evidence, positions where the extracted value is found within the document, extracted value confidence, etc. from a processed part of a document (e.g., on certain page ranges as pertaining to classification); (<NUM>) performing corrections by editing OCR errors in a correctly reported value, correcting positioning of a reported value, replacing a reported value entirely, adding a value missed by automatic processing, and/or by removing a value wrongly identified by automatic processing; (<NUM>) directly interacting with a graphical representation of the original file being processed by performing operations through selections of range, area, word(s), etc. directly on the original file view; (<NUM>) viewing and performing operations on the text version of the document; and/or also (<NUM>) fully manually processing documents when desired.

The user interface of some embodiments is straightforward to use and offers a visual approach to understanding and making judgements about individual fields and pieces of data to be identified and extracted in a file. The user interface in certain embodiments can be made available for usage independent of the data extraction components used in the previous automatic classification and data extraction phases, providing a single, unified, state-of-the-art user interface for document data extraction review and processing. This approach may eliminate the need for humans to learn and use different screens for data validation dependent on the data extraction techniques that are employed, thus unifying the overall experience and decreasing the learning curve and overall time for document processing. The data validation component of some embodiments allows definition and use of custom validation rules that can be checked before allowing a human to submit a document processed through the document validation functionality. In this manner, the module may ensure full extensibility and be made open to custom implementations depending on the business use case to be covered.

In some embodiments, a classifier training module facilitates the completion of the feedback loop for classifiers capable of learning from human feedback. The classifier training module may ensure, in a unified manner, that human-validated data and the information that enables algorithms to learn from human feedback reach the activated classifiers in a form that is understandable by the respective classifiers (e.g., by using the taxonomy mapping functionality) and with the correct permissions enabled (e.g., by using the train classifiers activation functionality). This approach may provide a unified experience for closing the feedback loop independent of the classifier(s) that are used, and may also be independent of the classifier components used for actual file classification. This allows RPA developers, for example, to train classifiers before they are activated for actual file classification, thus enabling "on-the-fly" training and eliminating the need of large volumes of up-front annotated files specific to each classification component.

In some embodiments, an extractor training module facilitates the completion of the feedback loop for extractors capable of learning from human feedback. The extractor training module may ensure, in a unified manner, that human-validated data and information that enables algorithms to learn from human feedback reach the activated extractor(s) in a form understandable by the extractor(s) (e.g., by using the taxonomy mapping functionality) and with the correct permissions enabled (e.g., by using the train extractors field-level activation functionality). This approach may provide a unified experience for closing the feedback loop independent of which extractor component(s) are used, and may also be independent of the extractor components used for actual file processing for data extraction. This allows RPA developers, for example, to train extractors before they are activated for actual document data extraction, thus enabling "on-the-fly" training and eliminating the need of large volumes of up-front annotated files specific for each extraction component.

<FIG> is a screenshot illustrating a classifier and extractor training workflow <NUM>, according to an embodiment of the present invention. In this example, a train classifier scope activity <NUM> includes a keyword-based classifier <NUM> that can be configured by clicking a configure classifiers link <NUM>. The functionality of the classifier training configuration interface may be similar to classifier configuration interface <NUM> of <FIG>, except that no minimum confidence threshold needs to be set in some embodiments. Such a classifier training configuration interface <NUM> is shown in <FIG>. However, in certain embodiments, the classifier training configuration interface may include additional training setup parameters that may be document type and classifier-specific. Similarly, the functionality of the extractor training configuration interface may be similar to extractor configuration interface <NUM> of <FIG>, and in certain embodiments, additional training setup parameters may be included.

<FIG> is a flowchart illustrating a process <NUM> for implementing a DPF for RPA, according to an embodiment of the present invention. The process beings with defining and storing a general taxonomy at <NUM>. In some embodiments, this may include executing a taxonomy manager providing an interface facilitating the definition of a list of document types targeted for classification and data extraction, along with associated fields for each of the document types, receiving the list of defined document types and the associated fields for each of the defined document types, and storing the list of document types and the associated fields in a master taxonomy data structure.

Next, a digitization activity is executed in an RPA workflow and a text version of a file and a Document Object Model (DOM) stored in a DOM data structure are output at <NUM>. In some embodiments, the DOM includes information pertaining to typed sections, typed word groups, and word level information in the file that are enhanced with rotation, skew, and relative width and height information. In certain embodiments, the digitization activity uses a plurality of OCR engines and the digitization activity includes implementing a voting system for the plurality of OCR engines and outputting a best combined result from the plurality of OCR engines.

The file is classified into one or more document types using one or more classifiers in the RPA workflow and automatic classification information is output at <NUM>. This information may be stored in a classification data structure in some embodiments. The one or more classifiers are configured to perform layout-based classification, sentiment-based classification, feature-based classification, natural language processing (NLP)-based classification, machine learning (ML)-based classification, deep learning-based classification, image-based classification, keyword-based classification, color-based classification, any combination thereof, or any other document classification method. In certain embodiments, the classification includes using acceptance criteria based on minimum confidence thresholds for each of the one or more classifiers. In some embodiments, the classification includes mapping the master taxonomy and an internal taxonomy of a respective classifier for the one or more classifiers. In certain embodiments, the classification includes prioritizing results from each classifier based on a classifier order in the RPA workflow, selecting classifiers of the one or more classifiers for use in the classification based on the document type, assigning a minimum confidence field to at least one of the one or more classifiers, or any combination thereof.

A classification validation module providing an interface for reviewing, correcting, and/or manually processing files for classification is executed and automatic classification information is output at <NUM>. A classifier training module that facilitates completion of a feedback loop for the one or more classifiers is executed at <NUM>. Then, data is extracted from the classified document using one or more extractors in the RPA workflow and the automatically extracted data is output at <NUM>. This extracted data are stored in an extraction data structure. In some embodiments, the extraction includes prioritizing results from each extractor based on an extractor order in the RPA workflow, selecting extractors of the one or more extractors for use in the extraction based on the document type, assigning a minimum confidence field to at least one of the one or more extractors, or any combination thereof.

A data extraction validation module providing an interface for correcting, and/or manually processing data points from the data extraction is executed and confirmed extracted data is output at <NUM>. An extractor training module that facilitates completion of a feedback loop for the one or more extractors is executed at <NUM>. The processed data, which includes classification results and extracted information, is then exported at <NUM>. It should be noted that once the taxonomy is defined, one or more of steps <NUM>-<NUM> may be optional in some embodiments based on the needs of the use case.

After process <NUM> is completed, validated information is now available for usage in other systems. For instance, the information may be inserted into a database or an Excel® file, a notification with specific values may be sent via email, etc. By way of nonlimiting example, process <NUM> of <FIG> may begin with a PDF file and end with knowledge that the file is an Invoice, the Total Amount written in that Invoice is $<NUM>, the Vendor is "XYZ Bookstore," and the purchase date was July <NUM>, <NUM>. These values are now available to be programmatically inserted into an accounting system for example.

The process steps performed in <FIG> may be performed by a computer program, encoding instructions for the processor(s) to perform at least part of the process described in <FIG>, in accordance with embodiments of the present invention. The computer program may be embodied on a non-transitory computer-readable medium. The computer-readable medium may be, but is not limited to, a hard disk drive, a flash device, RAM, a tape, and/or any other such medium or combination of media used to store data. The computer program may include encoded instructions for controlling processor(s) of a computing system (e.g., processor(s) <NUM> of computing system <NUM> of <FIG>) to implement all or part of the process steps described in <FIG>, which may also be stored on the computer-readable medium.

The computer program can be implemented in hardware, software, or a hybrid implementation. The computer program can be composed of modules that are in operative communication with one another, and which are designed to pass information or instructions to display. The computer program can be configured to operate on a general purpose computer, an ASIC, or any other suitable device.

It will be readily understood that the components of various embodiments of the present invention, as generally described and illustrated in the figures herein, may be arranged and designed in a wide variety of different configurations.

The features, structures, or characteristics of the invention described throughout this specification may be combined in any suitable manner in one or more embodiments. For example, reference throughout this specification to "certain embodiments," "some embodiments," or similar language means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present invention. Thus, appearances of the phrases "in certain embodiments," "in some embodiment," "in other embodiments," or similar language throughout this specification do not necessarily all refer to the same group of embodiments and the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

It should be noted that reference throughout this specification to features, advantages, or similar language does not imply that all of the features and advantages that may be realized with the present invention should be or are in any single embodiment of the invention. Thus, discussion of the features and advantages, and similar language, throughout this specification may, but do not necessarily, refer to the same embodiment.

Claim 1:
A non-transitory computer-readable medium storing a computer program, the program configured to cause at least one processor (<NUM>) to:
automatically classify a file into one or more document types using one or more classifiers via one or more respective classification activities (<NUM>, <NUM>) in a robotic process automation (RPA) workflow for an RPA robot (<NUM>), the one or more respective classification activities of the RPA workflow configured (<NUM>) to have a user selected data classification scope, the automatic classification comprising assigning a document type and a range of pages for the file to each classification result using one or more algorithms that perform layout-based classification, sentiment-based classification, feature-based classification, natural language processing (NLP)-based classification, machine learning (ML)-based classification, deep learning-based classification, image-based classification, keyword-based classification, colour-based classification, or any combination thereof;
store results of the automatic classification in a classification data structure;
automatically extract data from the classified file using one or more extractors via one or more respective extraction activities (<NUM>, <NUM>) in the RPA workflow for the RPA robot (<NUM>), the one or more respective activities of the RPA workflow configured (<NUM>) to have a user selected data extraction scope, the automatic extraction comprising assigning identified values from within the classified file or the range of pages of the classified file to a requested field using one or more algorithms that comprise a template-based extractor, a layout-based extractor, a keyword-based extractor, a regular expression-based extractor, a context-based extractor, a label/anchor-based extractor, a pattern-based extractor, a natural language processing-based extractor, a machine learning extractor, a metadata-based extractor, or any combination thereof;
store the automatically extracted data in an extraction data structure; and
output (<NUM>) the automatically extracted data,
wherein the RPA workflow comprises the one or more respective activities (<NUM>, <NUM>, <NUM>, <NUM>) of the one or more classifiers and the one or more extractors.