Media-to-workflow generation using artificial intelligence (AI)

A robotic process automation (RPA) workflow may be automatically created from text, an image, and/or a media file. A workflow sequence may be converted into a digital format using optical character recognition (OCR), and this information may then be analyzed by an artificial intelligence (AI) model and converted into a predicted RPA workflow. The predicted RPA workflow may be presented to a developer for approval, denial, or modification. Information pertaining to the selection by the developer may then be used for subsequent retraining of the AI model to improve prediction accuracy.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims the benefit and priority under 35 U.S.C. §119 of Indian Patent Application No. 201911041765 filed on Oct. 15, 2019. The subject matter of these earlier filed applications is hereby incorporated by reference in its entirety.

FIELD

The present invention generally relates to robotic process automation (RPA), and more specifically, to using deep learning (DL) or AI to automatically create a workflow from text, images, audio, or any combination thereof.

BACKGROUND

RPA workflows may be developed in a suitable environment or application (e.g., UiPath Studio™). However, when a developer is not using the studio application to develop RPA workflows (e.g., when the developer is commuting or performing other tasks), the workflow is not initially captured in the studio application. In those instances, the developer may write out a sequence of logic steps on paper, as text in a text or document editor (e.g., Notepad™ or Microsoft Word™), or via any other suitable capture mechanism, and then manually create the workflow in the RPA workflow development application at a later time. In these cases, the effort to generate the workflow is duplicative.

There are existing solutions that provide drag and drop functionality for building RPA workflows without requiring the developer to write code. For instance, UiPath Studio™ provides such functionality. However, this still requires the developer to manually reproduce the workflow that he or she created outside of the RPA workflow development application. For example, assuming that the user has an idea of how his or her workflow should be designed and defined, the user must go to the studio application and manually drag and drop the activities one after the other including the background Sequence, Excel activity, Log message, etc. In addition to this, he or she also must identify the datatypes and define the variables to be used. This results in considerable investment of time and effort, which can be minimized.

And hence, an improved approach may be beneficial.

SUMMARY

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 RPA techniques. For example, some embodiments of the present invention pertain to automatically creating an RPA workflow from a source external to an RPA workflow development application using AI.

In an embodiment, a computer-implemented method for creating a RPA workflow includes receiving a media file comprising a proposed workflow by way of a workflow development application on a computing system, and forwarding the received media file of the proposed workflow to a workflow generation module for processing. The method may also include accessing, by the workflow generation module, a model database to pull one or more workflow models. The method may further include loading, by the workflow generation module, the one or more workflow models and predicting one or more workflows for a user of the computing system to select. The method yet also include transmitting, from the workflow generation module, a list of possible XAML files comprising the one or more workflows to the workflow development application for the user to select.

In another embodiment, a computer program embodied on a non-transitory computer-readable medium is configured to cause at least one processor to receive a media file comprising a proposed workflow by way of a workflow development application on a computing system. The computer program is further configured to cause the at least one processer to forward the received media file of the proposed workflow to a workflow generation module for processing, and access, by the workflow generation module, a model database to pull one or more workflow models. The computer program is also configured to cause the at least one processer to load, by the workflow generation module, the one or more workflow models and predicting one or more workflows for a user of the computing system to select. The computer program is further configured to cause the at least one processer to transmit, from the workflow generation module, a list of possible XAML files comprising the one or more workflows to the workflow development application for the user to select.

In yet another embodiment, a computer system includes memory storing machine-readable computer program instructions, and at least one processor configured to execute the computer program instructions. The instructions are configured to cause the at least one processor to receive a media file comprising a proposed workflow by way of a workflow development application on a computing system, and forward the received media file of the proposed workflow to a workflow generation module for processing. The instructions are further configured to cause the at least one processor to access, by the workflow generation module, a model database to pull one or more workflow models. The instructions are also configured to cause the at least one processor to load, by the workflow generation module, the one or more workflow models and predicting one or more workflows for a user of the computing system to select. The instructions are further configured to cause the at least one processor to transmit, from the workflow generation module, a list of possible XAML files comprising the one or more workflows to the workflow development application for the user to select.

DETAILED DESCRIPTION OF THE EMBODIMENTS

Some embodiments pertain to automatically creating a robotic process automation (RPA) workflow from text, images, audio, or any combination thereof. In some embodiments, using a trained AI model, the RPA workflow is created from a sequence of logic steps written in a text file, from an image containing the sequence of logic steps, from an audio file containing the sequence of logic steps, any other suitable mechanism for conveying the logic steps, or any combination thereof. The trained AI model may understand the sequence of logic steps and automatically create the RPA workflow in an extensible application markup language (XAML) file format in some embodiments. In some embodiments, the trained AI model may provide suggestions to the developer, who may have drafted the sequence of logic steps, with multiple RPA workflows that can be used by the developer in the RPA workflow development application by selecting one of the suggested RPA workflows. Some embodiments allow the developer to make use of an intelligently generated RPA workflow, reducing development time and effort in the workflow development lifecycle.

FIG. 1is an architectural diagram illustrating an RPA system100, according to an embodiment of the present invention. RPA system100includes a designer110that allows a developer to design and implement workflows. Designer110may provide a solution for application integration, as well as automating third-party applications, administrative Information Technology (IT) tasks, and business IT processes. Designer110may facilitate development of an automation project, which is a graphical representation of a business process. Simply put, designer110facilitates 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 designer110is 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 designer110, execution of business processes is orchestrated by conductor120, which orchestrates one or more robots130that execute the workflows developed in designer110. One commercial example of an embodiment of conductor120is UiPath Orchestrator™. Conductor120facilitates management of the creation, monitoring, and deployment of resources in an environment. Conductor120may act as an integration point with third-party solutions and applications.

Conductor120may manage a fleet of robots130, connecting and executing robots130from a centralized point. Types of robots130that may be managed include, but are not limited to, attended robots132, unattended robots134, development robots (similar to unattended robots134, but used for development and testing purposes), and nonproduction robots (similar to attended robots132, but used for development and testing purposes). Attended robots132are triggered by user events and operate alongside a human on the same computing system. Attended robots132may be used with conductor120for a centralized process deployment and logging medium. Attended robots132may help the human user accomplish various tasks, and may be triggered by user events. In some embodiments, processes cannot be started from conductor120on this type of robot and/or they cannot run under a locked screen. In certain embodiments, attended robots132can only be started from a robot tray or from a command prompt. Attended robots132should run under human supervision in some embodiments.

Unattended robots134run unattended in virtual environments and can automate many processes. Unattended robots134may be responsible for remote execution, monitoring, scheduling, and providing support for work queues. Debugging for all robot types may be run in designer110in 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.).

Conductor120may 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 robots130and conductor120(e.g., a web application). Deployment may include assuring the correct delivery of package versions to assigned robots130for 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). Conductor120may provide interconnectivity by acting as the centralized point of communication for third-party solutions and/or applications.

Robots130are execution agents that run workflows built in designer110. One commercial example of some embodiments of robot(s)130is UiPath Robots™. In some embodiments, robots130install the Microsoft Windows® Service Control Manager (SCM)-managed service by default. As a result, such robots130can open interactive Windows® sessions under the local system account, and have the rights of a Windows® service.

In some embodiments, robots130can be installed in a user mode. For such robots130, this means they have the same rights as the user under which a given robot130has 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 robot130may be configured in an HD environment.

Robots130in 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 conductor120and the execution hosts (i.e., the computing systems on which robots130are executed). These services are trusted with and manage the credentials for robots130. 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 conductor120and the execution hosts. User mode robot services may be trusted with and manage the credentials for robots130. 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 robots130split 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 designer110may also be independent of browser zoom level sin some embodiments. For applications that are DPI-unaware or intentionally marked as unaware, DPI may be disabled in some embodiments.

FIG. 2is an architectural diagram illustrating a deployed RPA system200, according to an embodiment of the present invention. In some embodiments, RPA system200may be, or may be a part of, RPA system100ofFIG. 1. 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 application210includes executors212, an agent214, and a designer216. However, in some embodiments, designer216may not be running on computing system210. Executors212are running processes. Several business projects may run simultaneously, as shown inFIG. 2. Agent214(e.g., a Windows® service) is the single point of contact for all executors212in this embodiment. All messages in this embodiment are logged into conductor230, which processes them further via database server240, indexer server250, or both. As discussed above with respect toFIG. 1, executors212may 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 2012), multiple robots may be running at the same time, each in a separate Windows® session using a unique username. This is referred to as HD robots above.

Agent214is 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 agent214and conductor230is always initiated by agent214in some embodiments. In the notification scenario, agent214may open a WebSocket channel that is later used by conductor230to send commands to the robot (e.g., start, stop, etc.).

On the server side, a presentation layer (web application232, Open Data Protocol (OData) Representative State Transfer (REST) Application Programming Interface (API) endpoints234, and notification and monitoring236), a service layer (API implementation/business logic238), and a persistence layer (database server240and indexer server250) are included. Conductor230includes web application232, OData REST API endpoints234, notification and monitoring236, and API implementation/business logic238. In some embodiments, most actions that a user performs in the interface of conductor230(e.g., via browser220) 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 application232is the visual layer of the server platform. In this embodiment, web application232uses 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 application232via browser220in this embodiment in order to perform various actions to control conductor230. 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 application232, conductor230also includes service layer that exposes OData REST API endpoints234. However, other endpoints may be included without deviating from the scope of the invention. The REST API is consumed by both web application232and agent214. Agent214is 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 conductor230. 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 monitor web application232and agent214. Notification and monitoring API236may be REST endpoints that are used for registering agent214, delivering configuration settings to agent214, and for sending/receiving notifications from the server and agent214. Notification and monitoring API236may also use WebSocket communication in some embodiments.

The persistence layer includes a pair of servers in this embodiment—database server240(e.g., a SQL server) and indexer server250. Database server240in this embodiment stores the configurations of the robots, robot groups, associated processes, users, roles, schedules, etc. This information is managed through web application232in some embodiments. Database server240may manages queues and queue items. In some embodiments, database server240may store messages logged by the robots (in addition to or in lieu of indexer server250).

Indexer server250, which is optional in some embodiments, stores and indexes the information logged by the robots. In certain embodiments, indexer server250may be disabled through configuration settings. In some embodiments, indexer server250uses 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 server250, where they are indexed for future utilization.

FIG. 3is an architectural diagram illustrating the relationship300between a designer310, activities320,330, and drivers340, according to an embodiment of the present invention. Per the above, a developer uses designer310to develop workflows that are executed by robots. Workflows may include user-defined activities320and UI automation activities330. Some computer vision (CV) activities 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 designer310. However, any number and/or type of activities may be available without deviating from the scope of the invention.

UI automation activities330are 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 activities330facilitate these interactions via drivers340that allow the robot to interact with the desired software. For instance, drivers340may include OS drivers342, browser drivers344, VM drivers346, enterprise application drivers348, etc.

Drivers40may 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 drivers340.

FIG. 4is an architectural diagram illustrating an RPA system400, according to an embodiment of the present invention. In some embodiments, RPA system400may be or include RPA systems100and/or200ofFIGS. 1 and/or 2. RPA system400includes multiple client computing systems410running robots. Computing systems410are able to communicate with a conductor computing system420via a web application running thereon. Conductor computing system420, in turn, is able to communicate with a database server430and an optional indexer server440.

With respect toFIGS. 1 and 3, 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. 5is an architectural diagram illustrating a computing system500configured to automatically generate an RPA workflow, according to an embodiment of the present invention. In some embodiments, computing system500may be one or more of the computing systems depicted and/or described herein. Computing system500includes a bus505or other communication mechanism for communicating information, and processor(s)510coupled to bus505for processing information. Processor(s)510may 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)510may 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)510may 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 system500further includes a memory515for storing information and instructions to be executed by processor(s)510. Memory515can 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)510and may include volatile media, non-volatile media, or both. The media may also be removable, non-removable, or both.

Additionally, computing system500includes a communication device520, such as a transceiver, to provide access to a communications network via a wireless and/or wired connection. In some embodiments, communication device520may 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), 802.11x, Wi-Fi, Zigbee, Ultra-WideBand (UWB), 802.16x, 802.15, Home Node-B (HnB), Bluetooth, Radio Frequency Identification (RFID), Infrared Data Association (IrDA), Near-Field Communications (NFC), fifth generation (5G), 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 device520may 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)510are further coupled via bus505to a display525, 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, a4K 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. Display525may 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 keyboard530and a cursor control device535, such as a computer mouse, a touchpad, etc., are further coupled to bus505to 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 display525and/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 system500remotely via another computing system in communication therewith, or computing system500may operate autonomously.

Memory515stores software modules that provide functionality when executed by processor(s)510. The modules include an operating system540for computing system500. The modules further include a workflow generation module545that is configured to perform all or part of the processes described herein or derivatives thereof. Computing system500may include one or more additional functional modules550that 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.

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.

RPA Workflow Generation

RPA workflows may be created based on a sequence of logic steps. This sequence may be written and stored in a text file in some embodiments, however, any other suitable mechanism for capturing the steps may be used without deviating from the scope of the invention. For example, when the sequence of logic steps is written on a piece of paper, the sequence may be captured in an image. In another example, the sequence may be spoken and recorded/captured in an audio file. In these examples, the sequence(s) may include flowcharts, block diagrams, or both.

In some embodiments, a developer or any other individual interested in creating automated processes writes the sequence in a text designer module of the RPA workflow development application. However, any desired text editor or document editor, such as a MS Word®, may be used without deviating from the scope of the invention. When a third party text editor or document editor is used, a copy of the text may be pasted in the text designer module of the RPA workflow development application to create the RPA workflow.

In certain embodiments, the developer writes a sequence, a flowchart, and/or a block diagram on a piece of paper. An image of the sequence, the flowchart, and/or the block diagram is then captured by a photo application, an image capture module associated with the RPA workflow development application, or via any other suitable mechanism without deviating from the scope of the invention. In some embodiments, the image capture module may include an option to upload the captured image. In certain embodiments, an application may capture audio in addition to or in lieu of the image.

Upon uploading a captured image and/or uploading the audio file, an RPA workflow generation module of the RPA workflow development application may perform OCR on the captured image and/or perform speech-to-text recognition on the captured audio file to convert this information into a digital format. The RPA workflow generation module may then create an RPA workflow based on a trained AI model. The trained AI model may provide the RPA developer with one or more workflows and may allow the developer to select one or more of these workflows.

The AI model is trained on XAML file dataset, in some embodiments. XAML files are essentially contain workflows. The input data is passed in a common format (explained in the preprocessing section) to the AI model. The AI model may consume this input and then predict the respective activities. The format of this predicted output is a XAML file.

If the workflow generated by the RPA workflow development application is not suitable for the developer, he or she can reject the workflow in some embodiments. In certain embodiments, the developer may reject the workflow by selecting another workflow. The RPA developer's selection(s) may be used as feedback for the workflow generation module to retrain itself and recommend better workflows in the future. In some embodiments, a batch of selection information may be collected over a time period, and this information may then be used for retraining.

The workflow generation module may thus be further improved over time, and may generate more accurate and powerful workflows as it is retrained. In certain embodiments, the developer may use this approach to build a full-fledged workflow or build simple sequences of activities.

FIG. 6is a flow diagram illustrating a system600for automatically generating RPA workflows, according to an embodiment of the present invention. In some embodiments, the RPA developer or workflow creator uploads text file(s), image file(s), audio file(s), or any combination thereof (collectively, the “file”) containing a sequence of logic steps to RPA workflow development application602. RPA workflow development application602then converts the file containing the sequence to a digital format, or has the file converted to a digital format by a third party OCR application, and sends the digital format to a model server604for preprocessing.

Preprocessing is performed based on the following media types:Image: Extract text using OCR engine.Speech: Use a speech-to-text engine to convert the audio into a text file.Text: Along with the above output and text input will be further preprocessed like the removal of stop-words, punctuation, etc.

Model server604may predict the workflow using the AI model(s) stored in model database610. From the above pre-processing step, text is fed into the AI model, which has been trained in a supervised manner with the annotated workflow dataset (XAML format). For the respective text input, the AI model extracts the features and predicts the workflow with certain probability scores. Based on these probability scores, the workflow is generated and presented to the user.

After predicting the workflow(s) (e.g., identifying one or more workflows that appear to be candidates with at least a minimum confidence threshold in some embodiments), model server604returns the predicted RPA workflow(s) to RPA workflow development application602. In some embodiments, the predicted RPA workflow(s) are provided as a set of XAML files. Based on the prediction probability scores, one or more workflows are generated. In an embodiment, the user can set the probability score threshold, and if more than one workflow matches the threshold, separate workflows are presented to the user. Even in the case where the input is ambiguous, the AI model, based on the previous learnings, can produce multiple workflows and present the workflows to the user.

RPA workflow development application602may enable the developer to use the predicted workflow(s). Additionally or alternatively, in some embodiments, the developer may modify the predicted workflow(s) or reject the predicted workflow(s) via RPA workflow development application602. If the predicted workflow(s) is/are approved or modified by the developer, RPA workflow development application602may send the approved or modified workflow to training database606for storage. Training database606may forward the stored approved or modified RPA workflow to training module608so the workflow can be used for retraining the AI model in the future.

If the predicted workflow does not meet the user's use case, he or she can correct the workflow as per his requirement and then use the upload button in the designer tab to feed the corrected workflow as an input training data to the AI model for retraining purpose. The workflows that are uploaded as a part of feedback are annotated further before retraining the model. Once the model is retrained, it will be evaluated against a validation dataset, and if the metrics are better than the previous model, the updated model will be pushed into the model database.

After retraining occurs, the retrained model of the approved or modified workflow is then stored in model database610for subsequent use.

In certain embodiments, model database610stores AI models and associated information. In some embodiments, AI models and associated information may be defined as the files that contain learned features obtained after the training process

Metrics module612may contain the standard metrics used in the AI domain like precision, recall and f1 score, etc. These metrics are given an insight about the performance and efficiency of the AI model.

FIG. 7is flow chart illustrating an RPA workflow generation process700, according to an embodiment of the present invention. In some embodiments, the developer uploads text file(s), image file(s), audio file(s), or any combination thereof (collectively, the “file”) containing a sequence of logic steps via an RPA workflow development application at702. In some embodiments, a specified text editing tool, imaging tool, and/or audio tool may be incorporated within the RPA workflow development application in order to facilitate uploading of the file.

At704, once the input file is uploaded, the file is sent to a workflow generation module (or “model”). The workflow generation module is responsible for pre-processing the file, loading the AI model, and making the prediction. In some embodiments, if the sequence is captured in an image file, the workflow generation module performs OCR, or causes OCR to be performed, to capture the text. In certain embodiments, if the sequence is captured in an audio file, the workflow generation module performs speech-to-text recognition, or causes speech-to-text recognition to be performed, to capture the spoken text. Once the text has been captured, the text may then be processed to predict the workflow.

At706, the workflow generation module accesses the model database to pull the most recent workflow model(s). This will be the AI model, which has the best scores till now, which will be used for prediction. The comparison here is with the scores of the previously trained models with the current model. The workflow model(s) may be pulled from the workflow model inventory, which stores the previously stored workflow models.

At708, the workflow generation module loads the workflow model(s), and predicts the workflow using the preprocessed text from step704. At710, the workflow generation module transmits a list of possible XAML files to the RPA workflow development application based on the prediction(s). At712, the RPA workflow development application displays the list of possible XAML files for the developer to select. Workflows are generated and downloaded in a separate folder inside the project scope, which can be viewed in the UiPath Studio Project™ view, for example. Clicking on the file opens the file in the UiPath Studio Project™. The list of possible XAML files pertain to different workflows available for selection by the developer. At714, the RPA workflow development application receives the selection from the developer, producing the selected workflow.

In text-to-workflow generation, in some embodiments, the developer opens the RPA workflow development application and selects a text editor thereof. Within the text editor, the developer may write a sequence of logic steps in some embodiments. See, for example,FIG. 8, which is a graphical user interface (GUI)800illustrating a sequence of logic steps in the form of text to be converted into an RPA workflow, according to an embodiment of the present invention. It should be appreciated that in some embodiments, the developer may write the sequence of logic steps in a text or document editor. In such embodiments, the developer may copy-and-paste the steps into the text editor of the RPA workflow development application.

After entering the text in the text editor, the developer may click on a “GENERATE” button, for example. This triggers the AI model to build the workflow using the text and return the generated workflow in XAML format. See, for example,FIG. 10, which is an image900illustrating the RPA workflow created from the text ofFIG. 8, according to an embodiment of the present invention. The generated RPA workflow may then be shown to the developer in the RPA workflow development application. After the RPA workflow is generated, the developer may use the workflow, reject the workflow, or modify the workflow in some embodiments. See, for example,FIGS. 11-13, which are images1100,1200, and1300illustrating the workflow as received prior to modification, during modification when user is deleting an item from the workflow, and after modification of the workflow is completed by the user. This feedback may be captured and stored for subsequent retraining of the AI model to generate more efficient and personalized workflows.

In image to workflow generation, in some embodiments, the RPA developer opens the RPA workflow development application and selects an image designer module. An image of the workflow having a sequence of text, a flowchart, a block diagram, etc. may be uploaded via the image designer module. See, for example,FIG. 9, which is an image1000illustrating an example workflow written on paper, according to an embodiment of the present invention. In this embodiment, image1000is fed into an AI model that processes the image using OCR, or causes OCR to be performed. The AI model may understand the requirements and logic, and map the components shown in the image into RPA activities to generate an RPA workflow. After the RPA workflow is built, the RPA developer may choose to use the workflow, reject the workflow, or modify the workflow in some embodiments. This feedback may be captured and stored for subsequent retraining of the AI model to learn about developer's style and how the developer creates the workflows.

The AI model can process a human-drawn image or a machine-drawn image to generate an RPA workflow in some embodiments. This allows the developer to draw an image on a piece of paper or create it in another software application when he or she is commuting or otherwise does not have access to a computing system with an RPA workflow development application to create the RPA workflow. However, it should be noted that in some embodiments, the RPA workflow development application may be on a mobile device, and the user may thus have access to the application wherever he or she is. As mentioned above, when the button to create the workflow is selected in some embodiments, the workflow is be created.

The RPA workflow development application of some embodiments may include or have access to a speech-to-text module. In such embodiments, the developer records an audio recording that pertains to an RPA workflow using a recording device (e.g., a mobile phone or laptop computer with a microphone). Once the developer has access to the RPA workflow development application, the developer may upload the audio file into an audio-to-workflow module, for example, and select a “create workflow” button. Internally, the audio file may be processed using the speech-to-text module. This module may convert the speech into text and then consumes the text in the same way as mentioned above in the other embodiments.

One or more embodiments decrease the time required for RPA developers to reproduce RPA workflows generated outside of an RPA workflow development application. The developer may write workflows in text format in a text or document editor application, write a flowchart on paper, etc. In such embodiments, there may be no predefined syntax as to how the sequence of logic steps are written. Further, some embodiments do not require the developer to have knowledge regarding the modules in the RPA workflow development application as he or she can produce the workflow idea in a text format. The model may be customized to fit the developer's preference and needs in some embodiments.

The process steps performed inFIGS. 6 and 7may be performed by a computer program, encoding instructions for the processor(s) to perform at least part of the process(es) described inFIGS. 6 and 7, 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)510of computing system500ofFIG. 5) to implement all or part of the process steps described inFIGS. 6 and 7, 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.