Patent Description:
The present invention generally relates to robotic process automation (RPA), and more specifically, to screen response validation of robot execution for RPA.

RPA robots may interact with computing systems in a manner similar to users. For instance, robots may move the mouse, enter text, click buttons, etc. However, unlike human users, RPA robots cannot "see" what the computing system is doing responsive to their interactions, and RPA robots may proceed with their logic even though the computing system is not responding as intended. Thus, an improved approach to monitoring and verifying RPA robot interactions with the computing system may be beneficial.

<CIT> discloses a system for automatically testing an application system graphical user interface (GUI) including first and second application servers communicatively connected across a communication network. The second application server retrieves a GUI page provided by the first application server as part of the application system, and identifies text elements and user input objects in an image of the retrieved GUI page. Each user input object is then associated with a text element. Test parameter values are retrieved from a database storing test parameter data, and the application system is tested. In particular, for each user input object of the GUI page, a respective test parameter value is provided that is associated in the database with a same text element as is associated with the user input object. A response of the application system is then monitored.

<CIT> discloses a system for generating a set of robot commands uses user entry events in a user interface. Such a system may include an event queue to which the events are sent from the user interface and a RobotCreator tool for receiving the events as those events are submitted to the event queue. The event queue is configured to allow receipt of the events by the RobotCreator. The RobotCreator tool converts the events into robot commands.

<CIT> discloses a screen identification engine to identify a screen(s) of a test run, an element identification engine to selectively identify an element(s) among available elements of the screen(s), and a signature building engine to build a signature corresponding to the test run. The signature incorporates the screen(s) and element(s) to capture an application flow of the test run, while excluding available elements that do not correspond to the application flow.

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 RPA technologies. For example, some embodiments of the present invention pertain to screen response validation of robot execution 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:.

Unless otherwise indicated, similar reference characters denote corresponding features consistently throughout the attached drawings.

Some embodiments pertain to screen response validation of robot execution for RPA. Some embodiments recognize whether text, screen changes, images, and/or other expected visual actions occur in an application executing on a computing system that an RPA robot is interacting with. Such embodiments may look for where on the screen associated with a computing system the robot has been typing and provide the physical position on the screen based on the current resolution (e.g., in coordinates) of where one or more characters, images, windows, etc. appeared. The physical position of these elements may allow determination of which field(s) the robot is typing in and what the associated application is for the purpose of validation that the application and computing system are responding as intended. The lack of characters, images, windows, etc. appearing, or these elements appearing in the wrong place or having the wrong type, may be determined from this analysis, and the robot can stop and throw an exception, go back and attempt the intended interaction again, restart the workflow, or take any other suitable action without deviating from the scope of the invention if the application on the computing system is not implementing the functionality of the workflow.

Some embodiments are implemented in a feedback loop process that continuously or periodically compares the current screenshot to the previous screenshot, or compares a portion of a current screenshot to a portion of the previous screenshot (e.g., a portion of a screenshot associated with a visible application window), to identify whether changes occurred, and if so, what the changes were. In certain embodiments comparing portions of screenshots associated with windows, when the robot is an attended robot working alongside the user, the algorithm may accommodate for changes to the location of the window, the size of the window, the shape of the window, the zoom variable for the window, a combination thereof, etc., such that the algorithm recognizes that a current portion of a screenshot maps to a previous portion of a screenshot.

In the case of typed text validation, location(s) where visual changes occurred on the screen may be identified and optical character recognition (OCR) may be performed on the location(s) where the change occurred. Results of the OCR may then be compared to the content of a key queue (e.g., as determined by key press events generated by an RPA robot) to determine whether a match exists. The locations where the change occurred is determined by comparing a box of pixels from the current screenshot to a box of pixels in the same location from a previous screenshot.

Certain embodiments may be employed for robotic process automation (RPA). <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, 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, or one of the aggregation points, 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, versioning, 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. Versioning may include management of unique instances of some process or configuration in some embodiments. 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>), 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.

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 perform screen response validation of robot execution 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 <NUM>. 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 an RPA robot validation 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.

<FIG> and <FIG> are flowcharts illustrating a process <NUM> for screen response validation of robot execution for RPA, according to an embodiment of the present invention. Key press recording <NUM> and screen recording <NUM> (e.g., recording screenshots and comparing in real time, screen video recording of a longer series of screenshots potentially for later processing by a server, etc.) are performed to determine the keys that were "pressed" by the robot (e.g., key press events caused in the computing system by the robot, application-level API calls, etc.) and the location(s) on the screen where graphical changes occurred, respectively. However, in some embodiments, key press recording is not performed since the text that the robot was seeking to enter may be determined from the robot's activities. Although characters entered by the robot are called "key presses" herein, and step <NUM> is called "key press recording", the RPA robot does not actually type on a physical keyboard. Rather, the RPA robot generates system and/or application-level events that are the same as or similar to those that would be generated by an application, an operating system, or both, due to a user physically typing on the keyboard. In some real time or near-real time embodiments, video data may not be recorded and stored since frame differences may be calculated on the fly.

Key press recording <NUM> may be performed by monitoring key press events from the operating system. However, any key press event or functionality providing key press information for any programming language and any operating system (e.g., mobile, PC, Mac, etc.) may be used without deviating from the scope of the invention.

The key press event may include information regarding which character is associated with the key that was pressed (e.g., the letter "a", the number "<NUM>", the "%" sign, etc.), the time that the key press event occurred, etc. A queue of key characters (e.g., a first in - first out (FIFO) queue) may be stored for a time window (e.g., <NUM> milliseconds (ms), one second, etc.) to account for delays between when a key was pressed by the robot and when the corresponding character appears on the screen. The time window is usually longer than the typical time delay between when a robot presses a key and when the key appears on the screen (e.g., a <NUM> character appearance delay and a <NUM> buffer window).

The queue may also serve the purpose of capturing multiple characters that appear on the screen all at once, as often happens due to the speed at which robots operate due to fast modern computing hardware. For instance, if the robot presses "abc" in vary rapid succession (e.g., within <NUM>), but only <NUM> frames per second are captured (i.e., one frame every <NUM>), the text "abc" may appear all at once in the next screenshot. By having "a", "b", and "c" in the queue, the algorithm may search for each of these characters and/or their sequences when text recognition finds these characters and/or sequences. For instance, in some embodiments, if the robot types "abc" and "ab" appears in the next frame, it may be assumed that the order in the key press queue is the same as what appears on the screen.

In certain embodiments, the robot may provide a delay between key presses. For instance, the robot may create a key press event, wait <NUM>, create another key press event, etc. This may cause the robot to have more user-like interactions with the computing system, and may also assist in the validation since changes from one screenshot to another may be less extreme and closer to human interaction speeds. Additionally or alternatively, the robot may wait a sufficient time between text being entered and "pressing" a submit button, a send email button, etc. in order to allow the changes to appear in a screenshot for validation before they disappear after submission.

Screen recording <NUM> may be performed by any suitable hardware, software, or any combination thereof without deviating from the scope of the invention. For instance, video recording may be performed by an external video camera, an internal graphics card, a remote computing system monitoring a video stream from the display (via a connected computing system or not), etc. The recorded screenshots may be stored in any desired format, whether pixel perfect or not (e.g., JPEG, BMP, PNG, etc.).

In one implementation, screenshots are stored as BitmapData in <NUM>-bit color depth in Format 16bppRgb555 format. Compressing screenshots to a lossy format may reduce accuracy since changes in one or more pixels may change a color map that is propagated to the entire image in some formats, affect the color depth, decrease/shift the overall detail, or affect image pixels by the existence of compression artifacts (e.g., in JPEG format). To accommodate for this, tolerances may be used. Screen recording <NUM> produces frames at a certain frame rate, which may vary depending on current processing load of the computing system (e.g., <NUM> frames per second).

The current screenshot frame and the immediately previous frame are then compared to one another to determine whether differences therebetween exist at <NUM> (e.g., at least some of the corresponding pixels therein differ from one another). Based on the current action(s) the robot is taking in the workflow, corresponding screen changes may or may not be expected. In the case that a screen change did not occur at <NUM> and this was expected at <NUM> based on the workflow execution, the process advances to the next screenshot at <NUM> and returns to comparing this next screenshot with the former current screenshot at <NUM>.

If no screen change occurred at <NUM> and a change was expected at <NUM>, or if a screen change occurred at <NUM> but no change was expected at <NUM>, the system throws an exception or the robot is instructed to take a remedial action at <NUM>. In the case of an exception being thrown, the operation of the robot may be stopped and a message may be displayed on the screen or sent to a user of the computing system (e.g., via text or email) indicating that the robot failed to achieve the desired action(s). In certain embodiments, the action(s) that failed may be provided to the user so he or she can attempt to troubleshoot the problem, or the user can report the issue to be fixed by an RPA developer.

Rather than throwing an exception, another option is to have the robot take remedial action. The robot may be informed which step(s) of the workflow failed, take some corrective measure, and attempt the step(s) again. This may include, but is not limited to, the robot checking at the driver level whether a window for an application the robot is attempting to interact with is the active window, checking the location of the mouse pointer or caret relative to the pertinent graphical element, checking whether the correct graphical element is the active element, any combination thereof, etc. The robot may set the correct window to the active, focused window, set the focus to the desired active element, move the mouse and click, etc., and then attempt the step(s) again. If the remedial action succeeds, the process may return to step <NUM>. However, if the remedial action fails, an exception may be thrown.

If a screen change did occur at <NUM>, and the change was expected at <NUM> based on the workflow, the process proceeds to <FIG>. Regions where the screen changed are determined and characters in the regions (if any) are determined at <NUM>. In certain embodiments, this may include determining that a desired window appeared or disappeared, determine that an image appeared or changed, determining that a graphical component appeared or changed (e.g., a text box, a text field, a table, etc.), a combination thereof, etc. Such changes may be determined using CV, for example.

In certain embodiments, multiple frames may be used to further increase accuracy. For instance, if there is varying desynchronization between when a character is typed and when it appears on the screen (e.g., varying from <NUM> to <NUM> to <NUM>, etc.), using multiple frames may assist in identifying typed text. Location(s) of the visual changes may then be isolated, and an algorithm is run on the location where the change occurred to recognize characters. This algorithm may use OCR, pixel region comparisons against Boolean array maps of characters in certain fonts, etc. In some embodiments, character recognition is only run on the relatively small regions where changes occurred are isolated and analyzed, and the remaining regions are discarded. This helps to ensure that the algorithm can run in real time on computing systems where running OCR on the entire screen (e.g., a <NUM> × <NUM> pixel resolution) may be too computationally expensive for the computing system to keep up with the speed at which characters appear on the screen. However, for computing systems that have sufficient processing power, the entire screen may be analyzed without parsing out regions where no change occurred first.

Per the above, in certain embodiments, rather than being pixel perfect, video frame comparison computations use a tolerance. Color intensities, brightness, and/or other factors may be considered the same if they fall within a certain tolerance. For instance, pixels may be considered to be the same if changes in one or more of their values are less than a certain number, a certain percentage, etc. A change in red, green, blue, and brightness by less than <NUM>, less than <NUM>%, etc. may be considered to be the same. In certain embodiments, one or more of these variables may have different tolerances. For instance, perhaps brightness changes may need to be larger or smaller than color changes to be indicative of true pixel changes. Fuzzy image matching may be performed in certain embodiments to identify similarities/differences.

In some embodiments, fuzzy image matching takes into account brightness, image templates, edge comparisons, binarization, downscale and bit reduction, dilation, applies kernel blurring, a combination thereof, etc., to more accurately identify matches. Pixel-to-pixel RGB matching that applies a tolerance to RGB values may be used so close values that are not exactly the same may be identified as matches. Bit depth and/or color scale may be reduced and pixel-to-pixel RGB or grayscale matching may be applied. Edges from images may be detected and compared. Binarization may be applied to images (e.g., binary threshold, Otsu threshold, adaptive threshold, etc.) and pixel-to-pixel matching may be applied on binary images. The scale of images may be reduced and pixel-to-pixel matching may be performed. Dilatation of images may be performed and pixel-to-pixel matching may then be applied. Key points may be extracted from images (e.g., maximally stable extremal region (MSER) descriptors) and the extracted key points may be compared using feature matchers (e.g., brute force matching, k-nearest neighbors (kNN) matching, etc.).

There are various reasons that tolerance-based computations may be beneficial. For instance, if the image is compressed after a frame is captured, tolerance should be involved in the computations since lossy compression can affect pixel values. Also, the original visual source may be compressed before capture using lossy compression (e.g., when a virtual computing system is launched via an emulator and the emulator compresses the virtual computer screen content). This may occur because the images are broadcast from a remote machine (e.g., a server) to the local computing system.

Once the characters of the screen region(s) where changes occurred and/or other graphical changes occurred are identified at <NUM>, the characters are compared against the queue of stored characters corresponding with key press events. If a match is found, the screen coordinates of the match location are extracted at <NUM>, as well as the screen coordinates of other detected graphical changes. In some cases, the character recognition algorithm may fail to recognize a character on the screen for what it actually is. For instance, the OCR algorithm may recognize the letter "O" on the screen as the number "<NUM>". In that case, in some embodiments, the algorithm tracks the location of the caret on the screen. This may be determined by comparing image patterns of various caret shapes to the screen, using image detection (e.g., CV), etc. In some embodiments, the algorithm may account for a caret blinking, if it does so.

In certain embodiments, fuzzy matching may be used to compare OCR results to characters in the queue. Fuzzy matching logic may recognize that the letter "O" looks similar to the number "<NUM>" and may identify these characters as a match. If there are no other similar characters in the queue, the match may be confirmed.

In certain embodiments, caret tracking is performed. Analysis of the changed region(s) of the image may be performed to create a list of candidates that may correspond to the caret (e.g., the candidates appear as a thin vertical line or something similar). Validation may be performed to identify that a candidate is blinking over time, and the true caret may then be identified. Further validation may be performed to verify that the caret appears within a graphical element capable of text entry (e.g., a text box, a word processor document, a text field, etc.).

If no changes occurred elsewhere on the screen, or other changes match characters in the queue besides the one that is missing, the algorithm may then infer that because this is the only unidentified change, it must be the missing character. The algorithm may then infer that the recognized letter "O" is actually an otherwise unidentified "<NUM>" in the character queue, for instance, and extract the screen coordinates of the match location at <NUM>. This may improve the accuracy of the algorithm.

In some embodiments, characters may be removed from the queue when characters or a character sequence are found on the screen and uniquely identified, after a predetermined time elapses (e.g., <NUM>, one second, etc.), pop off characters at the end of the queue based on a queue of a predetermined size (e.g., <NUM> characters), etc. In order to remove characters falling outside a time window, the queue may store variables having the character that was typed and a time stamp. The algorithm may periodically compare the time stamps of key press variables in the queue (potentially beginning with the "first in" end) to the current time. If a key press variable is found in the queue that is older than the time window, the variable may be removed. In certain embodiments, once a key press variable is found that falls within the time window, it may be assumed that all other variables in the queue are within the time window, and processing may stop for that iteration.

After the coordinates of the screen region with the recently typed characters and/or identified graphical elements are extracted at <NUM>, the coordinates are compared to running application(s) and the active element is determined based on which element the extracted coordinates fall under at <NUM>. It is possible that the coordinates do not correspond with any potential active elements in the graphical application, or the coordinates correspond with the wrong active element. It is also possible that entered text is incomplete or wrong, or a desired screen change did not occur. Accordingly, validation is performed at <NUM> by checking the corresponding robot activity or activities in the workflow and ensuring that the active element, the entered text, and/or other desired screen changes occurred. This may include, but is not limited to, determining an action the robot is to take in a corresponding activity or series of activities (e.g., selecting a field, entering text into the field, clicking a button, etc.), determining that a screen change should occur based on an activity or series of activities (e.g., a new application window is expected to open, the application window is expected to change to a new format, certain text is expected to be presented by the application, etc.), or any other suitable validation action(s) without deviating from the scope of the invention. The validation may be performed by the robot itself, by another application or robot monitoring the robot's execution, or both. If the validation succeeds at <NUM>, the process proceeds to the next screenshot in step <NUM> of <FIG>. However, if the validation fails (e.g., the robot was supposed to enter "$<NUM>,<NUM>" in a field for an invoice, but instead this appeared in a "Company Name" field, or the entered value was wrong because a character was missed (e.g., "$<NUM>,<NUM>")), the process proceeds to throwing an exception taking remedial action at step <NUM> of <FIG>.

In some embodiments, the screenshot recording, key press recording, and/or processing thereof are performed by an RPA robot. In certain embodiments, a recorder application records the robot activity as screenshots or video, records a series of key presses, and saves this information for later processing or passes this information to another application running on the computing system or another computing system for real time or near-real time processing. CV may be applied prior to processing of the video and key presses, immediately after processing of the video and key presses, or applied later, to provide a set of recognized graphical elements including their bounding rectangles. Then, if an intersection is found between a graphical element bounding rectangle and coordinates of the caret/text, the particular element may be considered to be currently active, or "focused" (i.e., the "active element").

In some cases, changes to the screen from one frame to the next may be substantial. For instance, when a robot closes a window, the majority of the screen may change. Therefore, in some embodiments, a change threshold is determined and applied to determine whether to compare time-adjacent screenshots at all (e.g., more than <NUM>% of the screen changed, more than <NUM>%, <NUM>%, <NUM>%, <NUM>%, <NUM>%, etc.). When this threshold is met or exceeded, the frame comparison process may be skipped until the difference between two time-adjacent frames is below the threshold.

In some embodiments, the robot may pause after workflow steps that cause changes to the screen of the computing system until confirmation is received. This prevents the robot from running on to the next step, and the next, and the next, when a previous step failed. Thus, erroneous operation may be quickly identified and discontinued.

The monitoring and robot validation process may be performed by the same robot that is attempting to control the computing system in some embodiments. This provides a mechanism for the robot to validate its own operation. In certain embodiments, another robot operating on the same computing system, or operating on a different computing system (e.g., a server) receiving the video and key press information remotely, may perform the monitoring and validation. In some embodiments, a local or remotely operating non-robot application performs the monitoring and validation. In certain embodiments, the application or robot performing the analysis of the video and key presses and the robot or application performing the validation are different.

<FIG> is an architectural diagram illustrating a process <NUM> for screen response validation of robot execution for RPA, according to an embodiment of the present invention. Screen recording <NUM> is performed to produce frames N <NUM>, N - <NUM><NUM>, N - <NUM><NUM>, and N - <NUM><NUM>. Frames may be the entire screen, a window associated with a running application, etc. In certain embodiments, frame sets for multiple running windowed applications may be stored and compared, or these frames may be pulled from screenshots. Key press event recording or robot activity text retrieval <NUM> is also performed to produce a time windowed key character queue <NUM> including characters <NUM> associated with key presses attempted by the robot. In some embodiments, characters are removed from the character queue when the key press associated therewith exceeds a predetermined age. In certain embodiments, characters are removed from the queue after they are found on the screen if unique. However, if not unique (e.g., the user presses "a a a a a" rapidly in succession), the oldest instance of "a" may be removed from the queue in some embodiments.

Core logic <NUM> receives the current frame and immediately previous frame (in this case, frames <NUM>, <NUM>), as well as key character queue <NUM>. Core logic <NUM> may perform some or all of the logic described above with respect to <FIG> and <FIG> in some embodiments. For instance, core logic <NUM> may compare frames <NUM>, <NUM> to one another and determine region(s) therein where visual changes occurred. This may be determined by comparing whether red/green/blue (RGB) values of respective pixels exceed a threshold with respect to one another, for example.

Core logic <NUM> may isolate the location(s) of the visual changes, perform character recognition to recognize characters in the location(s), and determine whether the characters correspond to those that were expected to appear based on the contents of key character queue <NUM>, for example. Core logic <NUM> may also match recognized characters to characters <NUM> in key character queue <NUM>. When matches are found, the screen coordinates of the match location may be extracted and provided as caret and/or typing regions <NUM>.

Additionally or alternatively to using character recognition, in some embodiments, image recognition (e.g., CV) may be used to detect newly appearing or disappearing images or icons. The process may be otherwise the same as that described above, except with this replacement or supplemental logic at the region difference analysis stage. This may be useful for determining where a robot is cutting-and-pasting images or text into a document or field, for example.

In the case of cutting-and-pasting text, when text is flushed from clipboards, the individual characters are not captured as key press events. In such a case, the content of the clipboard and the time when the user is pressing CTRL+V can be captured. The content of the clipboard can then be compared to the OCR results, similar to how individual characters and character sequences are identified. However, rather than reviewing the typed character queue, the algorithm would work with a string of characters that were stored in the clipboard before CTRL+V occurred.

In some embodiments, the current active element may be saved for the case where a robot presses a certain key that causes a screen change. For instance, if the robot presses the enter key, it may cause a form to submit and a window to close out. In another example, if a robot presses enter in a URL input field for a web browser, this may cause the web browser to start navigating to a website. The algorithm may recognize this and wait for a certain amount of time before it looks where the active element is in these scenarios since significant screen changes are likely to be occurring. Once the screen becomes relatively static (e.g., only a relatively small portion of the screen changes from one screenshot to the next), the typed text detection and caret tracking may resume again to find the new active element. It may thus be helpful to know which element was focused when the robot pressed enter, escape, etc. Also, if the current operating system provides a reliable way to natively obtain the window bounding rectangle of the current foreground window (e.g., the "GetForegroundWindow" available in user32. dll in Windows®), the foreground window bounding rectangle may be used to limit the area that will be used for screen capturing and frame processing.

In some embodiments, if the focused or active element is determined, behavior from pressing keys that do not cause text to appear (e.g., enter, escape, control, a combination of control and/or alt and one or more characters, etc.) may be determined based on the nature of the active element. For instance, when such a "hot key" (invisible key or combination of keys) is pressed, the action that the "hot key" has triggered can be classified. If the focused element is labeled "Password" and the robot presses "Enter", it can be inferred that pressing "Enter" represents a "Login" action. This may be more descriptive than just knowing that the robot has pressed "Enter".

<FIG> illustrates portions <NUM>, <NUM> of screenshots <NUM>, <NUM> including an alumni donation window for frames N - <NUM> and N, respectively, according to an embodiment of the present invention. As can be seen in <FIG>, all fields in frame N - <NUM> are empty, but in frame N, the robot has typed the letter "E" in the first name field. In order to determine whether this change should have occurred, the algorithm of some embodiments scales the screenshots and normalizes them into squares. In this case, screenshot <NUM> (see <FIG>) and screenshot <NUM> (see <FIG>) are normalized into a grid of <NUM> squares by <NUM> squares, a <NUM> × <NUM> portion of which is shown in <FIG>. These squares, or regions, may be represented as a <NUM> × <NUM> binary matrix.

The row for each screenshot may then be compared to one another to see whether any pixels contained therein have changed, and the values of the matrix may be set to "<NUM>" for each row with a change. This may be done by checking whether a non-zero element exists in each row. As seen in <FIG>, when the algorithm checks row <NUM> of the matrix, designated <NUM> herein, a change is detected therein. The algorithm then steps into row <NUM>, as seen in <FIG>, and the square denoted <NUM> in column <NUM> includes a portion of the newly entered letter "E". The algorithm continues to identify squares including changes and stitches together adjacent squares <NUM> as a region to be submitted for OCR to be run on this portion. This is denoted a "region" herein. In some embodiments, if one or some squares making up the region do not have changes, these squares may be set to be entirely one color, for instance, to make OCR processing run faster. In certain embodiments, if sufficient processing resources are available, difference regions in the between the screenshots may be identified, OCR can be performed on the difference regions to recognize text, and this text can be compared to the key character queue to search for matches.

In the case that "E" was expected to appear in the first name field, the processing and validation may proceed to the next frame. If, however, the letter "E" was not the correct entry (e.g., "Ed" was expected instead of just "E"), "E" appeared in the wrong field, or both, this may be recognized by the validation logic. The robot may then attempt to enter remove the incorrect text and enter the correct text into the same field or a different field or take some other remedial action. If the remedial action fails, the robot may pause or stop execution of its workflow, notify a user or developer of the error, etc..

From time to time, the display resolution may change, an additional monitor may be hooked up, etc. Some embodiments detect and accommodate these changes so that the detection and validation processes remain accurate. <FIG> is a flowchart illustrating a process <NUM> for checking for resolution changes, checking for changes in the range of connected displays, and setting up the frame capture logic to accommodate changes, according to an embodiment of the present invention. The process begins with checking one or more connected displays for a computing system at <NUM> and comparing the connected display(s) to previously connected display(s) at <NUM>. This may involve checking whether a different display device is present, checking whether the resolution of the display device has changed, etc. In some embodiments, a "connected" display may be a display integrated with the computing system (e.g., as is normally the case with smart phones, laptop computers, etc.).

If the connected display device(s) and/or resolution have changed at <NUM>, the resolution and scale is obtained for each connected display at <NUM>. The screenshot area for screenshots that will be captured is set to the full display dimensions multiplied by the scale and aligned to multiples of a desired size (e.g., <NUM>, <NUM>, etc.) at <NUM>. The multiples may facilitate dividing the screenshots into squares, as discussed in further detail later herein. The frame capture logic is then set (e.g., restarted, reinitialized, provided with the new display settings, etc.) at <NUM>.

<FIG> is a flowchart illustrating a process <NUM> for a video recorder, according to an embodiment of the present invention. The process begins with taking a screenshot at <NUM>. In some embodiments, this may be accomplished in C# using the Graphics Device Interface (GDI) CopyFromScreen() instruction for Windows®. The screenshot is then added as a frame to a buffer at <NUM>. This can be accomplished by adding the screenshot to the buffer as a Bitmap object in C#, for example. If the process is still running at <NUM> (e.g., the process has not been stopped by closing the application, a screen resolution change, etc.), the screenshot capture may be repeated for the next screenshot. It should be noted that while C# examples are provided, for process <NUM> and the other processes disclosed herein, any suitable programming language, operating system, APIs, and functions may be used without deviating from the scope of the invention.

Some embodiments perform caret tracking to more accurately identify which element of a screen a robot is focusing on. For instance, if a caret appears in a graphical element where text is appearing, it is likely that newly added text in the key character queue is what is appearing in this graphical element. <FIG> is a flowchart illustrating a process <NUM> for performing caret tracking, according to an embodiment of the present invention. Typically, the caret will appear and start blinking at or near a location where the user most recently clicked. Accordingly, some embodiments store the coordinates of the last mouse click and search for the caret proximate to this location. This may reduce the amount of the screenshot that is processed to locate the caret and may further increase accuracy. In some embodiments, a history buffer of mouse clicks or the single most recent click location is used. In certain embodiments, if the robot "presses" the tab key, for example, the system may assume that the caret may have moved to the next graphical element on the screen and may refocus the search to that location, if known, or else search the entire screenshot.

Pixel changes are calculated for each region in the screenshot where changes occurred, and the regions are projected to a binary matrix at <NUM>. The binary matrix is a representation of whether the pixels of a region have changed, and may include a "<NUM>" for pixels with no change between screenshots and a "<NUM>" for pixels that changed. A "region" is a square where changes occurred that may include multiple squares from the screenshot in some embodiments. However, any other suitable shape (e.g., a rectangle, a hexagon, etc.) may be use without deviating from the scope of the invention. In some embodiments, a fixed number of regions are supported for analysis, depending on the processing power of the computing system. For instance, some embodiments support extraction and OCR of two regions, three regions, ten regions, etc. Some embodiments may look for both the caret and typed or pasted text. When more than a number L of change regions is found between the current screenshot and the previous screenshot, the first L regions that were found may be processed or the screenshot may be ignored entirely. This may help to ignore screens where the user has launched a different window or a sufficient portion of the screen is otherwise changed such that OCR might not be completed in time before the next screenshot is captured.

For each binary matrix, blinking caret region candidates are extracted at <NUM> and binary matrix members are joined at <NUM>. As used herein, "members" are connected shapes that are present in the binary matrix, such as shapes representing a letter, a cursor, etc. The joining of the matrix members may be performed using a Connected Components algorithm where components are <NUM>-connected, for example. Connected Components are a set of pixels where each pixel is connected to all other pixels.

Shapes are extracted from the matrix member joining results at <NUM> and the shapes are validated at <NUM>. The shape should typically be a perfect rectangle, for example, which can include a line. The validated shape candidates are stored and compared to a confirmation queue at <NUM>. The position, size, and shape of the caret candidates may be stored, along with a time stamp. The frequency with which a caret blinks should be consistent within a tolerance (e.g., <NUM>%). Since the caret blinks, the candidates should be stored for analysis to see whether they match the expected properties of the caret (i.e., position, size, and frequency). This can help to determine whether the caret candidate is blinking with a certain frequency when compared across multiple screenshots (e.g., <NUM>). This information may also help to identify the caret if it reappears elsewhere after the user clicks the mouse on a new field, presses the tab key, etc. Naturally, at the beginning of the process, the confirmation queue is empty in some embodiments.

If a given caret candidate is confirmed to be blinking at <NUM> based on the appearance/disappearance of the caret candidate, the size, and the position, caret tracking data is produced for the blinking caret at <NUM>. This may include the position of the caret on the screen, the graphical element in which it resides (i.e., the active element), etc. The validated candidate regions and the corresponding member binary matrix data are then saved to the confirmation queue at <NUM> for later validation, for instance. The process of <FIG> may be repeated for each new screenshot in some embodiments.

<FIG> are flowcharts illustrating a process <NUM> for performing screen response validation of robot execution for RPA, according to an embodiment of the present invention. Prior to process <NUM>, a check may be made for resolution changes and the caret tracking video logic may be set up to accommodate changes. See <FIG>, for example. In certain embodiments, process <NUM> may run concurrently with a video recorder. See <FIG>, for example. Process <NUM> is an example using C# and the Windows® operating system. However, any suitable programming language, operating system, associated APIs, formats, and functions may be used without deviating from the scope of the invention.

The process begins with performing LockBits on item N (e.g., a screenshot, a portion of a screen, an application window, etc.) using the format Format16bppRgb555 for create a BitmapData object for N at <NUM>. LockBits locks a rectangular portion of a bitmap and provides a temporary buffer that can be used to read or write pixel data in a specified format. BitmapData stores attributes of a bitmap.

BitmapData N and BitmapData N - <NUM> (i.e., the BitmapData object for the previous item) are then divided into horizontal rows with a height of <NUM> pixels at <NUM>. However, any desired height (e.g., <NUM> pixels, <NUM> pixels, etc.) for this step and other steps of process <NUM> may be used without deviating from the scope of the invention. For each horizontal row of BitmapData N and BitmapData N - <NUM> in the same vertical position (i.e., in the same "row" - see <FIG>), a MPCMP instruction is executed at <NUM>, which performs fast comparison of byte arrays. MEMCMP provides an indication of whether the rows are the same.

If all rows are the same between BitmapData N and BitmapData N - <NUM> at <NUM> (i.e., there is a difference in at least one corresponding row) and no difference was expected at <NUM> based on a current activity being executed by the robot, the process proceeds to step <NUM> of <FIG> and on to the next screen capture. However, if a difference was expected at <NUM>, the process proceeds to step <NUM> of <FIG> to attempt to remedy the error. If all rows are not the same between BitmapData N and BitmapData N - <NUM> at <NUM> (i.e., there is a difference in at least one corresponding row) and a difference was not expected at <NUM>, the process proceeds to step <NUM> of <FIG> to attempt to remedy the error. However, it should be noted that if the result of the comparison between BitmapData N and BitmapData N - <NUM> is not what is expected (i.e., an unexpected change occurred when no change was expected or no change occurred when a change was expected), in some embodiments, process steps <NUM>-<NUM> or a subset thereof may still be performed.

If all rows are not the same between BitmapData N and BitmapData N - <NUM> at <NUM> (i.e., there is a difference in at least one corresponding row) and a difference was expected at <NUM>, horizontal rows for BitmapData N and BitmapData N - <NUM> in the same row where the MEMCMP result is not equal to <NUM> are then extracted at <NUM>, and the extracted horizontal rows are then divided into size <NUM> × <NUM> pixels at <NUM>. See <FIG>, for example. For each <NUM> × <NUM> pixel block of BitmapData N and BitmapData N - <NUM>, blocks where a difference between them are then extracted at <NUM>. See <FIG>, for example. This may be performed using a combination of long XOR functions looping Intel Intrinsics® instructions or some other suitable capability.

Per the above, in some embodiments, the number of regions that can be processed is limited to a predetermined number L. In certain embodiments, the number of blocks that can be included in each region may be limited. For instance, a limit of <NUM> squares, <NUM> squares, <NUM> squares, etc. may be imposed to ensure that OCR can be run on each region before the next screenshot is obtained. This may be an "optimization threshold", which can include a limit on the number of regions that have changed, a limit on the number of squares contained in a given changed region, or both.

The total count of extracted <NUM> × <NUM> pixel blocks in each region, the number of regions, or both, are compared to the optimization threshold at <NUM>. If the optimization threshold is met at <NUM>, the process proceeds to step <NUM> of <FIG> and on to the next screen capture. If the threshold is not exceeded at step <NUM>, proximate <NUM> × <NUM> pixel blocks are joined at <NUM> using a Connected Components algorithm, which may be an <NUM>-connected Connected Components algorithm in some embodiments. This determines which blocks are neighbors.

Once the connected neighbors are determined, a bounding rectangle for each set of proximate blocks is determined at <NUM>, forming a region. This may be determined by an extremal algorithm where the blocks having the highest and lowest x-values (i.e., the leftmost and rightmost block(s)) and the highest and lowest y-values (i.e., the uppermost and lowest block(s)) are included. Such an example can be seen in <FIG>.

For each bounding rectangle for a region, pixel changes are calculated and projected to a binary matrix at <NUM>. An example binary matrix <NUM> for the letter "E" included in four <NUM> × <NUM> blocks that have been combined into a <NUM> × <NUM> region is shown in <FIG>, for example.

In most cases, the region will be larger than the member(s) contained therein (e.g., letters, caret, other shapes that changed the pixels, etc.). In order to increase the speed of the OCR algorithm, for each binary matrix, the member(s) included in each region are determined (e.g., using a Connected Components algorithm) and the binary matrix is cropped for each member at <NUM>. This produces cropped matrices for each member in each region. An example cropped member matrix <NUM> for the letter "E" produced from binary matrix <NUM> is shown in <FIG>. The cropping may also be performed using an extremal algorithm in some embodiments.

Blinking caret region candidates are then extracted from the member matrices at <NUM>. For example, candidates may have a rectangular shape, which potentially includes a vertical line with a width of a single pixel in some embodiments. The extracted blinking caret region candidates and the corresponding member matrix data is then compared to a confirmation queue at <NUM>, potentially analyzing size, location, and frequency of blinking. If blinking at <NUM>, caret tracking data is produced for the blinking caret at <NUM>. Blinking caret regions and their corresponding member matrix data are then saved to the confirmation queue at <NUM>. In some embodiments, this portion of process <NUM> may be the same as or similar to process <NUM> of <FIG>.

The member binary matrices only indicate whether a given pixel has changed from screen capture N - <NUM> to screen capture N in some embodiments. Accordingly, the pixel data is retrieved from BitmapData N for each pixel that has changed at <NUM>. Member rectangles are then generated and prepared for OCR at <NUM>. This may include populating pixel data for each changed pixel, eliminating caret pixels, processing the background (e.g., setting unchanged pixels to null or a highly contracting value), etc. In the case where the caret pixels were eliminated, it can be assumed that the caret itself was detected at this time with a certain position, shape, and set of binary matrix members. This information can be stored for caret tracking purposes. OCR is then performed for the prepared member rectangle pixel data at <NUM>.

The expected change for a current robot activity in the RPA workflow is determined and validated at <NUM>. The expected change may be determined by analyzing key press events in a key character queue, changes that should appear based on a robot activity in an RPA workflow, etc..

If validation succeeded at <NUM> and a key character queue is used, key character queue items (e.g., key press events) that matched OCR regions may be removed from the key character queue at <NUM>. Where multiple instances of a character exist, the oldest instance of that character in the key character queue may be removed, for example. UnlockBits is then performed on BitmapData N - <NUM> at <NUM>, which unlocks this bitmap from system memory, and BitmapData N is moved to position N - <NUM> at <NUM>. Process <NUM> can then return to the start for the next captured item.

However, if validation failed at <NUM> (e.g., expected changes did not occur or only partially occurred), remedial action is attempted at <NUM>. For instance, the robot may be informed which step(s) of the workflow failed, take some corrective measure, and attempt the step(s) again. This may include, but is not limited to, the robot checking at the driver level whether a window for an application the robot is attempting to interact with is the active window, checking the location of the mouse pointer or caret relative to the pertinent graphical element, checking whether the correct graphical element is the active element, any combination thereof, etc. The robot may set the correct window to the active, focused window, set the focus to the desired active element, move the mouse and click, etc., and then attempt the step(s) again. While taking remedial action, the frame comparison process may be paused in some embodiments. If the remedial action succeeds at <NUM>, the process may proceed to step <NUM>.

However, if the remedial action fails at <NUM>, error logic may be executed at <NUM>. The error logic may include, but is not limited to, throwing an exception, stopping execution of the robot and displaying a message on a screen or sending a message to a human (e.g., via text or email) indicating that the robot failed to achieve the desired action(s). In certain embodiments, the action(s) that failed may be provided to the user so he or she can attempt to troubleshoot the problem, or the user can report the issue to be fixed by an RPA developer.

<FIG> are flowcharts illustrating a process <NUM> for performing pasted text tracking and validation, according to an embodiment of the present invention. The process begins with optionally performing key press recording at <NUM> and performing screen capture (e.g., video recording, capturing screenshots of all or a portion of a screen, etc.) at <NUM> to determine the keys that were pressed and the location(s) on the screen where graphical changes occurred, respectively. A robot action is then interpreted at <NUM>. Robot actions may include, but are not limited to, mouse clicks, pressing CTRL+V, right clicking plus selecting paste from a menu, clicking a home button and pasting in an application, etc. If the robot clicks a location and pastes from the clipboard quickly, the caret may be missed and the robot's actions may need to be reconstructed differently. If a paste from the clipboard did not occur at <NUM>, typed text detection and caret tracking logic is performed at <NUM>, potentially along the lines of process <NUM> of <FIG> in some embodiments.

However, if the clipboard includes recently pasted data from the robot at <NUM> (e.g., pasted within the last <NUM>, the last second, etc.), pixel differences between frames N and N - <NUM> are calculated at <NUM>. Predicted same position regions where changes occurred between frames N and N - <NUM> are then extracted at <NUM>. For each region, pixel changes are calculated and the changes are projected to a binary matrix at <NUM>.

For each binary matrix, members are determined using a Connected Components algorithm, for example, and member matrices are determined for each member at <NUM>. Pixel data is extracted for each changed pixel in the member matrices and member rectangles are generated at <NUM>. Each member rectangle is prepared for OCR at <NUM> and OCR is run on each prepared member rectangle at <NUM>. A fuzzy matching comparison of the OCR results to the clipboard content is performed for each OCR result at <NUM> using clipboard content provided by a clipboard monitor at <NUM>. In some embodiments, clipboard text content be obtained from System. dll using Clipboard. If a match is found at <NUM>, the coordinates of the pasted text (e.g., in the form of a rectangle) are produced and the clipboard is flushed at <NUM> and the process returns to steps <NUM> and <NUM> for the next frame.

However, if a match was not found at <NUM> (e.g., expected pasted content did not appear or only partially appeared), remedial action is attempted at <NUM>. For instance, the robot may be informed which step(s) of the workflow failed, take some corrective measure, and attempt the step(s) again. This may include, but is not limited to, the robot checking at the driver level whether a window for an application the robot is attempting to interact with is the active window, checking the location of the mouse pointer or caret relative to the pertinent graphical element, checking whether the correct graphical element is the active element, any combination thereof, etc. The robot may set the correct window to the active, focused window, set the focus to the desired active element, move the mouse and click, etc., and then attempt the step(s) again. While taking remedial action, the frame comparison process may be paused in some embodiments. If the remedial action succeeds at <NUM>, the process may proceed to step <NUM>.

<FIG> is a flowchart illustrating a process <NUM> for using CV and expected results of robot activities to determine active elements and validate robot actions, according to an embodiment of the present invention. The process begins with determining whether a frame changed from the previous frame by more than a predetermined threshold at <NUM>. This may include checking whether more than a certain portion of the frame has changed (e.g., more than <NUM>%), whether more than a predetermined number of pixels has changed (e.g., more than <NUM>), whether changes occur outside of locations of graphical elements in the frame that permit text entry, etc..

If the threshold is exceeded at <NUM>, it is likely that at least some of the graphical elements on the screen have also changed. CV preprocessing is performed at <NUM> to identify graphical element types and locations, which may be stored in memory. The frame comparison process may be paused while the CV preprocessing is performed in some embodiments.

If the threshold was not exceeded at <NUM>, or after CV preprocessing is completed at <NUM>, newly appearing elements in the key character queue added between the previous frame and the current frame are determined at <NUM>. If there are newly appearing elements in the key character queue, it may be assumed that these appeared in a suitable graphical element on the screen. Location(s) where the screen changed in the current frame are then determined at <NUM> and an attempt is made to match changes to locations of the graphical elements at <NUM>. If changes occurred within only one of the graphical elements, the matched graphical element is set as the active element at <NUM>. However, if changes occurred within multiple graphical elements or no changes were found in a graphical element, remedial action is performed at <NUM> (e.g., removing content from a field where content should not have appeared and attempting to insert the content into the correct field). The next frame is then fetched at <NUM> and the process repeats.

<FIG> is an architectural diagram illustrating a system <NUM> configured to perform screen response validation of robot execution for RPA, according to an embodiment of the present invention. System <NUM> includes user computing systems, such as desktop computer <NUM>, tablet <NUM>, and smart phone <NUM>. However, any desired computing system may be used without deviating from the scope of invention including, but not limited to, smart watches, laptop computers, Internet-of-Things (IoT) devices, vehicle computing systems, etc..

Each computing system <NUM>, <NUM>, <NUM> has a digital process <NUM> running thereon that records screenshots, keystrokes, running applications, application visual elements, visual element locations, application locations, the robot workflow and current step(s) being executed, etc. pertaining to its own operation or that of a separate robot. Indeed, any desired information pertaining to screen graphics, robot inputs, display elements, etc. may be recorded without deviating from the scope of the invention. In certain embodiments, only video and keystroke recordings are captured initially, and other information is determined subsequently using CV. However, additional information may help to focus and improve the CV process. Digital processes <NUM> may be robots generated via an RPA designer application, part of an operating system, a downloadable application for a personal computer (PC) or smart phone, or be any other software and/or hardware without deviating from the scope of the invention. Indeed, in some embodiments, the logic of one or more of digital processes <NUM> is implemented partially or completely via physical hardware.

Digital processes <NUM> send recorded screenshots, keystrokes, running applications, application elements and locations, a combination thereof, etc. via a network <NUM> (e.g., a local area network (LAN), a mobile communications network, a satellite communications network, the Internet, any combination thereof, etc.) to a server <NUM>. In some embodiments, server <NUM> may run a conductor application and the data may be sent periodically as part of the heartbeat message. Server <NUM> stores information from digital processes <NUM> in a database <NUM>.

Server <NUM> runs instances <NUM> of screen response validation logic for computing systems that server <NUM> receives data from (i.e., computing systems <NUM>, <NUM>, <NUM>). Server may analyze results from instances <NUM> to determine what robots are doing when interacting with their computing systems. Steps in the workflow may be mapped and time synchronized with the video and key presses to ensure that instances <NUM> can determine what the robot is attempting to do. Information regarding whether the validation succeeded or failed, the nature of the failure if it occurred, the step the robot was on, etc. may be sent back to respective user computing systems <NUM>, <NUM>, <NUM> to validate the execution of robots monitored by digital processes <NUM>. In some embodiments, the validation by server <NUM> may occur in real time or near-real time so processes <NUM> can perform validation as an RPA robot executes on the respective computing system.

<FIG> is a flowchart illustrating a process <NUM> for performing screen response validation of robot execution for RPA, according to an embodiment of the present invention. The process begins with applying a CV algorithm to determine graphical elements and associated bounding rectangles in the user interface at <NUM>. The determined graphical elements may include a target graphical element for an RPA activity in some embodiments. A determination is made whether a difference exists between a current screenshot frame and a previous screenshot frame at <NUM>. The current screenshot frame and the previous screenshot frame may include an entire user interface or a portion thereof. When the difference exists between the current screenshot frame and the previous screenshot frame at <NUM> and a difference between the current screenshot frame and the previous screenshot frame is expected at <NUM> based on an activity in a workflow of an RPA robot, one or more changes are validated between the current screenshot frame and the previous screenshot frame against one or more expected screen changes based on the activity of the workflow of the RPA robot at <NUM>. In some embodiments, validation includes performing OCR on the one or more locations where the current screenshot frame differs from the previous screenshot frame and matching characters recognized by the OCR to characters in the key character queue.

When validation succeeds at <NUM>, the process proceeds to the next frames (i.e., the next frame as the current screenshot frame and the current screenshot frame as the previous screenshot frame) and to the next activity in the workflow or the next change to be affected by the same activity. However, when validation fails, an exception is thrown or remedial action is take at <NUM>. If remedial action is taken at <NUM> and it is successful, the process may proceed to step <NUM>.

Returning to step <NUM>, it a difference does not occur at <NUM> and a difference was not expected at <NUM>, the process proceeds to step <NUM>. However, if a difference was expected at <NUM>, the process proceeds to step <NUM>.

In some embodiments, the remedial action includes the RPA robot checking at the driver level whether a window for an application the robot is attempting to interact with is an active window, checking a location of a mouse pointer or caret relative to a graphical element in which a change was expected to occur, checking whether a correct graphical element is an active element, or any combination thereof. In certain embodiments, the remedial action includes the RPA robot setting a correct window as an active, focused window, setting a focus to an active element, moving a mouse and causing a mouse click event, or any combination thereof. In some embodiments, the throwing of the exception includes stopping operation of the RPA robot, displaying a message on a screen, sending a message indicating that the RPA robot failed to achieve the one or more expected screen changes, listing one or more actions that failed, or a combination thereof.

In some embodiments, a key character queue including key press events created by the RPA robot that occurred during a time window is generated at <NUM>. In certain embodiments, matching characters are removed from the key character queue during validation at <NUM>. In some embodiments, the key character queue is a FIFO queue that includes a character of a key press event and a time that the key press event occurred for each of the key press events during the time window.

In some embodiments, the validation includes determining one or more regions where the current screenshot frame differs from the previous screenshot frame, extracting one or more connected members in the one or more determined regions, and performing OCR on the extracted one or more connected members, producing one or more recognized characters, one or more recognized character sequences, or both, and respective positions. The validation may include comparing the one or more recognized characters, the one or more recognized character sequences, or both, to the key character queue or to one or more characters, one or more character sequences, or both, expected to appear based on the activity. In certain embodiments, a target graphical element and content to be input into the target graphical element are determined based on the activity in the workflow of the RPA robot and the validation includes validating whether the one or more changes between the current screenshot frame and the previous screenshot frame match a location of the target graphical element and whether the one or more changes match the content to be input into the target graphical element. In some embodiments, the activity includes entering text into a graphical element, moving a caret to the graphical element, pasting content into the graphical element, or a combination thereof. In certain embodiments, the one or more expected screen changes based on the activity of the workflow of the RPA robot include submission of a form, opening of a new application window, or changing of a current application window.

The process steps performed in <FIG>, <FIG>, <FIG>, <FIG>, <FIG>, and <FIG> may be performed by a computer program, encoding instructions for the processor(s) to perform at least part of the process(es) described in <FIG>, <FIG>, <FIG>, <FIG>, <FIG>, a<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>, <FIG>, <FIG>, <FIG>, <FIG>, and <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. Thus, the detailed description of the embodiments of the present invention, as represented in the attached figures, is not intended to limit the scope of the invention as claimed, but is merely representative of selected embodiments of the invention.

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.

One skilled in the relevant art will recognize that the invention can be practiced without one or more of the specific features or advantages of a particular embodiment.

Claim 1:
A non-transitory computer-readable medium (<NUM>) storing a computer program (<NUM>) comprising screen response validation logic for robotic process automation, the computer program (<NUM>) configured to cause at least one processor (<NUM>) to:
determine whether a difference exists (<NUM>) between a current screenshot frame and a previous screenshot frame by determining whether at least some pixels in the current screenshot frame differ from corresponding pixels in the previous screenshot frame, the current screenshot frame and the previous screenshot frame comprising an entire user interface or a portion thereof;
when the difference exists between the current screenshot frame and the previous screenshot frame and a difference between the current screenshot frame and the previous screenshot frame is expected (<NUM>) based on an activity in a workflow of a robotic process automation robot:
validate (<NUM>) one or more changes between the current screenshot frame and the previous screenshot frame against one or more expected screen changes based on the activity of the workflow of the robotic process automation robot, and
when the validation fails, throw an exception or initiate a remedial action (<NUM>), wherein
the expected difference between the current screenshot frame and the previous screenshot frame is determined based on a graphical element interaction caused by the activity of the workflow of the robotic process automation robot.