Abstract: A robotic process automation (RPA) system provides bots that interact with and provide user credentials to applications that require multi-factor authentication (MFA). First user credentials associated with MFA are retrieved by the bots from credential storage. Second user credentials that correspond to questions posed to a user of an application are retrieved from credential storage. Second user credentials that correspond to a one-time password are generated by the RPA system. The second user credentials may also be generated by a third-party authentication service that provides the credentials via a secondary channel such as email or SMS, which are then retrieved for presentation to the application.

Abstract: Task automation is enabled by recording, over a period of time, inputs of a computing device user to generate a log of inputs by the user in connection with one or more task applications. The user inputs are stored along with information pertaining to the one or more task applications. The log is processed to identify the one or more task applications to generate a user task file. The log is further processed to identify the fields in the task applications with which the user entered inputs and the identified fields are stored to the task file. The task file is processed to identify one or more tasks performed by the user. An automated software robot which is encoded with instructions to perform automatically, when invoked, one or more of the tasks performed by the user may be automatically generated.

Abstract: A computerized method for processing a set of robotic process automation (RPA) tasks receives service level requirement inputs that specify a first set of RPA tasks to be performed within a specified period of time. A response to the service level requirement inputs is computed to determine a number of computing resources required to perform the first set of RPA tasks in the specified period of time. Availability of computing resources from a set of computing resources is determined to generate an allocated set of computing resources. The allocated set of computing resources are deployed. A subset of the first set of RPA tasks is queued for each computing resource and each computing resource is monitored and redeployed as it completes tasks in its queue. Quality of Service (QOS) is achieved by prioritizing certain tasks above others.

Abstract: Image encoded documents are identified by recognizing known objects in each document with an object recognizer. The objects in each page are filtered to remove lower order objects. Known features in the objects are recognized by sequentially organizing each object in each filtered page into a one-dimensional array, where each object is positioned in a corresponding one-dimensional array as a function of location in the corresponding filtered page. The one-dimensional array is then compared to known arrays to classify the image document corresponding to the one-dimensional array.

Abstract: A robotic process automation system employs centralized compilation to generate a platform independent executable version of a bot, which is encoded to perform user level operations. The system employs an extensible set of commands which can be user generated. The bots execute on devices that are separate and independent from a server processor that controls the system. The devices execute bots in an execution environment that is provided by the server processor. Change in a command in a bot requires recompilation of the bot which is then delivered upon request to a device. The execution environment does not require recompilation upon a change in a command.

Abstract: A computer system and computerized method that groups documents with similar image layout together. A document similarity metric based on locally connected subgraphs is employed. Region adjacency graphs are generated from word segments extracted from document images. Fuzzy attributed graph isomorphism is performed on subgraphs checking node and edge attribute similarity. Document similarity is then calculated on a normalized score between matching subgraphs of different documents. Unsupervised clustering of document layouts is performed to generate clusters of documents with similar structure.

Abstract: Computerized robotic process automation (RPA) methods and systems that increase the flexibility and lower the cost with which RPA systems may be deployed are disclosed herein. In one embodiment, an RPA system and method avoids the need for preinstalled RPA software on a device employed by a user to create and/or execute software robots to perform RPA. In another embodiment, an RPA system and method provides a capability to execute software robots that may have been encoded in one or more programming languages to execute on an operating system different than that employed by a server of the RPA system.

Abstract: Computerized detection of one or more user interface objects is performed by processing an image file containing one or more user interface objects of a user interface generated by an application program. Sub-control objects are detected in the image file, where each sub-control object forms a portion of a user interface object that receives user input. Extraneous sub-control objects are detected. Sub-control objects that overlap with or that are within a predetermined vicinity of an identified set of sub-control objects are removed. Sub-control objects in the identified set of sub-control objects are correlated to combine one or more of the sub-control objects in the identified set of sub-control objects to generate control objects that correspond to certain of the user interface objects of the user interface generated by the application program.

Abstract: Information contained in tables in a digitized document is extracted by retrieving table layout data regarding bounding boxes, each being auto-generated by the system and/or (re)generated by a user to the digitized image of a sample document. A row template is used to identify a first table, by automatically scanning within the document. Upon detecting a possible row in the input image, a Row Possibility Confidence Value (RPCV) is generated that indicates a likelihood that the possible row corresponds to an actual row in the first table. The possible row is regarded as an actual row if the RPCV exceeds a predetermined threshold value. For repeated tables in a document only the first table needs to be identified via bounding boxes. Also, related tables can be linked to permit linked data to be extracted to a structured file. Also, only the primary column in a readable and existent table header is required to extract table values across columns.

Abstract: Automation controls and associated text in images displayed by a computer application are automatically detected by way of region-based R-FCN and Faster R-CNN engines. Datasets comprising images containing application controls, where the application controls include images of application where width is greater than height, width is equal to height and height is greater than width are retrieved and each dataset is processed with the R-FCN and Faster R-CNN engines to generate a software robot configured to recognize corresponding application controls. Text is recognized by an optical character recognition system that employs a deep learning system trained to process a plurality of images to identify images representing text within each image and to convert the images representing text to textually encoded data. The deep learning system is trained with training data generated from a corpus of real-life text segments that are generated by a plurality of OCR modules.

Abstract: A User Interface (UI) interface object detection system employs an initial dataset comprising a set of images, that may include synthesized images, to train a Machine Learning (ML) engine to generate an initial trained model. A data point generator is employed to generate an updated synthesized image set which is used to further train the ML engine. The data point generator may employ images generated by an application program as a reference by which to generate the updated synthesized image set. The images generated by the application program may be tagged in advance. Alternatively, or in addition, the images generated by the application program may be captured dynamically by a user using the application program.

Abstract: A system that automatically recognizes checkboxes within a document and extracts the contents thereof. A user views a digitized document and annotates the document by identifying checkboxes contained in the document, by way of visually perceptible bounding boxes. The annotated document is processed by a machine learning engine that employs multiple convolutional operations followed by a global average pooling layer, a fully connected layer with 1024 node and ‘ReLU’ activation, a fully connected layer with 2 node and ‘softmax’ activation. The identified checkboxes and their contents are stored as label-value pairs, where the label identifies the checkbox and the value identifies the value of the checkbox, which can be either Yes, No, or No checkbox found.

Abstract: A robotic process automation system includes a server processor that performs an automation task to process a work item, by initiating a java virtual machine on a second device. A first user session that employs credentials of a first user for managing execution of the automation task is also initiated on the second device. The server processor loads into the java virtual machine, with a platform class loader, one or more modules, such as logging and security, that perform functions common to the sets of task processing instructions. A first class loader a first set of task processing instructions is also loaded. Then each instruction in the first set of task processing instructions is loaded with a separate class loader. The server processor causes execution, under control of the first user session, on the second device, the task processing instructions that correspond to the work item.

Abstract: A server responds to a request to perform a first automation task to process a work item from the plurality of work items, on a first computing device that is separate and independent from the server. The server receives a request from the first computing device to download the first automation task and queries a distribution information file to identify one or more other computing devices that have a copy of the first automation task. The server provides to the first computing device, an identifier for each of one or more other computing devices that has a copy of the first automation task. If the distribution information file does not contain an identification of any other device that has a copy of the first automation task, then the server processor causes the first automation task to be retrieved and to be provided to the first computing device.

Abstract: Image encoded documents are identified by recognizing known objects in each document with an object recognizer. The objects in each page are filtered to remove lower order objects. Known features in the objects are recognized by sequentially organizing each object in each filtered page into a one-dimensional array, where each object is positioned in a corresponding one-dimensional array as a function of location in the corresponding filtered page. The one-dimensional array is then compared to known arrays to classify the image document corresponding to the one-dimensional array.

Abstract: Robotic process automation (RPA) tasks for operating on data within a productivity program may be initiated by single user action from within the productivity program. A user device is provided with a plugin program that provides an automation user interface within the productivity program. A request to perform an automation task on data in a productivity file is received along with identification of a software robot to perform the automation task. The request also specifies data from the productivity file. The request is provided to a control room server that controls execution of a plurality of different automation tasks by a plurality of different software robots. Results of the automation task are received from the control room server and are provided to the plugin program, which provides the results of the automation task to the user within a productivity file accessible by the user.

Abstract: A computerized method and system for adding distortions to a computer-generated image of a document stored in an image file. An original computer-generated image file is selected and is processed to generate one or more distorted image files for each original computer-generated image file by selecting one or more augmentation modules from a set of augmentation modules to form an augmentation sub-system. The original computer-generated image file is processed with the augmentation sub-system to generate an augmented image file by altering the original computer-generated image file to add distortions that simulate distortions introduced during scanning of a paper-based representation of a document represented in the original computer-generated image file.

Abstract: A computer implemented method and system for correcting error produced by Optical Character Recognition (OCR) of text contained in an image encoded document. An error model representing frequency and type of errors produced by Optical Character Recognition Engine is generated. An OCR character string generated by OCR is retrieved. A user-defined pattern of a plurality of character strings is retrieved, where each character string represents a possible correct representation of characters in the OCR character string. The OCR character string is compared to each of the above generated character strings and a ‘likelihood score’ is calculated based on the information from the error model. The character string with the highest ‘likelihood score’ is presumed to be the corrected version of the OCR character string.

Abstract: Image documents that have a visually perceptible geometric structure and a plurality of visually perceptible key-value pairs are grouped. The image documents are processed to generate a corresponding textually encoded document. The textually encoded documents are each assigned into one of a plurality of layout groups, wherein all textually encoded documents in a particular layout group share a visually perceptible layout that is substantially similar. Triplets are selected from the layout groups, where two documents are from the same layout group and one document is from a different layout group. The triplets are processed with a convolutional neural network to generate a trained neural network that may be used to classify documents in a production environment such that a template designed on one image document in a group permits an extraction engine to extract all relevant fields on all image documents within the group.

Abstract: A robotic process automation system processes task processing instructions which are operable to interact at a user level with user level application programs to process work items. A first server processor responds to a request to perform an automation task to automatically process without human user input, a work item by transmitting requests to an autologin program executing on a second server processor device to initiate a plurality of remote desktop protocol oriented processes within an operating system executing on the second server, where the remote desktop protocol oriented processes provide a graphical user interface to an authorized user. Credentials for the authorized users of the remote desktop protocol oriented processes are provided by one or more of the plurality of sets of task processing instructions, which upon logging into the corresponding remote desktop protocol oriented process, process the work items as encoded in the task processing instructions.