Abstract: A computerized system and method may generate computer automation opportunities based on segmenting action sequences from action data and/or information items. A computerized system including a processor or a plurality of processors, and a memory including a data store of a plurality of data items describing actions input to a computer may be used to receive an input query or a plurality of actions input to a computer; segment action sequences from the stored data items based on the query; and produce automation candidates based on the segmented sequences. Embodiments of the invention may include generating, by a machine learning model, vector embeddings for action sequences, calculating similarity scores for sequences based on the embeddings, and mining a plurality of action subsequences based on, a group or set of similar sequences, as well as additional and/or auxiliary procedures and operations.

Abstract: A computerized-method for sensitive data redaction from screenshots, is provided herein. The computerized-method includes retrieving records of a sequence of screenshots from a database. Then, grouping the sequence-of-screenshots by one feature of one or more features to yield one or more groups. Each group includes screenshots having one common feature. Then, calculating a score for each pixel across all similar screenshots in each group. For each group of screenshots, blackening pixels in all screenshots having a score above a preconfigured threshold to yield data redacted screenshots. The score of each pixel above the preconfigured threshold indicates a high variance between screenshots in the group and a presence of sensitive data therein and then storing the data-redacted screenshots in a screenshots-database.

Abstract: A system and method may identify computer-based processes which may be candidates for automation. Embodiments may involve a semi-supervised approach for identifying processes as automation opportunities. Transition probabilities for pairs of routines within a candidate process may be calculated based on a set of instances of the process (e.g., in a dataset of computer actions) using a dynamic time-window optimization procedure, where transition times may be measured for a plurality of instances of a first and second routines of a given pair of routines, and where statistical distributions may be calculated and used for deriving one or more time windows, describing a predetermined percentile (e.g., the 70th percentile) of the measured transitions and used for estimating a transition probability for the pair of routines. In some embodiments, the input set of transitions and routines may be generated by a user or business analyst using a graphical user interface (GUI).

Abstract: A system and method may identify computer-based processes involving the use of text templates which may be candidates for automation. Using one or more computers, embodiments of the invention may sort low-level user action information for a given process which may be received as input; search for a plurality of strings pasted multiple times in the sorted information; discard one or more of the strings found from the search which correspond to a set of criteria (e.g., found to be shorter, or pasted, or edited fewer times than a predetermined threshold); group the strings according to an identifier of the target app where each string was pasted; iteratively calculate a similarity score for strings or groups of strings, and cluster strings or groups for which the similarity score is below a predetermined threshold, to form final clusters; and suggest the final clusters as automation opportunities to, e.g., a business analyst.

Abstract: A method and system for dynamically determining a minimum support for automation mining is provided. The method and system include modifying the minimum support pattern such that the minimum support can result pattern mining algorithms finding a sufficient number of patterns in a practical duration.

Abstract: A method and system for analyzing and connecting computer-based actions into sentences may include for a series of computer-based actions, determining the case ID for the action for each action where an identifier or case ID can be determined, creating sequences of subsets of the series of computer-based actions using the case ID, and merging sequences having computer-based actions having the same case ID. A set of case IDs may be extracted from the actions using a clustering algorithm based on features of potential case IDs such as gaps in appearance of potential case IDs in a sequence of actions and consecutive appearances of potential case IDs in a sequence of actions. The extracted case IDs may be used when creating sequences.

Abstract: A system and method analyzes computer actions to identify computer-based processes (e.g. computer-user interactions) which are automation candidates. A data gathering process executed by a processor on a computer may collect low-level user action information or items, each low-level user action information or item including for example an input type description, a user name, and screen window information. At a computer sequential pattern mining may be applied to determine a set of subprocesses, each subprocess including a series of low-level user actions, each user action associated with a user action vector, and each subprocess associated with a subprocess vector generated from user action vectors associated with (typically generalized) low-level user actions comprised in the subprocess. The subprocess vectors may be grouped or clustered to create processes. For each process, an automation score may be calculated using the actions in the subprocesses in the process.

Abstract: A method and system for dynamically determining a minimum support for automation mining is provided. The method and system include modifying the minimum support pattern such that the minimum support can result pattern mining algorithms finding a sufficient number of patterns in a practical duration.

Abstract: A system is provided for an artificial intelligence engine adapted to identify robotic process automation' opportunities based on return on investment (ROI) potential for automation. The system includes a processor and a computer readable medium configured to perform operations comprising receiving an event log of a plurality of user actions, splitting the plurality of user actions into a plurality of user action sentences, determining a sequence of user actions in the plurality of user action sentences based on a recurrence for the sequence in the plurality of user action sentences, determining a score for the sequence based on a time duration in which the user completes the sequence and based on types of the plurality of user actions in the sequence, and filtering the sequence with a plurality of other sequences.

Abstract: A computerized-method for automatically documenting automatically-discovered actions of a business-process for automation thereof is provided herein. The computerized-method includes operating a build-automation-document module. The build-automation-document module includes: (i) retrieving the set of automatically-discovered actions of the business process for automation thereof. Each action is of a related type and related to an application to complete the business process via the applications portal.

Abstract: A method and system for analyzing and connecting computer-based actions into sentences may include for a series of computer-based actions, determining the case ID for the action for each action where an identifier or case ID can be determined, creating sequences of subsets of the series of computer-based actions using the case ID, and merging sequences having computer-based actions having the same case ID. A set of case IDs may be extracted from the actions using a clustering algorithm based on features of potential case IDs such as gaps in appearance of potential case IDs in a sequence of actions and consecutive appearances of potential case IDs in a sequence of actions. The extracted case IDs may be used when creating sequences.

Abstract: A system and method for segmenting or dividing a series of computer-based actions, for example into sentences, may provide a sequence of subsets of the series of actions to a neural network using a sliding window, and divide or segment the series actions into segments at points where the loss of the neural network is above a threshold. The dividing may include, for each of a sequence of computer-based actions within a sliding window determining if the sequence when provided to the neural network corresponds to a loss above or equal to a threshold, and if so, determining that an action in the sequence of actions within the sliding window should not be part of a segment or sentence being created.

Abstract: A computerized-method for automatically documenting automatically-discovered actions of a business-process for automation thereof is provided herein. The computerized-method includes operating a build-automation-document module. The build-automation-document module includes: (i) retrieving the set of automatically-discovered actions of the business process for automation thereof. Each action is of a related type and related to an application to complete the business process via the applications portal.

Abstract: A system and method may determine, e.g. for suggestion to an automation designer, a next action in an automation sequence of actions being created. The automation sequence may include actions executed by a computer system which substitute for actions taken by a user operating a computer system. A sequence of actions may be received, a database of action sequences may be searched for a set of action sequences corresponding to the sequence of actions, and the next action may be presented or determined as being the for example a subsequent new action immediately after the last action matching any action from the received sequence of actions in an action sequence in the set of action sequences.

Abstract: A system and method analyzes computer actions to identify computer-based processes (e.g. computer-user interactions) which are automation candidates. A data gathering process executed by a processor on a computer may collect low-level user action information or items, each low-level user action information or item including for example an input type description, a user name, and screen window information. At a computer sequential pattern mining may be applied to determine a set of subprocesses, each subprocess including a series of low-level user actions, each user action associated with a user action vector, and each subprocess associated with a subprocess vector generated from user action vectors associated with (typically generalized) low-level user actions comprised in the subprocess. The subprocess vectors may be grouped or clustered to create processes. For each process, an automation score may be calculated using the actions in the subprocesses in the process.

Abstract: A system and method analyzes computer actions to identify computer-based processes (e.g. computer-user interactions) which are automation candidates. A data gathering process executed by a processor on a computer may collect low-level user action information or items, each low-level user action information or item including for example an input type description, a user name, and screen window information. At a computer sequential pattern mining may be applied to determine a set of subprocesses, each subprocess including a series of low-level user actions, each user action associated with a user action vector, and each subprocess associated with a subprocess vector generated from user action vectors associated with (typically generalized) low-level user actions comprised in the subprocess. The subprocess vectors may be grouped or clustered to create processes. For each process, an automation score may be calculated using the actions in the subprocesses in the process.

Abstract: A system and method may determine, e.g. for suggestion to an automation designer, a next action in an automation sequence of actions being created. The automation sequence may include actions executed by a computer system which substitute for actions taken by a user operating a computer system. A sequence of actions may be received, a database of action sequences may be searched for a set of action sequences corresponding to the sequence of actions, and the next action may be presented or determined as being the for example a subsequent new action immediately after the last action matching any action from the received sequence of actions in an action sequence in the set of action sequences.

Abstract: A system and method analyzes computer actions to identify computer-based processes (e.g. computer-user interactions) which are automation candidates. A data gathering process executed by a processor on a computer may collect low-level user action information or items, each low-level user action information or item including for example an input type description, a user name, and screen window information. At a computer sequential pattern mining may be applied to determine a set of subprocesses, each subprocess including a series of low-level user actions, each user action associated with a user action vector, and each subprocess associated with a subprocess vector generated from user action vectors associated with (typically generalized) low-level user actions comprised in the subprocess. The subprocess vectors may be grouped or clustered to create processes. For each process, an automation score may be calculated using the actions in the subprocesses in the process.

Abstract: A system is provided for an artificial intelligence engine adapted to identify robotic process automation' opportunities based on return on investment (ROI) potential for automation. The system includes a processor and a computer readable medium configured to perform operations comprising receiving an event log of a plurality of user actions, splitting the plurality of user actions into a plurality of user action sentences, determining a sequence of user actions in the plurality of user action sentences based on a recurrence for the sequence in the plurality of user action sentences, determining a score for the sequence based on a time duration in which the user completes the sequence and based on types of the plurality of user actions in the sequence, and filtering the sequence with a plurality of other sequences.