Abstract: A system and method may analyze computer actions on a computer desktop system. Using a data gathering process, a low-level user action information item, describing input by a user (e.g. to the computer desktop system), may be received or gathered. The low-level user action information item may include an input type description and screen window information. Based on a series of low-level user action information items, a process a computer is engaging in with the user may be estimated or determined. The best or most appropriate next low-level user action may be displayed or suggested to the user, e.g. on a computer desktop system to a user.

Abstract: Systems and methods of discovering computer network assets, including: identifying, by a processor, in sampled traffic over at least one computer network, an internet protocol (IP) address of a node communicating over at least one port, wherein the at least one port is associated with an asset type, determining, by the processor, a volume of traffic associated with the IP address of the node communicating over the at least one port, discovering, by the processor, the IP address of the node as belonging to an asset of the asset type, based on the volume of traffic exceeding a dynamic threshold, and adding the asset, by the processor, to a list of discovered assets.

Abstract: Systems and methods of determining file-access patterns in at least one computer network, the network comprising a file-access server, including training a first machine learning (ML) algorithm with a first training dataset comprising vectors representing network traffic such that the first ML algorithm learns to determine network characteristics associated with file-access traffic, determining, using the first ML algorithm, network characteristics based on highest interaction of traffic with the file-access server compared to other interactions in the at least one computer network, and determining file-access patterns in the at least one computer network based on the network characteristics associated with file-access traffic.

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: Systems and methods of determining file-access patterns in at least one computer network, the network comprising a file-access server, including training a first machine learning (ML) algorithm with a first training dataset comprising vectors representing network traffic such that the first ML algorithm learns to determine network characteristics associated with file-access traffic, determining, using the first ML algorithm, network characteristics based on highest interaction of traffic with the file-access server compared to other interactions in the at least one computer network, and determining file-access patterns in the at least one computer network based on the network characteristics associated with file-access traffic.

Abstract: Systems and methods of determining a network performance property in at least one computer network, including: sampling traffic in active communication with the at least one computer network, analyzing the sampled traffic to group communication packets to flows, and predicting at least one network property of the at least one network based on the grouped communication packets and based on at least one traffic parameter in the at least one network, where the at least one traffic parameter is selected from the group consisting of: union of packet streams, intersection of packet streams, and differences of packet streams, and where the predicted at least one network property is selected from the group consisting of: total number of flows, number of flows with a predefined characteristic, number of packets, and volume of packets.

Abstract: Systems and methods of detecting communication anomalies in a computer network, including: analyzing sampled traffic within the computer network, to identify at least one entity in the computer network, generating a network graph that corresponds to the computer network, wherein the network graph includes a plurality of nodes based on the identified at least one entity, training a deep learning (DL) algorithm to generate at least one vector characterizing the behavior of each entity in the computer network based on the generated network graph, applying the trained DL algorithm on the sampled traffic, to predict the probability of a communication in the sampled traffic, wherein the prediction is based on the generated at least one vector, and detecting an anomaly when the predicted probability is below an anomaly threshold.

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 system and method may analyze computer actions on a computer desktop system. Using a data gathering process, a low-level user action information item, describing input by a user (e.g. to the computer desktop system), may be received or gathered. The low-level user action information item may include an input type description and screen window information. Based on a series of low-level user action information items, a process a computer is engaging in with the user may be estimated or determined. The best or most appropriate next low-level user action may be displayed or suggested to the user, e.g. on a computer desktop system to a user.

Abstract: A self-propelled printer is provided having communication circuitry to receive print data for an image to be formed. A drive mechanism of the printer to provides locomotion of the entire self-propelled printer and a print head is arranged to transfer a print material onto a print medium. Processing circuitry to generates an image formation path to be traversed by the print head via locomotion of the self-propelled printer. The image formation path is based at least in part on the received print data. The processing circuitry controls the drive mechanism to autonomously drive the self-propelled printer along the image formation path. A corresponding method and computer program for generating a 2D image using a self-propelled printer are provided.

Abstract: A system and method may analyze computer actions on a computer desktop system. Using a data gathering process, a low-level user action information item, describing input by a user (e.g. to the computer desktop system), may be received or gathered. The low-level user action information item may include an input type description and screen window information. Based on a series of low-level user action information items, a process a computer is engaging in with the user may be estimated or determined. The best or most appropriate next low-level user action may be displayed or suggested to the user, e.g. on a computer desktop system to a user.

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: Systems and methods of detecting communication anomalies in a computer network, including: applying a machine learning (ML) algorithm on sampled network traffic, wherein the ML algorithm is trained with a training dataset comprising vectors to identify an anomaly when the ML algorithm receives a new input vector representing sampled network traffic, normalizing a loss determined by the ML algorithm based on the output of the ML algorithm for the new input vector being different from the output of the ML algorithm for the training dataset, and applying the ML algorithm to analyze the normalized loss to identify an anomaly based on at least one communication pattern in the sampled network traffic, allowing a model trained in one installation to serve as a base model in another installation by normalizing the loss vectors of each installation.

Abstract: Systems and methods of determining file-access patterns in at least one computer network, the network comprising a file-access server, including training a first machine learning (ML) algorithm with a first training dataset comprising vectors representing network traffic such that the first ML algorithm learns to determine network characteristics associated with file-access traffic, determining, using the first ML algorithm, network characteristics based on highest interaction of traffic with the file-access server compared to other interactions in the at least one computer network, and determining file-access patterns in the at least one computer network based on the network characteristics associated with file-access traffic.

Abstract: Systems and methods of determining a network performance property in at least one computer network, including: sampling traffic in active communication with the at least one computer network, analyzing the sampled traffic to group communication packets to flows, and predicting at least one network property of the at least one network based on the grouped communication packets and based on at least one traffic parameter in the at least one network, where the at least one traffic parameter is selected from the group consisting of: union of packet streams, intersection of packet streams, and differences of packet streams, and where the predicted at least one network property is selected from the group consisting of: total number of flows, number of flows with a predefined characteristic, number of packets, and volume of packets.

Abstract: A system and method may analyze computer actions on a computer desktop system. Using a data gathering process, a low-level user action information item, describing input by a user (e.g. to the computer desktop system), may be received or gathered. The low-level user action information item may include an input type description and screen window information. Based on a series of low-level user action information items, a process a computer is engaging in with the user may be estimated or determined. The best or most appropriate next low-level user action may be displayed or suggested to the user, e.g. on a computer desktop system to a user.

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.