An error cause estimation device comprises a feature value generation unit for using data transmitted from the outside to generate feature values suitable for a machine learning model; a model database having a plurality of error prediction models, for determining whether an error has occurred using the feature values as input data; a model evaluation unit for evaluating the performance of an error prediction model by comparing a prediction result of the error prediction model and an actually measured error; a model selection unit for selecting from the model database an error prediction model for which an evaluation value calculated by the model evaluation unit is greater than or equal to a preset defined value; and an error prediction model generation unit for generating a new error prediction model with respect to the measured error when no corresponding error prediction model has been selected by the model selection unit.

An information analysis apparatus according to an embodiment includes an input unit that inputs information indicating an apparatus that is a fault occurrence location in a communication network and a factor of a fault, a storage unit that stores a restoration handling method rule in which the apparatus that is the fault occurrence location, the factor of the fault, and an appropriate restoration handling method for the fault are associated, and an analyzer that analyzes an appropriate restoration handling method for the apparatus and the factor indicated by the information that is input, based on information related to a past fault for the apparatus that is the fault occurrence location, or a condition inherent in the apparatus that is the fault occurrence location, from the restoration handling method rule.

A system and method for dynamic script generation for automated filing services is provided. In embodiments, a method includes: initiating a clickstream recording of an electronic document filing interface of a remote platform based on a triggering event; generating a clickstream recording of the electronic document filing interface, wherein the clickstream recording comprising a recording of a navigation of the electronic document filing interface through multiple steps of a document filing process, wherein the clickstream recording is in the form of scripts associated with each of the multiple steps of the document filing process; and generating automated filing instructions for the electronic document filing interface using the clickstream recording, the automated filing instructions enabling computer automated submission of one or more documents to the remote platform via the electronic document filing interface.

Technologies are disclosed herein for cross-correlating metrics for anomaly root cause detection. Primary and secondary metrics associated with an anomaly are cross-correlated by first using the derivative of an interpolant of data points of the primary metric to identify a time window for analysis. Impact scores for the secondary metrics can be then be generated by computing the standard deviation of a derivative of data points of the secondary metrics during the identified time window. The impact scores can be utilized to collect data relating to the secondary metrics most likely to have caused the anomaly. Remedial action can then be taken based upon the collected data in order to address the root cause of the anomaly.

An electronic computing system preserves a pre-error state of a processing unit by receiving a first stream of inputs; buffering the first stream of inputs to generate a buffered stream of inputs identical to the first stream of inputs; conveying the first stream to a primary instance of a first program; conveying the buffered stream to a secondary instance of the first program; executing the primary instance on the first stream in real time; executing the secondary instance on the buffered stream with a predefined time delay with respect to execution of the primary instance on the first stream; detecting an error state resulting from execution of the primary instance; and in response to detecting the error state, pausing the secondary instance and preserving a current state of the secondary instance, wherein the current state of the secondary instance corresponds to a pre-error state of the primary instance.

Systems and methods for proactive support of computing assets are presented. In contrast to existing techniques of reactive support, the proactive support techniques disclosed herein automatically collect operating data from a plurality of computing devices, analyze the operating data to identify predictive indicators associated with error conditions, identify a subset of affected computing devices that match the predictive indicators, and execute corrective scripts to remediate or avoid such error conditions before problems are experienced on the affected computing devices. The operating data may be used to train a machine learning model in order to identify the predictive indicators associated with each error condition. In some embodiments, the corrective scripts may be automatically generated to adjust operating parameters or applications of the affected computing devices based upon the identified predictive indicators.

A method for detecting and recovery from a soft error in a computing device is provided. In examples discussed herein, the method can be performed to detect soft errors that may occur during execution of a predefined critical instruction(s) and/or has been propagated in the computing device prior to the execution of the predefined critical instruction(s). Specifically, a software compiler may be used to embed an error detector block(s) after the predefined critical instruction(s). In this regard, the error detector block(s) can be executed after the predefined critical instruction(s) to detect the soft error. Accordingly, it may be possible to invoke a diagnosis routine to determine severity of the detected soft error and take appropriate action against the detected soft error. As such, it may be possible to protect the execution of the predefined critical instruction(s) concurrent to eliminating vulnerable voting intervals and reducing soft error detection overhead.

Systems, computer program products, and methods are described herein for monitoring and automatically controlling batch processing. The present invention may be configured to receive a plurality of data processing requests and determine a processing plan for the plurality of data processing requests. The present invention may be configured to provide, to processing applications and based on the processing plan, actions for performance by the processing applications to complete the plurality of data processing requests. The present invention may be configured to determine a state of the plurality of data processing requests, determine, using an event state decision machine learning model, remedial actions to resolve an error state, and provide instructions to the processing applications to perform the remedial actions.

A system displays a fault tree of a generated event on the basis of input information including diagnosis result information representing results of diagnosis of the cause of the generated event. The input information includes information representing causal relationships between a plurality of elements, including the generated event, failure causes that may be a cause of the event, and check items associated with the failure causes. The diagnosis result information includes an occurrence probability of each failure cause. The system determines, as highlighting target edges, all edges belonging to a path from a node corresponding to the generated event to a node corresponding to a failure cause having an occurrence probability that satisfies predetermined probability conditions, and all or some edges coupling the node corresponding to the failure cause having an occurrence probability that satisfies the predetermined probability conditions to nodes corresponding to check items associated with the failure cause.

Embodiments of the present disclosure provide a system for continuous and real-time management and monitoring of robotic process automation bots. In particular, the architecture of the system may comprise a centralized hub which provides various features and functions for bot management and monitoring, such as real-time health status updates, granular logging and notification functions, failure detection and reporting for debugging, bot inventory systems, or the like. Through the use of the components and/or features as described herein, the system may provide an efficient way to manage and monitor robotic process automation bots within a computing environment.