Abstract: A method for managing a client environment includes monitoring, by an upgrade management agent, the client environment for an application abstention by a plurality of client devices of the client environment, in response to the monitoring, obtaining device configuration information corresponding to each of the plurality of client devices, performing a text pre-processing on the device configuration information to obtain pre-processed information, applying a relational analysis to the pre-processed information to obtain a normalized context reference for application abstention corresponding to the plurality of client devices, and performing a clustering algorithm on the normalized context reference to generate a processed client intent dataset.

Abstract: An apparatus comprises a processing device configured to detect that a given software upgrade is available for a given computing device, to identify other computing devices on which the given software upgrade has been installed that exhibit at least a threshold level of similarity to the given computing device, and to determine whether any issues were encountered on the other computing devices as a result of the given software upgrade. The processing device is also configured to generate a recommendation as to whether to initiate download of the given software upgrade on the given computing device based at least in part on whether any issues were encountered on the other computing devices as a result of the given software upgrade, and to initiate download of the given software upgrade on the given computing device based at least in part on the generated recommendation.

Abstract: A method for managing a client environment includes monitoring, by an upgrade management agent, the client environment for an application abstention by a plurality of client devices of the client environment, in response to the monitoring, obtaining device configuration information corresponding to each of the plurality of client devices, performing a text pre-processing on the device configuration information to obtain pre-processed information, applying a relational analysis to the pre-processed information to obtain a normalized context reference for application abstention corresponding to the plurality of client devices, and performing a clustering algorithm on the normalized context reference to generate a processed client intent dataset.

Abstract: A method for managing a client environment includes obtaining a notification for an application abstention by a client device, in response to the notification, performing an intent analysis on the client device using a processed client intent dataset to determine a set of reasons for the application abstention, comparing the set of reasons to an upgrade coverage dataset, identifying, based on the comparing, a set of solutions corresponding to the set of reasons, generating, based on the comparing, a specialized upgrade notification based on the set of solutions, and issuing the specialized upgrade notification to the client device.

Abstract: A method and system for version history based upgrade testing across simulated information technology (IT) environments. At least with respect to computing, an upgrade may entail the replacement of a product - in the form of hardware, software, and/or firmware - with a newer or better version, which may serve to bring the product up to date, improve the characteristic(s) of the product, and/or resolve any issue(s) inflicting the product. Further, through the process of upgrade testing, an upgrade may be assessed to determine its impact on an operability of varying environments following application of the upgrade thereon. The disclosed method and system, accordingly, propose a framework directed to conducting upgrade tests within simulated IT environments - each reflective of different computing resources and upgrade histories - where results of the simulations may be examined to ascertain which environment configurations (if any) may cause the tested upgrade to fail.

Abstract: A method and system for machine learning based predictions of upgrade testing outcomes for information technology (IT) environments. At least with respect to computing, an upgrade may entail the replacement of a product—in the form of hardware, software, and/or firmware—with a newer or better version, which may serve to bring the product up to date, improve the characteristic(s) of the product, and/or resolve any issue(s) inflicting the product. Further, through the process of upgrade testing, an upgrade may be assessed to determine its impact on an operability of varying environments following application of the upgrade thereon. Moreover, each environment may reflect a different history of product upgrades that had been applied thereon prior to the upgrade being tested. The disclosed method and system, accordingly, propose a framework directed to ascertaining the post-upgrade outcomes of actual IT environments using machine learning applied to post-upgrade outcomes of simulated IT environments.

Abstract: In general, embodiments relate to a method for managing a technical support session, comprising: generating a question path graph (QPG) based on a plurality of question sequences associated with technical support sessions, and displaying at least a portion of the QPG to a technical support person (TSP) during a technical support session.

Abstract: In general, embodiments relate to a method for managing a technical support session, comprising: determining a technical support issue (TSI) for a technical support session; identifying a question path graph (QPG) associated with the TSI; and displaying at least a portion of the QPG to a technical support person (TSP) during the technical support session.

Abstract: A method comprises collecting operational data for one or more devices and identifying one or more anomalies associated with the one or more devices based at least in part on the collected operational data. At least a portion of the collected operational data corresponding to the identified one or more anomalies is analyzed, and a probability of automatic resolution for respective ones of the identified one or more anomalies is determined based at least in part on the analysis. The identifying, the analyzing and the determining are performed using one or more machine learning models.

Abstract: A method comprises collecting operational data for one or more devices and identifying one or more anomalies associated with the one or more devices based at least in part on the collected operational data. At least a portion of the collected operational data corresponding to the identified one or more anomalies is analyzed, and a probability of automatic resolution for respective ones of the identified one or more anomalies is determined based at least in part on the analysis. The identifying, the analyzing and the determining are performed using one or more machine learning models.

Abstract: An information handling system includes a data store configured to store an account associated with a first alert and a second alert. A processor may receive the first alert and the second alert, map the first alert and the second alert to a first node and a second node of a causality graph, traverse the causality graph starting from the sink node to the source node to determine an association between the first alert and the second alert, and generate the account based on the association between the first alert and the second alert.

Abstract: An apparatus comprises a processing device configured to detect that a given software upgrade is available for a given computing device, to identify other computing devices on which the given software upgrade has been installed that exhibit at least a threshold level of similarity to the given computing device, and to determine whether any issues were encountered on the other computing devices as a result of the given software upgrade. The processing device is also configured to generate a recommendation as to whether to initiate download of the given software upgrade on the given computing device based at least in part on whether any issues were encountered on the other computing devices as a result of the given software upgrade, and to initiate download of the given software upgrade on the given computing device based at least in part on the generated recommendation.

Abstract: A system and method model activities in the production environment as sequences of microservices, and identify unusual activities by analyzing these sequences. In particular, a directed graph of usual activity is formed as a basis for determining unusual activities. Next, activities that were actually performed are determined by statistically analyzing records of microservice invocation in application diagnostic files. These activity sequences are overlaid on the directed graph to determine relative fit by using a trace coverage percentage score. Application instances or activities with low relative fit are deemed suspicious. If the low fit persists for an extended duration, then the instances or activities are deemed unusual and an individual is alerted to begin a manual review.

Abstract: A system and method model activities in the production environment as sequences of microservices, and identify unusual activities by analyzing these sequences. In particular, a directed graph of usual activity is formed as a basis for determining unusual activities. Next, activities that were actually performed are determined by statistically analyzing records of microservice invocation in application diagnostic files. These activity sequences are overlaid on the directed graph to determine relative fit by using a trace coverage percentage score. Application instances or activities with low relative fit are deemed suspicious. If the low fit persists for an extended duration, then the instances or activities are deemed unusual and an individual is alerted to begin a manual review.