Abstract: A component tracking system includes a plurality of devices that each include a device component monitoring subsystem that monitors for and records the addition and/or removal of device components to/from that device. A device component manager system that is coupled to each of the plurality of devices identifies, via a recording by a first device monitoring subsystem in a first device included in the plurality of devices, the removal of a first device component from the first device. The device component manager system then determines, via a recording by a second device component monitoring subsystem in a second device included in the plurality of devices, that the first device component has been added to the second device within a time period and, in response, the device component manager device prevents a removed first device component alarm.

Abstract: A method includes extracting data pertaining to a plurality of user actions in connection with one or more changes to one or more of a plurality of applications, and training one or more machine learning models with the extracted data. The one more machine learning models are used to predict whether a user should receive a given notification in connection with a given change to a given application of the plurality of applications. In response to predicting that the user should receive the given notification, content of the given notification is determined. The method further includes generating the given notification for the user, and transmitting the given notification to the user.

Abstract: An apparatus comprises a processing device configured to identify, at a first microservice, a service call that is to be transmitted to a second microservice, and to modify the service call to include context information, the context information characterizing a current state of execution of one or more tasks by one of the first microservice and the second microservice. The processing device is further configured to provide, from the first microservice to the second microservice, the modified service call including the context information. The context information enables re-initiation of said one of the first microservice and the second microservice to continue execution of the one or more tasks from the current state.

Abstract: Methods, apparatus, and processor-readable storage media for automatically predicting device failure using machine learning techniques are provided herein. An example computer-implemented method includes obtaining telemetry data from at least one client device; predicting failure of at least a portion of the at least one client device by processing at least a portion of the telemetry data using a first set of one or more machine learning techniques; predicting lifespan information pertaining to at least a portion of the at least one client device by processing the predicted failure and at least a portion of the telemetry data using a second set of one or more machine learning techniques; and performing at least one automated action based at least in part on one or more of the predicted failure and the predicted lifespan information.

Abstract: One example method, includes checking, by an intelligent time detector service running on a computing system, a trial period duration of software on the computing system, starting, by the intelligent time detector service, a counter for the software, and the counter is operable to generate a counter value based on a clock of the computing system, storing, by the intelligent time detector service, a counter value of the counter, and decommissioning, by the intelligent time detector service, the software after the trial period has expired.

Abstract: Techniques described herein relate to a method for managing support cases of clients. The method may include obtaining a support case associated with a client of the clients; in response to obtaining the support case, obtaining contextual information associated with the support case; obtaining client telemetry data associated with the support case; selecting a portion of training data that is similar to the support case based on the client telemetry data, the contextual information, and the support case; generating a prediction model using the portion of the training data and a classification algorithm; and generating predictions using the prediction model, the client telemetry data, the contextual information, and the support case.

Abstract: A method for managing a client environment includes identifying, by an application upgrade monitoring agent, the application upgrade for an application, in response to the identifying: obtaining device configuration information associated with a client device executing an instance of the application, wherein the client device is executing on the client environment, sending an upgrade time estimation request to a device emulation system, wherein the upgrade time estimation request comprises the device configuration information, obtaining, in response to the upgrade time estimation request, an upgrade estimation from the device emulation system, wherein the upgrade estimation specifies a period of time the application upgrade is expected to take to complete on the client device, and initiating execution of the application upgrade on the client device based on the upgrade estimation.

Abstract: A method for managing a client environment includes obtaining, by a client device upgrade manager, an upgrade estimation for a client device executing in the client environment, wherein the upgrade estimation corresponds to an application upgrade for an application, in response to the upgrade estimation: performing an optimal time slot analysis for the client device to identify a set of optimal time slots, presenting the set of optimal time slots to the client device, obtaining, by the client device, a requested time slot for the application, and in response to the requested time slot, initiating an installation of an application upgrade of the application.

Abstract: In general, embodiments relate to a method for managing a technical support (TS) session on a technical support system. The embodiments include receiving TS correspondence from a client, wherein the TS correspondence is associated with the TS session, classifying the TS correspondence to assign it a question classification, based on the question classification, making a determination that the TS correspondence matches at least one prior received TS correspondence, wherein the at least one prior received TS correspondence is associated with the TS session, and visually identifying the TS correspondence and the at least one prior received TS correspondence on a graphical customer interface (GUI) of the technical support system.

Abstract: A method for managing a client environment includes obtaining, by a device emulation orchestration engine in an emulation system, an upgrade estimation time request associated with an application upgrade, in response to the upgrade estimation time request: performing a device emulation container analysis to determine a client device that requires the application upgrade, wherein the client device executes in the client environment, initiating an upgrade emulation using a device emulation container corresponding to the client device, obtaining, from a device emulation agent executing in the device emulation container, an upgrade estimation, and providing the upgrade estimation to the application upgrade monitoring agent.

Abstract: An apparatus comprises a processing device configured to identify a given one of one or more assets in an information technology infrastructure associated with a support indicator associated with a priority level, and to obtain information characterizing state transitions of the one or more assets. The processing device is also configured to determine, based at least in part on a current state of the given asset identified utilizing the obtained information, a probability of the given asset transitioning to each of a plurality of states and to select, based at least in part on the determined probabilities, one of the plurality of states as a predicted future state of the given asset utilizing conformal prediction. The processing device is further configured to modify the priority level of the support indicator associated with the given asset based at least in part on the predicted future state of the given asset.

Abstract: An apparatus comprises a processing device configured to receive, at a host operating system of a virtual machine host, a request to execute a virtual machine and to obtain, from a virtual trusted platform module running on the virtual machine host, credentials for logging in to a guest operating system of the virtual machine. The processing device is further configured to provide, to pre-boot authentication software associated with the virtual machine, the credentials obtained from the virtual trusted platform module, and to automatically log in to the guest operating system of the virtual machine utilizing the pre-boot authentication software and the provided credentials.

Abstract: An apparatus comprises a processing device configured to collect system state information from host devices, to split the collected system state information into logical chunks, and to determine, based at least in part on a plurality of factors, a compression level to be applied to each of the logical chunks. The plurality of factors comprise a first factor characterizing a time at which the collected system state information is needed at a destination device and at least a second factor characterizing resources available for at least one of performing compression of the collected system state information and transmitting the collected system state information over at least one network to the destination device. The processing device is further configured to apply the determined compression level to each of the logical chunks to generate compressed logical chunks, and to transmit the compressed logical chunks to the destination device.

Abstract: A system and method intelligently collect performance data from managed electronic devices. A machine learning model (e.g. linear time series forecasting) is used to predict a future workload for each of a selection of devices, and a regression analysis is used to predict how long is likely to be required to collect performance state from each component of each device. These data are then mapped together to predict future overall idle periods of each device, together with components whose performance data may be collected during those periods. The components are grouped in batches according to a relevance order that itself may be determined by applying a machine learning model such as k-nearest neighbors. Then, performance data are collected according to the batches. In this way, performance data may be collected in chunks while avoiding a negative impact on execution of the primary functions of the managed devices.

Abstract: A method for managing applications includes obtaining, by a client in a customer environment, an upgrade issue report for the application, making a first determination that a resynchronization of a client self-healing classification model with the production host environment (PHE) self-healing classification model is required, wherein the PHE self-healing classification model is stored in the PHE, performing the resynchronization with the PHE self-healing classification model to obtain a synchronized client self-healing classification model, applying the synchronized client self-healing classification model to the upgrade issue report to obtain a state of the upgrade issue report, making a second determination that the state indicates a self-healable state, based on the second determination, performing a self-healing process on the application based on the upgrade issue report, and storing a resolution report based on results of the self-healing process, wherein the PHE is operatively connected to the custome

Abstract: A method for managing applications includes obtaining an application resolution report from an administrative system, wherein the application resolution report comprises a customer query and a response, applying a neural network algorithm on a set of tokens associated with the application resolution report to obtain a vector representation of the application resolution report, performing a probability analysis on each vector in the vector representation, based on the probability analysis, identifying an unanswered portion of the application resolution report, and in response to the unanswered portion of the application resolution report, performing a query resolution.

Abstract: An information handling system receives a search query determines a first key attribute associated with a first search term and a second key attribute associated with a second search term, determines an intent of a user and context of the search query based on the first key attribute, and determines whether the search result corresponding to the search query includes an image with a first feature corresponding to the first key attribute and a second feature corresponding to the second key attribute. Responsive to a determination that the search result does not include the image with the first feature and the second feature, the system may generate a clubbed image that includes a first image and a second image, wherein the first image includes the first feature and the second image includes the second feature.

Abstract: Methods, apparatus, and processor-readable storage media for artificial intelligence-based redundancy management are provided herein. An example computer-implemented method includes obtaining telemetry data from one or more client devices within at least one system; predicting one or more hardware component failures in at least a portion of the one or more client devices within the at least one system by processing at least a portion of the telemetry data using a first set of one or more artificial intelligence techniques; determining, using a second set of one or more artificial intelligence techniques, one or more redundant hardware components for implementation in connection with the one or more predicted hardware component failures; and performing at least one automated action based at least in part on the one or more redundant hardware components.

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: Techniques for localization of a content presentation in an information processing system are provided. In one example, a method adapts translated content to accommodate an original content design structure (e.g., wireframe) when such translated content is inconsistent with the original content design structure. In another example, a method adapts the original content design structure when translated content is inconsistent with the original content design structure. In yet another example, an image analysis method is used to adapt translated content determined to be inconsistent with the original content design structure.