Abstract: A method comprises receiving product selection data, wherein the product selection data characterizes at least one combination of at least two products. In the method, the product selection data is analyzed using one or more machine learning algorithms. The method further comprises predicting based, at least in part, on the analyzing, whether the at least one combination is anomalous. One or more alerts are generated in response to predicting that the at least one combination is anomalous.

Abstract: Methods, apparatus, and processor-readable storage media for automated database operation classification using artificial intelligence techniques are provided herein. An example computer-implemented method includes obtaining at least one query related to one or more database operations; obtaining information pertaining to a set of multiple storage resources; classifying the at least one query as associated with at least one of multiple subsets of storage resources among the set of multiple storage resources by processing at least a portion of the query and at least a portion of the information pertaining to the set of multiple storage resources using one or more artificial intelligence techniques; and performing one or more automated actions based at least in part on the classifying of the at least one query.

Abstract: A method comprises receiving a request to predict an amount of at least one resource for at least one hosting instance of one or more microservices. Using one or more machine learning models, the amount of the at least one resource is predicted in response to the request. The at least one hosting instance is generated based, at least in part, on the predicted amount. In some embodiments, the at least one resource comprises, for example, a memory and/or a CPU, and the amount of the at least one resource comprises a size of the memory and/or a number of CPU core units.

Abstract: A method comprises receiving an input of at least one image associated with an issue. In the method, text is extracted from the at least one image, and an intent is determined from the extracted text. The method further includes recommending a response to the issue based at least in part on the determined intent, and transmitting the recommended response to a user. The extracting, determining and recommending are performed at least in part using one or more machine learning models.

Abstract: In some examples, a computing device may receive, from an email server, an email rule comprising conditions and actions and determine a subset of archived emails stored on the computing device that satisfy the conditions. The computing device may perform the actions to the subset of archived emails, such as placing a legal hold on the subset of archived emails, creating and sending copies of the subset of archived emails to the email server, or deleting, from the computing device, the subset of archived emails. After determining that a user is attempting to perform a prohibited action on (e.g., delete, edit, forward) an email of the subset of archived emails, the computing device may perform a protective action, such as logging the user out of an email account, preventing the user from logging in to the email account, automatically sending an email to an administrator or the like.

Abstract: In one aspect, an example methodology implementing the disclosed techniques includes, by a virtual event assistant, joining an online meeting and receiving a content of the online meeting in real-time, the content including an audio stream and a video stream of the online meeting. The method also includes, by the virtual event assistant, generating a summarized content of the online meeting based on a transcript of the online meeting, wherein generating the summarized content includes applying one or more artificial intelligence (AI)-based techniques to the transcript of the online meeting. The method further includes, by the virtual meeting assistant, determining contextual metadata of the online meeting based on analysis of the content of the online meeting. The method may also include providing the summarized content with the contextual metadata of the online meeting for playback by a user, for example.

Abstract: Methods, apparatus, and processor-readable storage media for automatically managing event-related communication data using machine learning techniques are provided herein.

Abstract: A method comprises collecting parameters corresponding to at least one microservice operation processed by a first instance of a microservice, and analyzing the parameters using one or more machine learning algorithms. Based at least in part on the analyzing, a prediction is made whether the at least one microservice operation is anomalous. In the method, the first instance of the microservice is designated as being in an anomalous state responsive to predicting that the at least one microservice operation is anomalous. One or more microservice requests for the microservice are routed to a second instance of the microservice responsive to the anomalous state designation.

Abstract: Methods, apparatus, and processor-readable storage media for automatically classifying cloud infrastructure components for prioritized multi-tenant cloud environment resolution using artificial intelligence techniques are provided herein. An example computer-implemented method includes obtaining historical data pertaining to a multi-tenant cloud environment; training one or more artificial intelligence techniques, using at least a portion of the obtained historical data, for classifying cloud infrastructure components for prioritizing incident-related resolution; classifying one or more cloud infrastructure components, within the multi-tenant cloud environment and associated with one or more server-related issues, into one or more of multiple resolution priority classes; and performing one or more automated actions based at least in part on the classifying of the one or more cloud infrastructure components.

Abstract: A method may include receiving data from a device within a network, wherein the data is associated with one or more features of the device, and determining a subset of the features of the device that is associated with a runtime behavior of the device. The method may also perform a univariate analysis on a feature dataset that is associated with the subset of the features of the device, perform a multivariate analysis on the feature dataset that is associated with correlated features in the subset of the features, and generate a device signature based on the univariate analysis and the multivariate analysis.

Abstract: An apparatus comprises a processing device configured to generate a model of a plurality of devices characterizing relationships between the devices, to build a device dependency chain for the devices based on the model, to predict workload for each of the devices in one or more time slots of a given time period, and to determine a deployment schedule for the devices based on the device dependency chain and the predicted workload. The processing device is also configured to utilize the deployment schedule to select a device of the devices on which to perform an operation, to determine whether the selected device corresponds to an additional device of the devices configured to operate in place of the selected device during performance of the operation, and to control performance of the operation on the selected device responsive to the determination of whether the selected device corresponds to the additional device.

Abstract: In one aspect, an example methodology implementing the disclosed techniques includes, by a product subscription service, receiving information regarding a hardware asset being returned at an end of a subscription and predicting, using a first machine learning (ML) model, whether the hardware asset has reached EOL. The method also includes, responsive to predicting that the hardware asset has reached EOL, creating, by the product subscription service, a work order to dispatch an eco-partner. The method may further include, by the product subscription service, responsive to predicting that the hardware asset has not reached EOL, predicting, using a second ML model, one or more new subscription orders matching the hardware asset and recommending the one or more matching new subscription orders as possible fits for the hardware asset.

Abstract: Methods, apparatus, and processor-readable storage media for automatically classifying images for storage-related determinations using artificial intelligence techniques are provided herein. An example computer-implemented method includes obtaining at least one image associated with an enterprise; classifying one or more portions of the at least one image into one or more enterprise-related categories by processing at least a portion of the at least one image using one or more artificial intelligence techniques; and performing, based at least in part on the classifying of the one or more portions of the at least one image, one or more automated actions pertaining to storing the at least one image in at least one of multiple storage options.

Abstract: Methods, apparatus, and processor-readable storage media for generating and processing personal information chains using machine learning techniques are provided herein. An example computer-implemented method includes processing data, from one or more data sources, pertaining to one or more events involving an individual; generating a personal information chain associated with the individual by processing at least a portion of the processed data using at least one cryptographic function and linking that at least a portion of the processed data in accordance with at least one temporal parameter; performing anomaly detection by processing at least a portion of the personal information chain associated with the individual using one or more machine learning techniques; and performing one or more automated actions based at least in part on one or more of the personal information chain and results from the anomaly detection.

Abstract: A method comprises analyzing a plurality of natural language inputs associated with at least one user, and determining a plurality of contexts for the plurality of natural language inputs based, at least in part, on the analysis. In the method, a plurality of relationships linked to the at least one user are identified based, at least in part, on the analysis, and the at least one user is classified in one or more categories based, at least in part, on the plurality of contexts and the plurality of relationships. At least one of the analyzing, determining, identifying and classifying is performed using one or more machine learning models.

Abstract: A method including: generating a conversation flow signature based on a set of communication transcripts, each of the communication transcripts being associated with a support request for a product, each of the communication transcripts being a text transcript of a communication between a respective customer and a respective customer support agent; classifying the conversation flow signature into one of a plurality of categories, the conversation flow signature being classified by using a machine learning classifier that is trained based on customer support records, each of the plurality of categories corresponding to a respective set of steps for configuring or repairing the product; and outputting an indication of the respective set of steps that is associated with the category in which the conversation flow signature is classified.

Abstract: Methods, apparatus, and processor-readable storage media for automated configuration determinations for data center devices using artificial intelligence are provided herein. An example computer-implemented method includes obtaining input information pertaining to one or more device-related changes to a data center; obtaining telemetry data attributed to one or more devices in the data center; determining one or more device configurations for implementation in at least one device in the data center in connection with the one or more device-related changes by processing the input information and the obtained telemetry data using one or more artificial intelligence techniques; and performing at least one automated action based at least in part on the one or more determined device configurations.

Abstract: A method comprises receiving at least one natural language input corresponding to at least one task, and analyzing the at least one natural language input using one or more machine learning models to determine one or more parameters of the at least one task. One or more skills and/or availabilities of respective ones of a plurality of resources are identified. The method further comprises identifying at least a subset of the plurality of resources to perform the at least one task based at least in part on the one or more parameters and on the one or more skills and/or the availabilities. The at least one task is routed to one or more resources of the subset.

Abstract: In one aspect, an example methodology implementing the disclosed techniques includes, by a survey workflow module, receiving a survey request event for which a survey is requested and generating a first feature vector for the survey request event, wherein the first feature vector comprises features extracted from the survey request event. The method also includes predicting, by a first trained prediction module, whether the survey request event will result in a survey based on the first feature vector, wherein the first trained prediction module is trained using a training dataset generated from a corpus of historical survey request event-survey data.

Abstract: Methods, apparatus, and processor-readable storage media for generating data replication configurations using AI techniques are provided herein. An example computer-implemented method includes obtaining input data pertaining to at least one data replication operation; determining a set of configuration parameters for the at least one data replication operation by applying one or more AI techniques to at least a portion of the input data; and performing one or more automated actions based at least in part on the determined set of configuration parameters for the at least one data replication operation.