Abstract: In one aspect, an example methodology implementing the disclosed techniques includes, by a computing device, retrieving touchpoint data of a product and creating a data element based on the retrieved touchpoint data, the data element includes information about a touchpoint of the product by a stakeholder. The method also includes, by the computing device, adding the created data element to a product pedigree list, wherein the product pedigree list is a pedigree list which contains data elements of the product. The method may further include, by the computing device, storing the product pedigree list in a graph database. The method may further include, by the computing device, predicting, using a machine learning (ML) model (e.g., an autoencoder), whether a state of the product pedigree list is normal or anomalous.

Abstract: Methods, apparatus, and processor-readable storage media for automatically performing varied security scans on distributed files using machine learning techniques are provided herein. An example computer-implemented method includes obtaining at least one input file from one of multiple source channels; identifying a data security scan operation, from a set of multiple data security scan operations, for the at least one input file by processing historical data attributed to the at least one input file using machine learning techniques; executing the identified data security scan operation on the at least one input file; generating a hash of the at least one input file and information pertaining to results of the executed data security scan operation; caching the generated hash in at least one cache; and performing automated actions based on the caching of the generated hash in the at least one cache.

Abstract: In one aspect, an example methodology implementing the disclosed techniques includes receiving information regarding a customer to onboard to a managed service and determining one or more relevant features from the information regarding the customer, the one or more relevant features correlated with historical onboarding times. The method also includes determining, using a machine learning (ML) model, an expected time to onboard the customer to the managed service based on the one or more relevant features.

Abstract: Methods, apparatus, and processor-readable storage media for automatically prioritizing supply chain-related demand using artificial intelligence techniques are provided herein.

Abstract: In one aspect, an example methodology implementing the disclosed techniques includes receiving a new application programming interface (API) specification and extracting one or more keywords from the new API specification. The method also includes identifying, using a trained machine learning (ML) model, one or more existing API specifications that are similar to the new API specification based on the one or more keywords from the new API specification and, responsive to the identification, outputting information regarding the one or more existing API specifications that are similar to the new API specification.

Abstract: Methods, apparatus, and processor-readable storage media for automatically predicting fail-over of message-oriented middleware systems are provided herein. An example computer-implemented method includes obtaining one or more message-oriented middleware parameter values for at least a portion of multiple message-oriented middleware systems; detecting one or more fail-over-related anomalies associated with at least one of the multiple message-oriented middleware systems by processing at least a portion of the one or more message-oriented middleware parameter values using one or more machine learning techniques; and automatically migrating, based at least in part on the one or more detected fail-over-related anomalies, at least a portion of data associated with the at least one message-oriented middleware system associated with the one or more detected fail-over-related anomalies to at least one of the other of the multiple message-oriented middleware systems.

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 comprises receiving at least one natural language input, converting the at least one natural language input to a graphical input, retrieving relationship data from a graph database based at least in part on the graphical input, and generating at least one natural language response of a virtual assistant to the at least one natural language input based at least in part on the relationship data from the graph database. At least the generating is performed using one or more machine learning models.

Abstract: In one aspect, an example methodology implementing the disclosed techniques includes, by a warranty system, receiving a customer-specific usage-related data for a product at a customer location and generating a feature vector for the product, wherein the feature vector represents one or more features from the customer-specific usage-related data. The method also includes, by the warranty system using a trained incident prediction module, predicting a number of future incidents for the product at the customer location based on the first feature vector. The method further includes, by the warranty system, determining a price for an extended warranty for the product based on the predicted number of future incidents for the product at the customer location.

Abstract: Methods, apparatus, and processor-readable storage media for generating temporal predictions for provisioning cloud resources using trained machine learning techniques are provided herein. An example computer-implemented method includes training one or more machine learning techniques using historical data pertaining to cloud resource provisioning and related order information; obtaining data pertaining to at least one order related to one or more hardware components, wherein the at least one order includes one or more cloud resources; generating at least one temporal prediction for provisioning the one or more cloud resources, in fulfillment of the at least one order, by processing at least a portion of the obtained data using the one or more trained machine learning techniques; and performing one or more automated actions based at least in part on the at least one generated temporal prediction.

Abstract: A method comprises collecting parameters corresponding to processing by a first application programming interface of at least one application programming interface transaction, analyzing the parameters using one or more machine learning algorithms, and predicting, based at least in part on the analyzing, whether the at least one application programming interface transaction is anomalous. In the method, the first application programming interface is designated as being in an anomalous state responsive to predicting that the at least one application programming interface transaction is anomalous. One or more application programming interface requests for the first application programming interface are routed to a second application programming interface responsive to the anomalous state designation.

Abstract: Methods, apparatus, and processor-readable storage media for a neural network-based message communication framework with summarization and on-demand audio output generation are provided herein.

Abstract: An example methodology implementing the disclosed techniques includes receiving a parts configuration specified for a product and generating a first feature vector that represents features from the product. The method also includes predicting, using a trained quote-time issue prediction module, whether the parts configuration specified for the product will or will not result in issues based on the first feature vector and, responsive to a prediction that the parts configuration specified for the product will not result in issues, accepting an order for the product. The method may further include receiving manufacturing details selected for the product, generating a second feature vector that represents features from the product and the selected manufacturing details, and predicting, using a trained manufacture-time issue prediction module, whether producing the product in accordance with the selected manufacturing details will or will not result in issues based on the second feature vector.

Abstract: In one aspect, an example methodology implementing the disclosed techniques includes, by a computing device, determining a source platform code for migration from a source platform to a target platform and determining one or more attributes of the source platform code. The method also includes determining, using a machine learning (ML) model, one or more existing templates based on the one or more attributes of the source platform code, and recommending the one or more existing templates for use in generating a template for migration of the source platform code to the target platform. The template for the source platform code is configured to convert the source platform code to a target platform code suitable for the target platform. The one or more existing template can then be used to generate a template for migrating the source platform code to a target platform code suitable for the target platform.

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: A method comprises receiving at least one natural language input, and determining an intent of the at least one natural language input. In the method, a virtual assistant of a plurality of virtual assistants is recommended to respond to the at least one natural language input based at least in part on the determined intent, and the at least one natural language input is transmitted to the recommended virtual assistant. The determining and recommending are performed using one or more machine learning models, and the plurality of virtual assistants respectively correspond to a plurality of different functions of an enterprise.

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: A method comprises extracting first task data from a first data source corresponding to a first application and second task data from a second data source corresponding to a second application, and comparing the first task data to the second task data using one or more natural language processing techniques. In the method, one or more matching tasks between the first task data and the second task data are identified based at least in part on the comparing. Code of at least one of the first application and the second application is analyzed to determine whether the code of at least one of the first application and the second application implements the one or more matching tasks.

Abstract: A method comprises collecting message-oriented-middleware system parameters from a plurality of message-oriented-middleware clusters, analyzing the parameters using one or more machine learning algorithms, and predicting, based at least in part on the analyzing, at least one anomaly in a message-oriented-middleware cluster of the plurality of message-oriented-middleware clusters. In the method, message metadata is collected from the message-oriented-middleware cluster, and at least part of the message metadata is transmitted to one or more remaining ones of the plurality of message-oriented-middleware clusters. At least the part of the message metadata corresponds to messaging operations to be transferred from the message-oriented-middleware cluster to the one or more remaining ones of the plurality of message-oriented-middleware clusters.

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.