Abstract: Systems, methods, and computer-readable storage media for building knowledge graphs which map the aspects of entities, then transforming the graphs into vectors for a similarity comparison. The resulting vectors can be used to identify skills and competencies of individuals, which the system uses to compare those individuals to others. The system can then quantify who would make for a good replacement for a given individual and make an associated recommendation.

Abstract: Systems, methods, and computer-readable storage media for recommending computational commands based on a weighted, ranked hierarchical graph. The system converts a map of intended user actions to canonical actions (commands) into a graph representation. Then, via a chatbot algorithm, the system receives an initial intent as a request by a user of the chatbot algorithm. Based on the popularity of alternative canonical actions, the system makes recommendations of alternative actions which are similar to the initial intent provided by the user.

Abstract: A method for predicting customer churn includes receiving a graph data structure storing data associated with activity of a user, the graph data structure having multiple nodes, including a user input node associated with the user. The method includes updating at least the user input node with a vector representation of a received user input, and using historical user input, training a sentiment model to classify user input according to one of multiple sentiments. The method includes using the trained sentiment model to classify the received user input as a particular sentiment, and adding to the graph data structure a sentiment node that is associated with the particular sentiment and that is connected to the user input node. The method includes, using the graph data structure, training a churn model to estimate customer churn probability, and using the trained churned model to estimate a particular churn probability for the user.

Abstract: Systems, methods, and computer-readable storage media for identifying and generating insights using machine learning and serverless computing systems. When events are detected, data from those events is collected and formatted into predefined formats. That formatted data is then used within an instance, where a machine learning algorithm is executed by a serverless computing system using the data within the instance as input. The result from the machine learning algorithm is an insight into the event and event data, which can be presented to users for interpretation.

Abstract: Systems, methods, and computer-readable storage media for recommending computational commands based on a weighted, ranked hierarchical graph. The system converts a map of intended user actions to canonical actions (commands) into a graph representation. Then, via a chatbot algorithm, the system receives an initial intent as a request by a user of the chatbot algorithm. Based on the popularity of alternative canonical actions, the system makes recommendations of alternative actions which are similar to the initial intent provided by the user.

Abstract: Systems, methods, and computer-readable storage media for identifying and generating insights using machine learning and serverless computing systems. When events are detected, data from those events is collected and formatted into predefined formats. That formatted data is then used within an instance, where a machine learning algorithm is executed by a serverless computing system using the data within the instance as input. The result from the machine learning algorithm is an insight into the event and event data, which can be presented to users for interpretation.

Abstract: Systems, methods, and computer-readable storage media for building knowledge graphs which map the aspects of entities, then transforming the graphs into vectors for a similarity comparison. The resulting vectors can be used to identify skills and competencies of individuals, which the system uses to compare those individuals to others. The system can then quantify who would make for a good replacement for a given individual and make an associated recommendation.

Abstract: A method receives an electronic image and uses the image as an input to a neural network. Based on a determination that the image represents a document, the method uses the image as an input to another neural network to identify a portion of the document containing an identifier. The method extracts the identifier by performing character recognition on the identified portion and determines whether the identifier is valid by using a validation API to determine whether the identifier is associated with a valid account at an institution. Based on a determination that the identifier is associated with a valid account, the method authorizes a transaction associated with the identifier. Based on a determination that the identifier is not associated with a valid account, the method denies the transaction. The first neural network classifies the electronic image into one of multiple valid document types and an invalid document type.

Abstract: Systems, methods, and computer-readable storage media for using graph theory to identify conflicts of interests between entities. A system can receive first data from a first entity, the first data identifying relationships associated with the first entity, as well as second data from a second entity, the second data identifying relationships associated with the second entity. The system can also receive information about the first and second entity being competitors, and using the first data, second data, and competitor data, construct a knowledge graph regarding the relationships between the entities. The system can then analyze the graph model, resulting in a subset of the graph model which identifies any competitive relationships. This subset can then be output to the first and/or second entity.

Abstract: Systems, methods, and computer-readable storage media for recommending computational commands based on a weighted, ranked hierarchical graph. The system converts a map of intended user actions to canonical actions (commands) into a graph representation. Then, via a chatbot algorithm, the system receives an initial intent as a request by a user of the chatbot algorithm. Based on the popularity of alternative canonical actions, the system makes recommendations of alternative actions which are similar to the initial intent provided by the user.

Abstract: Systems, methods, and computer-readable storage media for identifying and generating insights using machine learning and serverless computing systems. When events are detected, data from those events is collected and formatted into predefined formats. That formatted data is then used within an instance, where a machine learning algorithm is executed by a serverless computing system using the data within the instance as input. The result from the machine learning algorithm is an insight into the event and event data, which can be presented to users for interpretation.

Abstract: Examples disclosed herein relate to collecting a plurality of sensor data associated with a hard disk drive, calculating a health factor for the hard disk drive according to the plurality of sensor data, calculating a health offset for the hard disk drive according to the plurality of sensor data, and generating a remaining lifetime forecast for the hard disk drive according to an estimated overall lifetime for the hard disk drive, the health factor for the hard disk drive, and the health offset for the hard disk drive.

Abstract: An example method of collecting an n-component subset of resource usage data descriptive of usage of resources of a device or a system includes normalizing a set of resource usage data. Normalizing the set of resource usage data includes comparing usage of a first resource by a first component with usage of the first resource by other components.

Abstract: A technique includes, for each storage drive of a plurality of storage drives, determining a metric associated with the storage drive based at least in part on a plurality of attributes associated with the storage drive; and determining a health of a given storage drive of the plurality of storage drives based at least in part on the metric associated with the given storage drive and the metric associated with at least one other storage drive of the plurality of storage drives.

Abstract: In some examples, a computing device can determine a data state from telemetry data received from a plurality of client devices, determine a state change using the data state determined from the telemetry data, determine, using the state change, a potential cause of a fault of a component of a client device of the plurality of client devices, where the potential cause of the fault is determined in response to receiving a support inquiry from the client device, and display a timeline of state changes, where the timeline includes the determined state change of the client device.

Abstract: A technique includes, for each storage drive of a plurality of storage drives, determining a metric associated with the storage drive based at least in part on a plurality of attributes associated with the storage drive; and determining a health of a given storage drive of the plurality of storage drives based at least in part on the metric associated with the given storage drive and the metric associated with at least one other storage drive of the plurality of storage drives.

Abstract: In some examples, a computing device can determine a data state from telemetry data received from a plurality of client devices, determine a state change using the data state determined from the telemetry data, determine, using the state change, a potential cause of a fault of a component of a client device of the plurality of client devices, where the potential cause of the fault is determined in response to receiving a support inquiry from the client device, and display a timeline of state changes, where the timeline includes the determined state change of the client device.