Abstract: In an approach, one or more computer processors determine that a user is in a vehicle. The one or more computer processors identify historical vehicle occupancy data of the user. The one or more computer processors monitor one or more driving metrics of the vehicle while the user is an occupant. The one or more computer processors determine whether a vehicle exceeds a threshold based, at least in part, on a comparison of the monitored one or more driving metrics and the identified historical vehicle occupancy data. The one or more computer processors determine an action based, at least in part, on the vehicle exceeding the threshold.

Abstract: The present invention provides a method, a system, and a computer program product of determining tasks of a project and determining personality traits of the tasks. In an embodiment, the method, the system, and the computer program product include in response to receiving a set of data for a current project, generating, by one or more processors, a set of one or more current tasks for the current project based on at least a first machine learning associated with one or more preexisting projects, generating a set of one or more personality traits for a current task in the set of one or more current tasks based on at least a second machine learning associated with one or more preexisting tasks, and compiling the set of one or more current tasks and the set of one or more personality traits into one or more current task profiles.

Abstract: Aspects of the present invention provide an approach for processing a notification on a mobile device. In an embodiment, a set of classifications is assigned to a notification received by a user on a mobile device. An expected response action and an expected response time specific to the user is derived based on a comparison of the set of classifications assigned to the notification with a set of prior response profiles in a notification tracking repository. If the user does not perform a response action within the expected response time, the notification is reissued to the user.

Abstract: An approach is provided in which a fault-injecting system injects a natural language fault into a first text segment to produce a second text segment that are both written in a natural language. The fault-injecting system receives a third text segment from a reviewer that includes at least one correction to the second text segment. The fault-injecting system compares the third text segment against the first text segment and generates an efficacy score. The efficacy score indicates whether the correction in the third text segment corrects the natural language fault. In turn, the fault-injecting system sends the efficacy score to an author of the first text segment.

Abstract: In an approach, one or more computer processors determine that a user is in a vehicle. The one or more computer processors identify historical vehicle occupancy data of the user. The one or more computer processors monitor one or more driving metrics of the vehicle while the user is an occupant. The one or more computer processors determine whether a vehicle exceeds a threshold based, at least in part, on a comparison of the monitored one or more driving metrics and the identified historical vehicle occupancy data. The one or more computer processors determine an action based, at least in part, on the vehicle exceeding the threshold.

Abstract: An approach is provided in which a fault-injecting system injects a natural language fault into a first text segment to produce a second text segment that are both written in a natural language. The fault-injecting system receives a third text segment from a reviewer that includes at least one correction to the second text segment. The fault-injecting system compares the third text segment against the first text segment and generates an efficacy score. The efficacy score indicates whether the correction in the third text segment corrects the natural language fault. In turn, the fault-injecting system sends the efficacy score to an author of the first text segment.

Abstract: An approach is provided in which a fault-injecting system injects a natural language fault into a first text segment to produce a second text segment that are both written in a natural language. The fault-injecting system receives a third text segment from a reviewer that includes at least one correction to the second text segment. The fault-injecting system compares the third text segment against the first text segment and generates an efficacy score. The efficacy score indicates whether the correction in the third text segment corrects the natural language fault. In turn, the fault-injecting system sends the efficacy score to an author of the first text segment.

Abstract: The present invention provides a method, a system, and a computer program product of determining tasks of a project and determining personality traits of the tasks. In an embodiment, the method, the system, and the computer program product include in response to receiving a set of data for a current project, generating, by one or more processors, a set of one or more current tasks for the current project based on at least a first machine learning associated with one or more preexisting projects, generating a set of one or more personality traits for a current task in the set of one or more current tasks based on at least a second machine learning associated with one or more preexisting tasks, and compiling the set of one or more current tasks and the set of one or more personality traits into one or more current task profiles.

Abstract: Aspects of the present invention provide an approach for processing a notification on a mobile device. In an embodiment, a set of classifications is assigned to a notification received by a user on a mobile device. An expected response action and an expected response time specific to the user is derived based on a comparison of the set of classifications assigned to the notification with a set of prior response profiles in a notification tracking repository. If the user does not perform a response action within the expected response time, the notification is reissued to the user.

Abstract: Aspects of the present invention provide an approach for processing a notification on a mobile device. In an embodiment, a set of classifications is assigned to a notification received by a user on a mobile device. An expected response action and an expected response time specific to the user is derived based on a comparison of the set of classifications assigned to the notification with a set of prior response profiles in a notification tracking repository. If the user does not perform a response action within the expected response time, the notification is reissued to the user.

Abstract: Aspects of the present invention provide an approach for processing a notification on a mobile device. In an embodiment, a set of classifications is assigned to a notification received by a user on a mobile device. An expected response action and an expected response time specific to the user is derived based on a comparison of the set of classifications assigned to the notification with a set of prior response profiles in a notification tracking repository. If the user does not perform a response action within the expected response time, the notification is reissued to the user.

Abstract: Systems and methods for personality-based cognitive team member selection are disclosed. In embodiments, a computer-implemented method, comprises: retrieving from multiple remote data resources, by a computing device, communication data authored by a participant; processing, by the computing device, the communication data to produce formatted data; analyzing, by the computing device, the formatted data to obtain personality trait scores for corresponding personality traits based on the formatted data; generating, by the computing device, a user profile for the participant including the personality trait scores; and storing, by the computing device, the user profile in a database.