TASK-BASED BIOMETRIC DIFFERENTIATOR OF STRESS LEVELS TO MAXIMIZE PRODUCTIVITY
    1.
    发明申请
    TASK-BASED BIOMETRIC DIFFERENTIATOR OF STRESS LEVELS TO MAXIMIZE PRODUCTIVITY 审中-公开
    基于任务的生物能力差异化应力水平最大化生产力

    公开(公告)号:US20170046643A1

    公开(公告)日:2017-02-16

    申请号:US14821978

    申请日:2015-08-10

    摘要: An aspect includes receiving a request to assign a task to a user based on biometric data. Correlated data that correlates characteristics of tasks previously performed by the user with observed productivity and stress levels of the user while performing the tasks is accessed. An optimization model is applied to select a task that maximizes a predicted productivity of the user in performing the task while at the same time minimizes a predicted negative stress level of the user while performing the task. Input to the optimization model includes the correlated data and characteristics of a plurality of potential tasks. The selected task is assigned to the user. The correlated data for the user is updated based on the observed productivity of the user in performing the selected task, the observed stress level of the user while performing the selected tax, and at least one characteristic of the selected task.

    摘要翻译: 一方面包括基于生物特征数据接收向用户分配任务的请求。 访问将用户先前执行的任务的特征与执行任务时用户的观察到的生产力和压力水平相关联的相关数据。 应用优化模型来选择在执行任务时使用户的预测生产率最大化的任务,同时在执行任务的同时将用户的预测负应力水平最小化。 优化模型的输入包括相关数据和多个潜在任务的特征。 所选任务被分配给用户。 基于用户在执行所选择的任务时观察到的生产率,用户在执行所选税时观察到的压力水平以及所选择的任务的至少一个特征来更新用户的相关数据。

    Processing performance analyzer and process manager

    公开(公告)号:US10025624B2

    公开(公告)日:2018-07-17

    申请号:US15085472

    申请日:2016-03-30

    摘要: Embodiments include method, systems and computer program products for z Integrated Information Processors (zIIP) processing performance analysis and process management. In some embodiments, at least a portion of an application may be executed using a general-purpose processor of a mainframe computing device, wherein the at least the portion of the application comprises a zIIP-enabled process. A first set of data associated with the general-purpose processor may be collected. At least a portion of the application may be executed using a zIIP of the mainframe computing device. A second set of data associated with the zIIP may be collected. An efficiency percentage may be calculated using the first set of data and the second set of data. A portion of the application may be authorized to execute on the zIIP based on the efficiency percentage.

    Task-based biometric differentiator of stress levels to maximize productivity

    公开(公告)号:US10380531B2

    公开(公告)日:2019-08-13

    申请号:US14879240

    申请日:2015-10-09

    摘要: An aspect includes receiving a request to assign a task to a user based on biometric data. Correlated data that correlates characteristics of tasks previously performed by the user with observed productivity and stress levels of the user while performing the tasks is accessed. An optimization model is applied to select a task that maximizes a predicted productivity of the user in performing the task while at the same time minimizes a predicted negative stress level of the user while performing the task. Input to the optimization model includes the correlated data and characteristics of a plurality of potential tasks. The selected task is assigned to the user. The correlated data for the user is updated based on the observed productivity of the user in performing the selected task, the observed stress level of the user while performing the selected tax, and at least one characteristic of the selected task.

    Task-based biometric differentiator of stress levels to maximize productivity

    公开(公告)号:US10380530B2

    公开(公告)日:2019-08-13

    申请号:US14821978

    申请日:2015-08-10

    摘要: An aspect includes receiving a request to assign a task to a user based on biometric data. Correlated data that correlates characteristics of tasks previously performed by the user with observed productivity and stress levels of the user while performing the tasks is accessed. An optimization model is applied to select a task that maximizes a predicted productivity of the user in performing the task while at the same time minimizes a predicted negative stress level of the user while performing the task. Input to the optimization model includes the correlated data and characteristics of a plurality of potential tasks. The selected task is assigned to the user. The correlated data for the user is updated based on the observed productivity of the user in performing the selected task, the observed stress level of the user while performing the selected tax, and at least one characteristic of the selected task.