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1. 发明申请

US20230004918A1 AUTOMATIC REMOTE TASK ALLOCATION VIA VIRTUAL TRAINING 有权
公开(公告)号：US20230004918A1
公开(公告)日：2023-01-05
申请号：US17363663
申请日：2021-06-30
申请人： International Business Machines Corporation
发明人： Fearghal O'Donncha , Paulito Palmes , Albert Akhriev
IPC分类号： G06Q10/06 , G06F16/28
摘要： A processor may identify a first task of a set of tasks. The processor may identify features of the first task. The processor may generate a reputation assessment for a first user related to the features of the first task. The processor may match the first user to the first task based on the reputation assessment.

2. 发明申请

US20230004843A1 DECISION OPTIMIZATION UTILIZING TABULAR DATA 有权
公开(公告)号：US20230004843A1
公开(公告)日：2023-01-05
申请号：US17363542
申请日：2021-06-30
申请人： International Business Machines Corporation
发明人： Radu Marinescu , Akihiro Kishimoto , Paulito Palmes , Martin Wistuba
IPC分类号： G06N5/04 , G06N20/00 , G06F16/22 , G06K9/62
摘要： A computer-implemented method for automated policy decision making optimization is disclosed. The computer-implemented method includes creating a dataset from a tabular database, wherein the dataset includes one or more columns selected as state variables, a column selected as action variables, and a column selected as reward variables. The computer-implemented method further includes determining a candidate function approximator Q based on applying at least one state variable, one action variable, and one reward variable to a trained regression model. The computer-implemented method further includes learning a decision policy based on applying the candidate function approximator Q to a reinforcement learning algorithm. The computer-implemented method further includes determining, based on the learned decision policy, an expected reward.

3. 发明授权

US11074513B2 Disruption forecasting in complex schedules 有权
公开(公告)号：US11074513B2
公开(公告)日：2021-07-27
申请号：US14657378
申请日：2015-03-13
申请人： International Business Machines Corporation
发明人： Randall L. Cogill , Jakub Marecek , Martin Mevissen , Paulito Palmes , Robert Shorten , Fabian R. Wirth
IPC分类号： G06N7/00 , G06Q10/06 , G06N20/00
摘要： A method for forecasting time delays added to a scheduled start time and a scheduled end time of a task includes generating a stochastic model of the task and resources affecting the task, the stochastic model includes a reactionary delay component that is a function of previous task end times and a root cause delay component that is an independent random process at a specific time. The method further includes: calculating a probability distribution of time delays added to the scheduled start time as a combination of the reactionary delay component and the root cause delay component using the stochastic model to provide a probability distribution of start times; and calculating a probability distribution of time delays added to the scheduled end time as a combination of the reactionary delay component and the root cause delay component using the stochastic model to provide a probability distribution of end times.

4. 发明授权

US11023816B2 Disruption forecasting in complex schedules 有权
公开(公告)号：US11023816B2
公开(公告)日：2021-06-01
申请号：US14746282
申请日：2015-06-22
申请人： International Business Machines Corporation
发明人： Randall L. Cogill , Jakub Marecek , Martin Mevissen , Paulito Palmes , Robert Shorten , Fabian R. Wirth
IPC分类号： G06N7/00 , G06Q10/06 , G06N20/00
摘要： A method for forecasting time delays added to a scheduled start time and a scheduled end time of a task includes generating a stochastic model of the task and resources affecting the task, the stochastic model includes a reactionary delay component that is a function of previous task end times and a root cause delay component that is an independent random process at a specific time. The method further includes: calculating a probability distribution of time delays added to the scheduled start time as a combination of the reactionary delay component and the root cause delay component using the stochastic model to provide a probability distribution of start times; and calculating a probability distribution of time delays added to the scheduled end time as a combination of the reactionary delay component and the root cause delay component using the stochastic model to provide a probability distribution of end times.

5. 发明授权

US10690548B2 Environmental factor assessment by a non-intrusive sensor in a fluid transfer pumping system 有权
公开(公告)号：US10690548B2
公开(公告)日：2020-06-23
申请号：US15689108
申请日：2017-08-29
申请人： INTERNATIONAL BUSINESS MACHINES CORPORATION
发明人： Niall Brady , Liam S. Harpur , Paulito Palmes
IPC分类号： G01K1/02 , G01F1/704 , G01F15/06 , G06Q50/06 , G06Q10/10 , G01K3/10 , G01F1/708
摘要： Embodiments for assessing energy usage efficiency in a fluid transfer pumping system in a cloud computing environment by a processor. A rate of temperature decay may be determined over a selected time period using a temperature signal collected by one or more non-intrusive Internet of Things (IoT) sensors located at one or more selected positions of a piping network in the fluid transfer pumping system so as to determine energy efficiency in the fluid transfer pumping system associated with a heating service, a cooling service, or combination thereof.

6. 发明申请

US20160267391A1 DISRUPTION FORECASTING IN COMPLEX SCHEDULES 审中-公开
公开(公告)号：US20160267391A1
公开(公告)日：2016-09-15
申请号：US14746282
申请日：2015-06-22
申请人： International Business Machines Corporation
发明人： Randall L. Cogill , Jakub Marecek , Martin Mevissen , Paulito Palmes , Robert Shorten , Fabian R. Wirth
IPC分类号： G06N7/00 , G06N99/00
CPC分类号： G06N7/005 , G06N20/00 , G06Q10/06311 , G06Q10/0635
摘要： A method for forecasting time delays added to a scheduled start time and a scheduled end time of a task includes generating a stochastic model of the task and resources affecting the task, the stochastic model includes a reactionary delay component that is a function of previous task end times and a root cause delay component that is an independent random process at a specific time. The method further includes: calculating a probability distribution of time delays added to the scheduled start time as a combination of the reactionary delay component and the root cause delay component using the stochastic model to provide a probability distribution of start times; and calculating a probability distribution of time delays added to the scheduled end time as a combination of the reactionary delay component and the root cause delay component using the stochastic model to provide a probability distribution of end times.

7. 发明申请

US20160267390A1 DISRUPTION FORECASTING IN COMPLEX SCHEDULES 审中-公开
标题翻译：复杂时间表中的破坏预测
公开(公告)号：US20160267390A1
公开(公告)日：2016-09-15
申请号：US14657378
申请日：2015-03-13
申请人： International Business Machines Corporation
发明人： Randall L. Cogill , Jakub Marecek , Martin Mevissen , Paulito Palmes , Robert Shorten , Fabian R. Wirth
IPC分类号： G06N7/00 , G06N99/00 , G06Q10/06
CPC分类号： G06N7/005 , G06N20/00 , G06Q10/06311 , G06Q10/0635
摘要： A method for forecasting time delays added to a scheduled start time and a scheduled end time of a task includes generating a stochastic model of the task and resources affecting the task, the stochastic model includes a reactionary delay component that is a function of previous task end times and a root cause delay component that is an independent random process at a specific time. The method further includes: calculating a probability distribution of time delays added to the scheduled start time as a combination of the reactionary delay component and the root cause delay component using the stochastic model to provide a probability distribution of start times; and calculating a probability distribution of time delays added to the scheduled end time as a combination of the reactionary delay component and the root cause delay component using the stochastic model to provide a probability distribution of end times.
摘要翻译：用于预测添加到任务的预定开始时间和预定结束时间的时间延迟的方法包括生成任务的随机模型和影响任务的资源，随机模型包括作为先前任务结束的函数的反动延迟分量时间和根本原因延迟分量，这是特定时间的独立随机过程。该方法还包括：使用随机模型来计算添加到调度的开始时间的时间延迟的概率分布，作为反动延迟分量和根本原因延迟分量的组合，以提供开始时间的概率分布; 以及使用所述随机模型计算添加到所述预定结束时间的时间延迟的概率分布作为所述反动延迟分量和所述根本原因延迟分量的组合，以提供结束时间的概率分布。

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