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    Optimizing revenue savings for actionable predictions of revenue change
    1.
    发明授权
    Optimizing revenue savings for actionable predictions of revenue change 有权

    公开(公告)号：US11042891B2

    公开(公告)日：2021-06-22

    申请号：US15614163

    申请日：2017-06-05

    申请人： International Business Machines Corporation

    发明人： Jeanette L. Blomberg ,  Abhinav Maurya ,  Aly Megahed ,  Hovey R. Strong, Jr.

    IPC分类号： G06Q30/02 ,  G06Q40/00 ,  G06Q10/04 ,  G06N7/00 ,  G06F16/248 ,  G06N5/00 ,  G06N3/12

    摘要： One embodiment provides optimizing potential revenue savings when predicting client revenue change including receiving revenue data with timestamps for a number of historical periods at a particular level, with attributes of the particular level and a percentage of the required revenue change. The data is filtered. The filtered data is aggregated at the particular level for a selected prediction. A sliding window of the number of historical periods is moved over business periods, creating a data point for each historical period temporal window by extracting features. A required target output is created for each data point for at least one future time period. A weight is assigned to each data point proportional to value of revenue. A model is trained to optimize a weighted linear combination of losses over each data point. A set of recent histories is converted into a quantitative health value.

    PREDICTING LEDGER REVENUE CHANGE BEHAVIOR OF CLIENTS RECEIVING SERVICES
    2.
    发明申请
    PREDICTING LEDGER REVENUE CHANGE BEHAVIOR OF CLIENTS RECEIVING SERVICES 审中-公开

    公开(公告)号：US20180349928A1

    公开(公告)日：2018-12-06

    申请号：US15614146

    申请日：2017-06-05

    申请人： International Business Machines Corporation

    发明人： Jeanette L. Blomberg ,  Abhinav Maurya ,  Aly Megahed ,  Hovey R. Strong, JR.

    IPC分类号： G06Q30/02 ,  G06Q40/00 ,  G06Q10/04 ,  G06F17/30

    摘要： One embodiment provides a method for predicting revenue change in a ledger including receiving, by a processor device, revenue data with timestamps for a number of historical periods at a particular level, with attributes of the particular level and a percentage of the required revenue change. The data is filtered. The filtered data is aggregated at the particular level for a selected prediction. A sliding window of the number of historical periods is moved over business periods, creating a data point for each historical period temporal window by extracting features. A required target output is created for each data point for at least one future time period. A statistical classification model is trained to predict the revenue change. A set of recent histories is converted into a quantitative health value.

    SELECTING REPRESENTATIVE MODELS
    7.
    发明申请
    SELECTING REPRESENTATIVE MODELS 审中-公开
    选择代表性模型

    公开(公告)号：US20150100369A1

    公开(公告)日：2015-04-09

    申请号：US14046861

    申请日：2013-10-04

    申请人： INTERNATIONAL BUSINESS MACHINES CORPORATION

    发明人： Jeanette L. Blomberg ,  Neil H.A. Boyette ,  Anca A. Chandra ,  Se Chan Oh ,  Hovey R. Strong, JR.

    IPC分类号： G06Q10/06

    CPC分类号： G06Q10/06315

    摘要： Embodiments of the present invention provide a system, method and computer program product for selecting representative models. A method comprises generating a first data model representing a first aggregation level, and generating multiple additional data models. Each additional data model represents a lower aggregation level than the first data model. For each additional data model, a corresponding score is determined. For each lower aggregation level, a corresponding combined score is determined based on two or more highest scoring additional data models representing the lower aggregation level. The method further comprises reporting a second aggregation level and a set of data models. The second aggregation level is a lower aggregation level having the highest combined score over all other lower aggregation levels. The set of data models comprises two, or more, highest scoring additional data models representing the second aggregation level.

    摘要翻译： 本发明的实施例提供一种用于选择代表性模型的系统，方法和计算机程序产品。 一种方法包括生成表示第一聚合级别的第一数据模型，以及生成多个附加数据模型。 每个附加数据模型表示比第一个数据模型更低的聚合级别。 对于每个附加数据模型，确定相应的分数。 对于每个较低的聚合级别，基于表示较低聚合级别的两个或更多个最高得分的附加数据模型来确定相应的组合分数。 该方法还包括报告第二聚合级别和一组数据模型。 第二个聚合级别是在所有其他较低聚合级别上具有最高组合得分的较低聚合级别。 该组数据模型包括表示第二聚合级别的两个或更多个最高得分的附加数据模型。

    Meeting room reservation system
    8.
    发明授权
    Meeting room reservation system 有权

    公开(公告)号：US11188878B2

    公开(公告)日：2021-11-30

    申请号：US14861959

    申请日：2015-09-22

    申请人： INTERNATIONAL BUSINESS MACHINES CORPORATION

    发明人： Jeanette L. Blomberg ,  Anca A. Chandra ,  Pawan R. Chowdhary ,  Susanne M. Glissmann-Hochstein ,  Sunhwan Lee ,  Hovey Raymond Strong, Jr.

    IPC分类号： G06Q10/10

    摘要： Embodiments of the present invention provide a method comprising maintaining historical meeting information, receiving an event data stream corresponding to a meeting, and delaying confirmation of an assignment of a meeting room for the meeting for a period of delay defined by a confirmation condition to predict a number of in-person attendees at the meeting based on the event data stream and the historical meeting information. The meeting room is tentatively assigned to the meeting based on the predicted number of in-person attendees. The method further comprises sending confirmation of the assignment of the meeting room for the meeting to at least one invitee only after the period of delay has elapsed.

    QUANTITATIVE DISCOVERY OF NAME CHANGES
    10.
    发明申请
    QUANTITATIVE DISCOVERY OF NAME CHANGES 审中-公开

    公开(公告)号：US20190220780A1

    公开(公告)日：2019-07-18

    申请号：US16367046

    申请日：2019-03-27

    申请人： International Business Machines Corporation

    发明人： Jeanette L. Blomberg ,  Anca A. Chandra ,  Pawan R. Chowdhary ,  Se Chan Oh ,  Hovey R. Strong, JR. ,  Suppawong Tuarob

    IPC分类号： G06N20/00 ,  G06Q10/06

    CPC分类号： G06N20/00 ,  G06Q10/06375

    摘要： Embodiments of the present invention provide a method for detecting a temporal change of name associated with performance data. The method comprises receiving at least one candidate name replacement pair comprising a pair of names. The method further comprises, in a training stage, for each known name replacement pair included in the performance data, determining a window of time covering a most recent appearance of a first name of the known name replacement pair. The window of time is determined based on quantitative features of a time series model comprising performance data for the first name and a second name of the known name replacement pair. The method further comprises, in the training stage, training a machine learning classifier based on quantitative features computed using a portion of the performance data for the first name and the second name, where the portion is within the window of time determined.