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    BENCHMARK SCALABILITY FOR SERVICES
    1.
    发明申请
    BENCHMARK SCALABILITY FOR SERVICES 审中-公开

    公开(公告)号：US20200050993A1

    公开(公告)日：2020-02-13

    申请号：US16102622

    申请日：2018-08-13

    Applicant: International Business Machines Corporation

    Inventor： Shubhi Asthana ,  Valeria Becker ,  Aly Megahed ,  Michael E. Rose ,  Brian D. Yost ,  Taiga Nakamura ,  Hovey R. Strong, JR.

    IPC: G06Q10/06 ,  G06Q30/02

    Abstract: A computer-implemented method, according to one embodiment, includes: receiving an offer request including one or more desired services, and selecting available offerings, each of which include at least one of the desired services. A determination is made whether available benchmarks exist for each of the at least one desired service included in each of the selected available offerings. For each desired service determined as not having available benchmarks, a draft benchmark is computed for each of a plurality of criteria. A confidence weight is also computed for each of the draft benchmarks. The available benchmarks, the draft benchmarks, and the confidence weights are further used to construct an offer which is submitted in response to the received offer request. Moreover, the draft benchmarks and the corresponding confidence weights are re-computed for each of the respective desired services in response to determining that the submitted offer was not accepted.

    QUANTITATIVE DISCOVERY OF NAME CHANGES
    2.
    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

    Applicant: International Business Machines Corporation

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

    IPC: G06N20/00 ,  G06Q10/06

    CPC classification number: G06N20/00 ,  G06Q10/06375

    Abstract: 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.

    SYSTEMS AND TECHNIQUES FOR RECOMMENDING PERSONALIZED HEALTH CARE BASED ON DEMOGRAPHICS
    3.
    发明申请
    SYSTEMS AND TECHNIQUES FOR RECOMMENDING PERSONALIZED HEALTH CARE BASED ON DEMOGRAPHICS 审中-公开

    公开(公告)号：US20180113982A1

    公开(公告)日：2018-04-26

    申请号：US15331630

    申请日：2016-10-21

    Applicant: International Business Machines Corporation

    Inventor： Shubhi Asthana ,  Hovey R. Strong, JR.

    IPC: G06F19/00 ,  G06F17/30 ,  G06N5/04

    CPC classification number: G06F16/9535 ,  G06F16/2246 ,  G06F16/288 ,  G06N5/045 ,  G16H10/60 ,  G16H50/20 ,  G16H50/70 ,  G16H70/60

    Abstract: Computer program products are configured to perform methods for determining likely health conditions based on demographic information and/or determining appropriate wearable technology and services to monitor a patient's health. In one embodiment, a computer program product is configured to perform a method including receiving historical demographic data comprising a plurality of attributes; associating the historical demographic data with labels corresponding to known causes of particular health conditions; building a decision tree model using the historical demographic data and the associated label(s); generating a vector Yk using the model, Yk representing probable causes of a plurality of health conditions; and determining likely health conditions for a patient based on comparing the vector Yk to a second vector Zk, Zk representing probable causes of health conditions determined based on a health care record for the patient. Appropriate wearables for tracking the health of the patient may be determined using textual analysis.

    QUANTITATIVE DISCOVERY OF NAME CHANGES
    4.
    发明申请
    QUANTITATIVE DISCOVERY OF NAME CHANGES 审中-公开
    定量发现名称变更

    公开(公告)号：US20160358097A1

    公开(公告)日：2016-12-08

    申请号：US14728926

    申请日：2015-06-02

    Applicant: International Business Machines Corporation

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

    IPC: G06N99/00 ,  G06F17/30

    CPC classification number: G06N20/00 ,  G06Q10/06375

    Abstract: 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.

    Abstract translation: 本发明的实施例提供了一种用于检测与演奏数据相关联的名称的时间变化的方法。 该方法包括接收包括一对名称的至少一个候选名称替换对。 该方法还包括在训练阶段中针对包括在演奏数据中的每个已知名称替换对，确定涵盖已知名称替换对的名字的最近出现的时间窗口。 时间窗口基于包括名字的性能数据和已知名称替换对的第二名称的时间序列模型的定量特征来确定。 该方法还包括在训练阶段中，基于使用第一名称和第二名称的性能数据的一部分计算的定量特征来训练机器学习分类器，其中该部分在确定的时间窗口内。

    EXTRAPOLATING A TIME SERIES
    5.
    发明申请
    EXTRAPOLATING A TIME SERIES 审中-公开
    超级时间系列

    公开(公告)号：US20150100367A1

    公开(公告)日：2015-04-09

    申请号：US14046849

    申请日：2013-10-04

    Applicant: International Business Machines Corporation

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

    IPC: G06Q10/06

    CPC classification number: G06Q10/06315

    Abstract: Embodiments of the present invention provide a system, method and computer program product for extrapolating a time series. A method comprises receiving multiple sequences of data values over time. Each sequence of data values is partitioned into a corresponding plurality of segments comprising at least one rising segment that rises to a peak data value of the sequence of data values and at least one falling segment that falls to a trough data value of the sequence of data values. For each sequence of data values, a corresponding sequence of segments that rise and fall alternately is generated based on a corresponding plurality of segments for the sequence of data values. An aggregated sequence of segments is generated by aggregating each sequence of segments generated. The aggregated sequence of segments represents a typical model for the sequences of data values.

    Abstract translation: 本发明的实施例提供了一种用于外推时间序列的系统，方法和计算机程序产品。 一种方法包括随时间接收多个数据值序列。 数据值的每个序列被划分成对应的多个段，包括至少一个上升段，其上升到数据值序列的峰值数据值，以及至少一个下降段，其落入数据序列的低谷数据值 价值观。 对于每个数据值序列，基于用于数据值序列的对应的多个段来生成对应的交替上升和下降的段的序列。 通过聚合生成的每个片段序列来生成片段的聚合序列。 段的聚合序列表示数据值序列的典型模型。

    STABILIZING CONSUMER ENERGY DEMAND
    6.
    发明申请
    STABILIZING CONSUMER ENERGY DEMAND 审中-公开

    公开(公告)号：US20200059096A1

    公开(公告)日：2020-02-20

    申请号：US16661893

    申请日：2019-10-23

    Applicant: International Business Machines Corporation

    Inventor： Raphael I. Arar ,  Sandeep Gopisetty ,  Hovey R. Strong, JR.

    IPC: H02J3/14 ,  H02J3/32 ,  H02J7/35

    Abstract: A computer-implemented method, according to one embodiment, includes: setting a target power demand corresponding to a consumer, and performing a process. The process includes: determining an actual power demand presented to a utility by the consumer, and determining a current error. The current error is the difference between the actual power demand and the target power demand. A determination is also made as to whether the actual power demand is adjustable in a direction that reduces the current error. In response to determining that the actual power demand is adjustable in the direction that reduces the current error, the current error is reduced by adjusting the actual power demand. Moreover, in response to determining that the actual power demand is not adjustable in the direction that reduces the current error, the target power demand is modified.

    PREDICTING LEDGER REVENUE CHANGE BEHAVIOR OF CLIENTS RECEIVING SERVICES
    7.
    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

    Applicant: International Business Machines Corporation

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

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

    Abstract: 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.

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