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    REAL-TIME OPPORTUNITY DISCOVERY FOR PRODUCTIVITY ENHANCEMENT
    33.
    发明申请
    REAL-TIME OPPORTUNITY DISCOVERY FOR PRODUCTIVITY ENHANCEMENT 有权

    公开(公告)号：US20220101225A1

    公开(公告)日：2022-03-31

    申请号：US17037850

    申请日：2020-09-30

    Applicant: International Business Machines Corporation

    Inventor： Nianjun Zhou ,  Dharmashankar Subramanian ,  WingHang Crystal Lui

    IPC: G06Q10/06 ,  G06N3/04

    Abstract: In an approach for real-time opportunity discovery for productivity enhancement of a production process, a processor extracts a set of features from time series data, through autoencoding using a neural network, based on non-control variables for the time series data. A processor identifies one or more operational modes based on the extracted features including a dimensional reduction with a representation learning from the time series data. A processor identifies a neighborhood of a current operational state based on the extracted features. A processor compares the current operational state to historical operational states based on the time series data at the same operational mode. A processor discovers an operational opportunity based on the comparison of the current operational state to the historical operational states using the neighborhood. A processor identifies control variables in the same mode which variables are relevant to the current operational state.

    Resource reallocation based on expected rewards
    34.
    发明授权
    Resource reallocation based on expected rewards 有权

    公开(公告)号：US10970389B2

    公开(公告)日：2021-04-06

    申请号：US15860278

    申请日：2018-01-02

    Applicant: International Business Machines Corporation

    Inventor： Janusz Marecki ,  Fei Fang ,  Dharmashankar Subramanian

    IPC: G06F21/55 ,  G06F9/50 ,  G06F21/57

    Abstract: Methods and systems for determining a reallocation of resources are described. A device may determine initial allocation data that indicates a first amount of resources allocated to a plurality of areas. The device may determine a set of attacker expected rewards based on the initial allocation data. The device may determine a set of defender expected rewards based on the attacker expected rewards. The device may determine moving rewards indicating defensive scores in response to movement of the resources among the plurality of areas. The device may determine defender response rewards indicating defensive scores resulting from an optimal attack on the plurality of areas. The device may generate reallocation data indicating an allocation of a second amount of resources to the plurality of areas. The second amount of resources may maximize the moving rewards and the defender response rewards.

    RESOURCE REALLOCATION BASED ON EXPECTED REWARDS
    36.
    发明申请
    RESOURCE REALLOCATION BASED ON EXPECTED REWARDS 审中-公开

    公开(公告)号：US20190205534A1

    公开(公告)日：2019-07-04

    申请号：US15860278

    申请日：2018-01-02

    Applicant: International Business Machines Corporation

    Inventor： Janusz Marecki ,  Fei Fang ,  Dharmashankar Subramanian

    IPC: G06F21/55 ,  G06F9/50 ,  G06F21/57

    CPC classification number: G06F21/554 ,  G06F9/5016 ,  G06F21/552 ,  G06F21/577 ,  G06F2221/034

    Abstract: Methods and systems for determining a reallocation of resources are described. A device may determine initial allocation data that indicates a first amount of resources allocated to a plurality of areas. The device may determine a set of attacker expected rewards based on the initial allocation data. The device may determine a set of defender expected rewards based on the attacker expected rewards. The device may determine moving rewards indicating defensive scores in response to movement of the resources among the plurality of areas. The device may determine defender response rewards indicating defensive scores resulting from an optimal attack on the plurality of areas. The device may generate reallocation data indicating an allocation of a second amount of resources to the plurality of areas. The second amount of resources may maximize the moving rewards and the defender response rewards.

    METHOD AND APPLICATION FOR BUSINESS INITIATIVE PERFORMANCE MANAGEMENT
    38.
    发明申请
    METHOD AND APPLICATION FOR BUSINESS INITIATIVE PERFORMANCE MANAGEMENT 审中-公开
    业务性能绩效管理的方法与应用

    公开(公告)号：US20150339604A1

    公开(公告)日：2015-11-26

    申请号：US14282000

    申请日：2014-05-20

    Applicant: International Business Machines Corporation

    Inventor： Iqbal Alikhan ,  Pu Huang ,  Tarun Kumar ,  Margaret A. Marx ,  Bonnie K. Ray ,  Dharmashankar Subramanian ,  Sanjay Tripathi ,  Shanchi Zhan

    IPC: G06Q10/06

    CPC classification number: G06Q10/0635 ,  G06Q10/0639 ,  G06Q10/067

    Abstract: A method including, for a set of historical and/or ongoing business initiatives, determining key negative and positive performance factors by a computer from a structured taxonomy of negative and positive performance factors stored in a memory; modeling at least one of the performance factors for the ongoing business initiative or a new business initiative at at least one level of the hierarchical taxonomy. The key negative and positive performance factors are modeled based, at least partially, upon a likelihood of occurrence of the key negative performance factors during the business initiative, and based, at least partially, upon potential impact of the key performance factors on the business initiative. The method further includes providing the modeled performance factors in a report to a user, where the report identifies the modeled performance factors, and the potential impact of the at least one modeled performance factor.

    Abstract translation: 一种方法，包括针对一组历史和/或正在进行的商业举措，通过计算机从存储在存储器中的负面和正面性能因素的结构化分类法确定关键的负面和正面性能因素; 在至少一级的分级分类法中建模至少一个正在进行的业务计划的性能因素或新业务计划。 至关重要的负面和积极的绩效因素至少部分地基于在业务计划中发生关键负面绩效因素的可能性，并且至少部分地基于关键绩效因素对业务主动性的潜在影响 。 该方法还包括向用户提供建模的性能因素，其中报告识别建模的性能因素，以及至少一个建模的性能因素的潜在影响。

    SEQUENTIAL DECISION OPTIMIZATION FOR DYNAMIC PROCESSES
    39.
    发明申请
    SEQUENTIAL DECISION OPTIMIZATION FOR DYNAMIC PROCESSES 有权

    公开(公告)号：US20250123606A1

    公开(公告)日：2025-04-17

    申请号：US18486444

    申请日：2023-10-13

    Applicant: International Business Machines Corporation

    Inventor： Dharmashankar Subramanian ,  Pavankumar Murali ,  Nianjun Zhou

    IPC: G05B13/04 ,  G05B13/02

    Abstract: Techniques are provided for dynamic prediction-based regression optimization. In one embodiment, the techniques involve determining, via a process model, a variable state of the process model, wherein the variable state includes a first input state variable, a first output state variable, and a first control parameter, generating, via a short-term prediction module, a first prediction of a first update of the variable state, generating, via a terminal value prediction module, a second prediction of a second update to the variable state, generating, via a control optimization module, a second control parameter based on the first prediction and the second prediction, and controlling, via a processor, a production process of the process model based on the second control parameter.

    MODEL SEARCH AND OPTIMIZATION
    40.
    发明申请
    MODEL SEARCH AND OPTIMIZATION 有权

    公开(公告)号：US20250068902A1

    公开(公告)日：2025-02-27

    申请号：US18453401

    申请日：2023-08-22

    Applicant: International Business Machines Corporation

    Inventor： Long VU ,  Dharmashankar Subramanian ,  Radu Marinescu

    IPC: G06N3/08

    Abstract: Methods and systems for tuning a model include generating pipelines. The pipelines have elements that include at least an agent, a foundation model, and a tuning type. Hyperparameters of elements of the pipelines are set in accordance with an input task. Elements of the pipelines are tuned in accordance with the input task. The input task is performed using a highest-performance pipeline.

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