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13 条记录 (耗时 0.017 秒)

    FAILURE MODE DISCOVERY FOR MACHINE COMPONENTS
    2.
    发明公开
    FAILURE MODE DISCOVERY FOR MACHINE COMPONENTS 审中-公开

    公开(公告)号：US20240054800A1

    公开(公告)日：2024-02-15

    申请号：US18186001

    申请日：2023-03-17

    申请人： DIMAAG-AI, Inc.

    发明人： Rajaram Kudli ,  Satish Padmanabhan ,  Fuk Ho Pius Ng

    IPC分类号： G06V30/19 ,  G06V30/182 ,  G06F16/35

    CPC分类号： G06V30/19107 ,  G06V30/1823 ,  G06F16/35

    摘要： The failure modes of mechanical components may be determined based on text analysis. For example, a word embedding may be determined based on a plurality of text documents that include a plurality of maintenance records characterizing failure of mechanical components. A vector representation for a particular maintenance record may then be determined based on the word embedding. Based on the vector representation, the particular maintenance record may then be identified as belonging to a particular failure mode out of a set of possible failure modes.

    Failure mode discovery for machine components
    4.
    发明授权
    Failure mode discovery for machine components 有权

    公开(公告)号：US11954929B2

    公开(公告)日：2024-04-09

    申请号：US18186001

    申请日：2023-03-17

    申请人： DIMAAG-AI, Inc.

    发明人： Rajaram Kudli ,  Satish Padmanabhan ,  Fuk Ho Pius Ng

    IPC分类号： G06V30/19 ,  G06F16/35 ,  G06V30/182

    CPC分类号： G06V30/19107 ,  G06F16/35 ,  G06V30/1823

    摘要： The failure modes of mechanical components may be determined based on text analysis. For example, a word embedding may be determined based on a plurality of text documents that include a plurality of maintenance records characterizing failure of mechanical components. A vector representation for a particular maintenance record may then be determined based on the word embedding. Based on the vector representation, the particular maintenance record may then be identified as belonging to a particular failure mode out of a set of possible failure modes.

    Detection and visualization of novel data instances for self-healing AI/ML model-based solution deployment
    5.
    发明授权
    Detection and visualization of novel data instances for self-healing AI/ML model-based solution deployment 有权

    公开(公告)号：US11783233B1

    公开(公告)日：2023-10-10

    申请号：US18153010

    申请日：2023-01-11

    申请人： DIMAAG-AI, Inc.

    发明人： Rajaram Kudli ,  Satish Padmanabhan ,  Fuk Ho Pius Ng ,  Nagarjun Pogakula Surya Prakash ,  Ananda Shekappa Sonnada

    IPC分类号： G06N20/00

    CPC分类号： G06N20/00

    摘要： A feature data segment may be determined by applying a feature segmentation model to a test data observation. The feature segmentation model may be pre-trained via a plurality of training data observations and may divide the plurality of training data observations into a plurality of feature data segments. A predicted target value may be determined by applying to a test data observation a prediction model pre-trained via a plurality of training data observations. One or more distance metrics representing a respective distance between the test data observation and the feature data segment along one or more dimensions may be determined. The one or more distance metrics may be represented in a user interface. An updated prediction model and an updated feature segmentation model that both incorporate the test data observation and the training data observations may be determined based on user input.

    Drift detection in static processes
    6.
    发明授权
    Drift detection in static processes 有权

    公开(公告)号：US11740905B1

    公开(公告)日：2023-08-29

    申请号：US17814684

    申请日：2022-07-25

    申请人： DIMAAG-AI, Inc.

    发明人： Rajaram Kudli ,  Satish Padmanabhan ,  Fuk Ho Pius Ng ,  Dushyanth Gokhale

    IPC分类号： G06N5/022 ,  G06F9/30

    CPC分类号： G06F9/30192 ,  G06N5/022

    摘要： In many industrial settings, a process is repeated many times, for instance to transform physical inputs into physical outputs. To detect a situation involving such a process in which errors are likely to occur, information about the process may be collected to determine time-varying feature vectors. Then, a drift value may be determined by comparing feature vectors corresponding with different time periods. When the drift value crosses a designated drift threshold, a predicted outcome value may be determined by applying a prediction model. Sensitivity values may be determined for different features, and elements of the process may then be updated based at least in part on the sensitivity values.

    Apparatus for knowledge based evolutionary learning in AI systems
    7.
    发明授权
    Apparatus for knowledge based evolutionary learning in AI systems 有权

    公开(公告)号：US11676036B2

    公开(公告)日：2023-06-13

    申请号：US16880346

    申请日：2020-05-21

    申请人： DIMAAG-AI, Inc.

    发明人： Nagarjun Pogakula Surya ,  Gomathi Sankar ,  Fuk Ho Pius Ng ,  Satish Padmanabhan

    IPC分类号： G06N3/086 ,  G06N3/045

    CPC分类号： G06N3/086 ,  G06N3/045

    摘要： Systems and methods are disclosed for training a previously trained neural network with incremental dataset. Original train data is provided to a neural network and the neural network is trained based on the plurality of classes in the sets of training data and/or testing data. The connected representation and the weights of the neural network is the model of the neural network. The trained model is to be updated for an incremental train data. The embodiments provide a process by which the trained model is updated for the incremental train data. This process creates a ground truth for the original training data and trains on the combined set of original train data and the incremental train data. The incremental training is tested on a test data to conclude the training and to generate the incremental trained model, minimizing the knowledge learned with the original data. Thus, the results remain consistent with the original model trained by the original dataset except the incremental train data.

    Failure mode discovery for machine components
    8.
    发明授权
    Failure mode discovery for machine components 有权

    公开(公告)号：US11636697B1

    公开(公告)日：2023-04-25

    申请号：US17818373

    申请日：2022-08-09

    申请人： DIMAAG-AI, Inc.

    发明人： Rajaram Kudli ,  Satish Padmanabhan ,  Fuk Ho Pius Ng

    IPC分类号： G06F16/35 ,  G06V30/19 ,  G06V30/182

    摘要： The failure modes of mechanical components may be determined based on text analysis. For example, a word embedding may be determined based on a plurality of text documents that include a plurality of maintenance records characterizing failure of mechanical components. A vector representation for a particular maintenance record may then be determined based on the word embedding. Based on the vector representation, the particular maintenance record may then be identified as belonging to a particular failure mode out of a set of possible failure modes.