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    QUANTIFYING AND IMPROVING THE PERFORMANCE OF COMPUTATION-BASED CLASSIFIERS
    2.
    发明申请
    QUANTIFYING AND IMPROVING THE PERFORMANCE OF COMPUTATION-BASED CLASSIFIERS 有权

    公开(公告)号：US20220300557A1

    公开(公告)日：2022-09-22

    申请号：US17203300

    申请日：2021-03-16

    Applicant: ADOBE INC.

    Inventor： Debraj Debashish Basu ,  Ganesh Satish Mallya ,  Shankar Venkitachalam ,  Deepak Pai

    IPC: G06F16/906 ,  G06F16/9035 ,  G06F16/901

    Abstract: Enhanced methods for improving the performance of classifiers are described. A ground-truth labeled dataset is accessed. A classifier predicts a predicted label for datapoints of the dataset. A confusion matrix for the dataset and classifier is generated. A credibility interval is determined for a performance metric for each label. A first labels with a sufficiently large credibility interval is identified. A second label is identified, where the classifier is likely to confuse, in its predictions, the first label with the second label. The identification of the second label is based on instances of incorrect label predictions of the classifier for the first and/or the second labels. The classifier is updated based on a new third label that includes an aggregation of the first label and the second label. The updated classifier model predicts the third label for any datapoint that the classifier previously predicted the first or second labels.

    MACHINE LEARNING MODELS APPLIED TO INTERACTION DATA FOR FACILITATING MODIFICATIONS TO ONLINE ENVIRONMENTS
    3.
    发明申请
    MACHINE LEARNING MODELS APPLIED TO INTERACTION DATA FOR FACILITATING MODIFICATIONS TO ONLINE ENVIRONMENTS 有权

    公开(公告)号：US20220214957A1

    公开(公告)日：2022-07-07

    申请号：US17703188

    申请日：2022-03-24

    Applicant: Adobe Inc.

    Inventor： Meghanath M Y ,  Shankar Venkitachalam ,  Deepak Pai

    IPC: G06F11/34 ,  G06N20/00 ,  G06F9/451

    Abstract: In some embodiments, a computing system identifies a current engagement stage of a user with an online platform by applying a stage prediction model based on interaction data associated with the user. The interaction data describe actions performed by the user with respect to the online platform and context data associated with each of the actions. The computing system further identifies one or more critical events for promoting the user to transition from one engagement stage to a higher engagement stage based on the interaction data associated with the user. The computing system can make the identified current engagement stage of the user or the identified critical event to be accessible by the online platform so that user interfaces presented on the online platform can be modified to improve a likelihood of the user to transit from the current stage to a higher engagement stage.

    Machine learning models applied to interaction data for facilitating modifications to online environments
    5.
    发明授权
    Machine learning models applied to interaction data for facilitating modifications to online environments 有权

    公开(公告)号：US11775412B2

    公开(公告)日：2023-10-03

    申请号：US17703188

    申请日：2022-03-24

    Applicant: Adobe Inc.

    Inventor： Meghanath M Y ,  Shankar Venkitachalam ,  Deepak Pai

    IPC: G06F11/34 ,  G06N20/00 ,  G06F9/451

    CPC classification number: G06F11/3438 ,  G06F9/451 ,  G06N20/00

    Abstract: In some embodiments, a computing system identifies a current engagement stage of a user with an online platform by applying a stage prediction model based on interaction data associated with the user. The interaction data describe actions performed by the user with respect to the online platform and context data associated with each of the actions. The computing system further identifies one or more critical events for promoting the user to transition from one engagement stage to a higher engagement stage based on the interaction data associated with the user. The computing system can make the identified current engagement stage of the user or the identified critical event to be accessible by the online platform so that user interfaces presented on the online platform can be modified to improve a likelihood of the user to transit from the current stage to a higher engagement stage.

    Anomaly detection and reporting for machine learning models
    9.
    发明授权
    Anomaly detection and reporting for machine learning models 有权

    公开(公告)号：US11449712B2

    公开(公告)日：2022-09-20

    申请号：US16220333

    申请日：2018-12-14

    Applicant: ADOBE INC.

    Inventor： Deepak Pai ,  Vijay Srivastava ,  Joshua Sweetkind-Singer ,  Shankar Venkitachalam

    IPC: G06K9/62 ,  G06F16/904 ,  G06N20/00 ,  G06N5/00

    Abstract: In various embodiments of the present disclosure, output data generated by a deployed machine learning model may be received. An input data anomaly may be detected based at least in part on analyzing input data of the deployed machine learning model. An output data anomaly may further be detected based at least in part on analyzing the output data of the deployed machine learning model. A determination may be made that the input data anomaly contributed to the output data anomaly based at least in part on comparing the input data anomaly to the output data anomaly. A report may be generated that is indicative of the input data anomaly and the output data anomaly, and the report may be transmitted to a client device.

    Generating weighted contextual themes to guide unsupervised keyphrase relevance models
    10.
    发明授权
    Generating weighted contextual themes to guide unsupervised keyphrase relevance models 有权

    公开(公告)号：US12190621B2

    公开(公告)日：2025-01-07

    申请号：US17653414

    申请日：2022-03-03

    Applicant: Adobe Inc.

    Inventor： Debraj Debashish Basu ,  Shankar Venkitachalam ,  Vinh Khuc ,  Deepak Pai

    IPC: G06F17/00 ,  G06F40/295 ,  G06N20/00 ,  G06V30/19 ,  G06V30/416

    Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media that utilize intelligent contextual bias weights for informing keyphrase relevance models to extract keyphrases. For example, the disclosed systems generate a graph from a digital document by mapping words from the digital document to nodes of the graph. In addition, the disclosed systems determine named entity bias weights for the nodes of the graph utilizing frequencies with which the words corresponding to the nodes appear within named entities identified from the digital document. Moreover, the disclosed systems generate a keyphrase summary for the digital document utilizing the graph and a machine learning model biased according to the named entity bias weights for the nodes of the graph.

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