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    Modeling time to open of electronic communications
    12.
    发明授权
    Modeling time to open of electronic communications 有权

    公开(公告)号：US11295233B2

    公开(公告)日：2022-04-05

    申请号：US15808171

    申请日：2017-11-09

    Applicant: Adobe Inc.

    Inventor： Moumita Sinha ,  Vishwa Vinay ,  Harvineet Singh ,  Frederic Mary

    IPC: G06N20/00 ,  H04L51/234 ,  H04L51/18

    Abstract: The present disclosure relates applying a survival analysis to model when a particular recipient will view an electronic message. For example, one or more embodiments train a survivor function to model the time that will elapse, on a continuous scale, before a recipient will open an electronic message. For example, one or more embodiments involve accessing analytics training data and extracting a first set of features affecting the time that elapsed before past recipients opened an electronic message and a second set of features affecting whether the recipients opened the electronic message at all. The system then generates a mixture model modified survivor function and determines the effect of each feature set on its corresponding outcome to learn parameters for the mixture model modified survivor function.

    End of period metric projection with intra-period alerts
    13.
    发明授权
    End of period metric projection with intra-period alerts 有权

    公开(公告)号：US11205111B2

    公开(公告)日：2021-12-21

    申请号：US15609254

    申请日：2017-05-31

    Applicant: Adobe Inc.

    Inventor： Shiv Kumar Saini ,  Prakhar Gupta ,  Harvineet Singh ,  Gaurush Hiranandani

    IPC: G06N3/04 ,  G06N7/00 ,  G06F8/77 ,  G06N3/08 ,  G06F8/70

    Abstract: Techniques of forecasting web metrics involve generating, prior to the end of a period of time, a probability of a metric taking on an anomalous value, e.g., a value indicative of an anomaly with respect to web traffic, at the end of the period based on previous values of the metric. Such a probability is based on a distribution of predicted values of the metric at some previous period of time. For example, a web server may use actual values of the number of bounces collected at hourly intervals in the middle of a day to predict a number of bounces at the end of the current day. Further, the web server may also compute a confidence interval to determine whether a predicted end-of-day number of bounces may be considered anomalous. The width of the confidence interval indicates the probability that a predicted end-of-day number of bounces has an anomalous value.

    PERSONALIZED E-LEARNING USING A DEEP-LEARNING-BASED KNOWLEDGE TRACING AND HINT-TAKING PROPENSITY MODEL
    15.
    发明申请
    PERSONALIZED E-LEARNING USING A DEEP-LEARNING-BASED KNOWLEDGE TRACING AND HINT-TAKING PROPENSITY MODEL 审中-公开

    公开(公告)号：US20190333400A1

    公开(公告)日：2019-10-31

    申请号：US15964869

    申请日：2018-04-27

    Applicant: ADOBE INC.

    Inventor： Shiv Kumar Saini ,  Ritwick Chaudhry ,  Pradeep Dogga ,  Harvineet Singh

    IPC: G09B7/00 ,  G09B5/06 ,  G06N99/00 ,  G06N7/00

    Abstract: Techniques are described for jointly modeling knowledge tracing and hint-taking propensity. During a read phase, a co-learning model accepts as inputs an identification of a question and the current knowledge state for a learner, and the model predicts probabilities that the learner will answer the question correctly and that the learner will use a learning aid (e.g., accept a hint). The predictions are used to personalize an e-learning plan, for example, to provide a personalized assessment. By using these predictions to personalize a learner's experience, for example, by offering hints at optimal times, the co-learning system increases efficiencies in learning and improves learning outcomes. Once a learner has interacted with a question, the interaction is encoded and provided to the co-learning model to update the learner's knowledge state during an update phase.

    Personalized e-learning using a deep-learning-based knowledge tracing and hint-taking propensity model
    18.
    发明授权
    Personalized e-learning using a deep-learning-based knowledge tracing and hint-taking propensity model 有权

    公开(公告)号：US10943497B2

    公开(公告)日：2021-03-09

    申请号：US15964869

    申请日：2018-04-27

    Applicant: ADOBE INC.

    Inventor： Shiv Kumar Saini ,  Ritwick Chaudhry ,  Pradeep Dogga ,  Harvineet Singh

    IPC: G09B7/00 ,  G06N7/00 ,  G09B5/06 ,  G06N20/00

    Abstract: Techniques are described for jointly modeling knowledge tracing and hint-taking propensity. During a read phase, a co-learning model accepts as inputs an identification of a question and the current knowledge state for a learner, and the model predicts probabilities that the learner will answer the question correctly and that the learner will use a learning aid (e.g., accept a hint). The predictions are used to personalize an e-learning plan, for example, to provide a personalized assessment. By using these predictions to personalize a learner's experience, for example, by offering hints at optimal times, the co-learning system increases efficiencies in learning and improves learning outcomes. Once a learner has interacted with a question, the interaction is encoded and provided to the co-learning model to update the learner's knowledge state during an update phase.

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