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    NEURAL NETWORK BASED PREDICTION OF EVENTS ASSOCIATED WITH USERS
    1.
    发明申请
    NEURAL NETWORK BASED PREDICTION OF EVENTS ASSOCIATED WITH USERS 有权

    公开(公告)号：US20220366237A1

    公开(公告)日：2022-11-17

    申请号：US17322740

    申请日：2021-05-17

    Applicant: Humana Inc.

    Inventor： Yunliang Cai ,  Peyman Yousefian

    IPC: G06N3/08 ,  G06N3/04

    Abstract: A system trains a neural network for predicting time for communicating with users. The system trains the neural network using user data for users that includes a communication time series and an event time series. The system trains the neural network by masking a portion of the time series data and provides the masked time series data as input to the neural network. The system executes the neural network to predict values of the masked portion of the time series data. The system determines a loss value based on the accuracy of the prediction of the masked portion of the time series data and adjusts parameters of the neural network to minimize the loss value. The system uses the trained neural network to predict timing for communicating with a particular user.

    Ensemble Time Series Model for Forecasting
    4.
    发明公开
    Ensemble Time Series Model for Forecasting 审中-公开

    公开(公告)号：US20230351155A1

    公开(公告)日：2023-11-02

    申请号：US17731183

    申请日：2022-04-27

    Applicant: Humana Inc.

    Inventor： Nibhrat Lohia ,  Peyman Yousefian ,  Sayantan Mitra ,  Rajiv Gumpina

    IPC: G06N3/04 ,  G06N3/08

    CPC classification number: G06N3/0454 ,  G06N3/0445 ,  G06N3/084

    Abstract: An ensemble time series prediction system that makes predictions based on observed data. The disclosed ensemble time series prediction system may leverage different types of datasets and information from different resources for making predictions. The disclosed ensemble time series prediction system may extract time dependent features from autoregressive time dependent data, embedding features from sparse datasets, continuous features from continuous dataset, and time lagged features from data that include time-lag information. The disclosed ensemble time series prediction system may then consolidate the features extracted from the different types of datasets and generate a set of consolidated input features for training a neural network, which may include a recurrent neural unit that finds sequential pattern for the sequence of input features and a regression unit that performs regression and predictions. The ensemble time series prediction system may output a set of outputs that include predicted values and associated confidence intervals.

    GENERATING MACHINE LEARNING BASED MODELS FOR TIME SERIES FORECASTING
    6.
    发明申请
    GENERATING MACHINE LEARNING BASED MODELS FOR TIME SERIES FORECASTING 有权

    公开(公告)号：US20230075453A1

    公开(公告)日：2023-03-09

    申请号：US17469598

    申请日：2021-09-08

    Applicant: Humana Inc.

    Inventor： Sayantan Mitra ,  Nibhrat Lohia ,  Peyman Yousefian ,  Harpreet Singh ,  Rajiv Kumar Gumpina

    IPC: G06N5/04 ,  G06N5/02 ,  G06F16/28

    Abstract: A system according determines a machine learning based model for forecasting time series data for a given use case. The system determines a model metric for a specific use case of time series data. The system accesses a pool of machine learning based models including a plurality of machine learning based models machine learning based models based on different machine learning techniques. For each of the plurality of machine learning based models the system performs forecasting using the machine learning based model and determines the value of the model metric for the machine learning based model. The system selects a machine learning based model based on comparison of values of the model metric for machine learning based models. The system uses the selected machine learning based model for forecasting values for the time series data for the application.

    MACHINE LEARNING BASED MODEL FOR DETERMINING EFFECTIVE COMMUNICATION MECHANISM WITH USERS
    7.
    发明申请
    MACHINE LEARNING BASED MODEL FOR DETERMINING EFFECTIVE COMMUNICATION MECHANISM WITH USERS 有权

    公开(公告)号：US20220366279A1

    公开(公告)日：2022-11-17

    申请号：US17322738

    申请日：2021-05-17

    Applicant: Humana Inc.

    Inventor： Yunliang Cai ,  Peyman Yousefian ,  Sravya Etlapur

    IPC: G06N5/04 ,  G06N20/00 ,  H04L29/08

    Abstract: A system uses a machine learning based model to select a channel for communicating with users. The system generates a feature vector based on a user profile of the user. The user profile data includes time series data describing past communications to users and past user actions. The system executes one or more machine learning based models, each machine learning based model configured to receive a feature vector describing a particular user and predict a likelihood of the particular user performing an expected user action responsive to a communication sent via the communication channel. The system selects a communication channel based on the results of the machine learning based models and sends a communication to the user via the selected communication channel.

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