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    Systems and methods for machine-automated classification of website interactions
    21.
    发明授权
    Systems and methods for machine-automated classification of website interactions 有权

    公开(公告)号：US11366834B2

    公开(公告)日：2022-06-21

    申请号：US16923633

    申请日：2020-07-08

    Applicant: Express Scripts Strategic Development, Inc.

    Inventor： Christopher R. Markson ,  Adithya Chowdary Boppana ,  Rahul Rajendran ,  Pritesh J. Shah ,  Christopher G. Lehmuth

    IPC: G06F16/00 ,  G06F16/28 ,  G06F16/958

    Abstract: A system includes a processor and memory. The memory stores a model database including models and a classification database including classification scores corresponding to an input. The memory stores instructions for execution by the processor. The instructions include, in response to receiving a first input from a user device of a user, determining, for the first input, classification scores for classifications by applying the models to the first input. Each model determines one of the classification scores. The instructions include storing the classification scores as associated with the first input in the classification database and identifying the first input as within a first classification in response to a first classification score corresponding to the first classification exceeding a first threshold. The instructions include transmitting, for display on an analyst device, the first input based on the first classification to a first analyst queue associated with the first classification.

    SYSTEMS AND METHODS FOR MACHINE-AUTOMATED CLASSIFICATION OF WEBSITE INTERACTIONS
    22.
    发明申请
    SYSTEMS AND METHODS FOR MACHINE-AUTOMATED CLASSIFICATION OF WEBSITE INTERACTIONS 有权

    公开(公告)号：US20220012267A1

    公开(公告)日：2022-01-13

    申请号：US16923633

    申请日：2020-07-08

    Applicant: Express Scripts Strategic Development, Inc.

    Inventor： Christopher R. Markson ,  Adithya Chowdary Boppana ,  Rahul Rajendran ,  Pritesh J. Shah ,  Christopher G. Lehmuth

    IPC: G06F16/28 ,  G06F16/958

    Abstract: A system includes a processor and memory. The memory stores a model database including models and a classification database including classification scores corresponding to an input. The memory stores instructions for execution by the processor. The instructions include, in response to receiving a first input from a user device of a user, determining, for the first input, classification scores for classifications by applying the models to the first input. Each model determines one of the classification scores. The instructions include storing the classification scores as associated with the first input in the classification database and identifying the first input as within a first classification in response to a first classification score corresponding to the first classification exceeding a first threshold. The instructions include transmitting, for display on an analyst device, the first input based on the first classification to a first analyst queue associated with the first classification.

    COMPUTER IMAGING PRE-PROCESSING FOR AUTOMATED MEDICATION DISPENSING ANALYSIS
    23.
    发明申请
    COMPUTER IMAGING PRE-PROCESSING FOR AUTOMATED MEDICATION DISPENSING ANALYSIS 有权

    公开(公告)号：US20210004958A1

    公开(公告)日：2021-01-07

    申请号：US16998358

    申请日：2020-08-20

    Applicant: Express Scripts Strategic Development, Inc.

    Inventor： Christopher R. Markson ,  Pritesh J. Shah ,  Christopher G. Lehmuth

    IPC: G06T7/00 ,  G06T7/70 ,  G06T7/13 ,  G06T11/00 ,  G16H20/13 ,  G06T7/12 ,  G16H30/40

    Abstract: A computer system includes an input configured to receive a first image of medication located in a receptacle, memory, and a processor configured to execute instructions including creating a second image based on the first image, dividing pixels of the second image into first and second subsets, and scanning the second image along a first axis to count, for each point along the first axis, a number of pixels in the first subset along a line perpendicular to the first axis that intersects the first axis at the point. The instructions also include estimating positions of first and second edges of the receptacle along the first axis based on the counts of the pixels, defining an opening of the receptacle based on the estimated positions of the first and second edges, and outputting a processed image that indicates areas of the image that are outside of the defined opening.

    COMPUTER IMAGING PRE-PROCESSING FOR AUTOMATED MEDICATION DISPENSING ANALYSIS
    24.
    发明申请
    COMPUTER IMAGING PRE-PROCESSING FOR AUTOMATED MEDICATION DISPENSING ANALYSIS 审中-公开

    公开(公告)号：US20190156475A1

    公开(公告)日：2019-05-23

    申请号：US16190548

    申请日：2018-11-14

    Applicant: Express Scripts Strategic Development, Inc.

    Inventor： Christopher R. Markson ,  Pritesh J. Shah ,  Christopher G. Lehmuth

    IPC: G06T7/00 ,  G06T7/70 ,  G06T7/13 ,  G06T11/00 ,  G06T7/12 ,  G16H20/13

    Abstract: A method includes capturing a first image of medication held by a receptacle. The method includes creating a second image based on the first image. The method includes determining a first subset of pixels of the second image that are more likely to correspond to the receptacle. The method includes processing the second image along a first axis by, for each point: defining a line perpendicularly intersecting the first axis at the point and counting how many of the pixels located along the line are in the first subset. The method includes determining first and second local maxima of the counts. The method includes estimating positions of first and second edges of the receptacle based on positions of the local maxima. The method includes defining an ellipse based on the first and second edges and excluding areas of the first image outside the defined ellipse from further processing.

    Automated intervention system based on channel-agnostic intervention model
    26.
    发明授权
    Automated intervention system based on channel-agnostic intervention model 有权

    公开(公告)号：US11830629B2

    公开(公告)日：2023-11-28

    申请号：US18094472

    申请日：2023-01-09

    Applicant: Express Scripts Strategic Development, Inc.

    Inventor： Amit K. Bothra ,  Pritesh J. Shah ,  Christopher G. Lehmuth ,  Bradley D. Flynn

    IPC: G16H80/00 ,  G06F17/18 ,  G16H40/20 ,  G16H20/10

    CPC classification number: G16H80/00 ,  G06F17/18 ,  G16H20/10 ,  G16H40/20

    Abstract: A method includes generating an intervention model by determining principal components for features of a training set, associating each feature of the training set with one of the principal components, selecting features of the training set most closely correlated with the principal components, performing a regression analysis on the selected features to determine a subset of the selected features that are most closely correlated with a model target, training a machine learning model with the subset, verifying the trained machine learning model with a verification set, and saving the verified trained machine learning model as the intervention model. The method includes determining an intervention expectation indicating a likelihood that the user will take action in response to an intervention being execute, determining a likelihood of a gap in care for the user, selecting and scheduling an intervention for execution based on the care gap likelihood and the intervention expectation.

    AUTOMATED INTERVENTION SYSTEM BASED ON CHANNEL-AGNOSTIC INTERVENTION MODEL
    28.
    发明公开
    AUTOMATED INTERVENTION SYSTEM BASED ON CHANNEL-AGNOSTIC INTERVENTION MODEL 审中-公开

    公开(公告)号：US20230162872A1

    公开(公告)日：2023-05-25

    申请号：US18094472

    申请日：2023-01-09

    Applicant: Express Scripts Strategic Development, Inc.

    Inventor： Amit K. Bothra ,  Pritesh J. Shah ,  Christopher G. Lehmuth ,  Bradley D. Flynn

    IPC: G16H80/00 ,  G06F17/18 ,  G16H40/20 ,  G16H20/10

    CPC classification number: G16H80/00 ,  G06F17/18 ,  G16H40/20 ,  G16H20/10

    Abstract: A method includes generating an intervention model by determining principal components for features of a training set, associating each feature of the training set with one of the principal components, selecting features of the training set most closely correlated with the principal components, performing a regression analysis on the selected features to determine a subset of the selected features that are most closely correlated with a model target, training a machine learning model with the subset, verifying the trained machine learning model with a verification set, and saving the verified trained machine learning model as the intervention model. The method includes determining an intervention expectation indicating a likelihood that the user will take action in response to an intervention being execute, determining a likelihood of a gap in care for the user, selecting and scheduling an intervention for execution based on the care gap likelihood and the intervention expectation.

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