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    Systems and methods for user interface adaptation for per-user metrics
    21.
    发明授权
    Systems and methods for user interface adaptation for per-user metrics 有权

    公开(公告)号：US11513821B2

    公开(公告)日：2022-11-29

    申请号：US17121943

    申请日：2020-12-15

    Applicant: Express Scripts Strategic Development, Inc.

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

    IPC: G06F7/02 ,  G06F16/00 ,  G06F9/451 ,  G16H40/67 ,  G06F17/11 ,  G06Q10/08 ,  G16H70/40 ,  G06N20/00

    Abstract: A computer system for dynamic adaptation of a user interface according to data store mining includes a data store configured to index event data of a plurality of events. A data analyst device is configured to render the user interface to a data analyst and transmit a message that identifies a selected identifier of the plurality of identifiers. A data processing circuit is configured to train a machine learning model based on event data stored by the data store for a first set of identifiers from within a predetermined epoch. An interface circuit determines an interface metric for the selected identifier based on the determined output of the selected identifier and transmits the interface metric to the data analyst device. The data analyst device is configured to, in response to the interface metric from the interface circuit, selectively perform a modification or removal of a second user interface element.

    Computer-implemented automated authorization system using natural language processing
    22.
    发明授权
    Computer-implemented automated authorization system using natural language processing 有权

    公开(公告)号：US11301630B1

    公开(公告)日：2022-04-12

    申请号：US16575821

    申请日：2019-09-19

    Applicant: Express Scripts Strategic Development, Inc.

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

    IPC: G06F40/284 ,  G06N5/00 ,  G06F40/253

    Abstract: A method includes maintaining a question repository in which each question corresponds to a set of decision trees. A distance matrix encodes a distance between each pair of questions. In response to a request for a new question, the method converts the new question into a set of tokens. For each question of the existing questions, the method determines a minimum distance between each token of the new question and the tokens of the question and sums the minimum distances to calculate a distance between the question and the new question. The method includes performing cluster analysis on the distance matrix. Performing cluster analysis includes normalizing the distance matrix and applying a hierarchical clustering process to the normalized distance matrix. Based on the cluster analysis, the method transmits an alternative question proposal or adds the new question to the question repository.

    SYSTEMS AND METHODS FOR USER INTERFACE ADAPTATION FOR PER-USER METRICS
    23.
    发明申请
    SYSTEMS AND METHODS FOR USER INTERFACE ADAPTATION FOR PER-USER METRICS 有权

    公开(公告)号：US20210255880A1

    公开(公告)日：2021-08-19

    申请号：US17307502

    申请日：2021-05-04

    Applicant: Express Scripts Strategic Development, Inc.

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

    IPC: G06F9/451 ,  G16H40/67 ,  G06F17/11 ,  G06Q10/08 ,  G16H70/40 ,  G06N20/00

    Abstract: A computer system for transforming a user interface according to data store mining includes a data store configured to store a parameter related to a user and index event data of a set of events. A data processing circuit is configured to identify a first set of identifiers and train a machine learning model based on event data by the data store. An interface circuit is configured to receive an indication of a selected identifier of the plurality of identifiers, determine a first intake metric of the selected identifier using the machine learning model, and a second intake metric of the selected identifier and the parameter using the machine learning model. The interface circuit is configured to transform the user interface according to the first intake metric and the second intake metric.

    Machine model generation systems and methods
    24.
    发明授权
    Machine model generation systems and methods 有权

    公开(公告)号：US11087880B1

    公开(公告)日：2021-08-10

    申请号：US15655647

    申请日：2017-07-20

    Applicant: Express Scripts Strategic Development, Inc.

    Inventor： Pritesh J. Shah ,  Christopher Markson ,  Logan R. Meltabarger

    IPC: G16H50/20 ,  G06Q50/22 ,  G16H10/60 ,  G06N20/00

    Abstract: A method includes defining model attributes of an organizational machine model that organizes feedback data from one or more sources of the feedback data into plural different topic groups based on similarities in concepts expressed in the feedback data. The model attributes represent criteria for establishment of the organizational machine model and include a topic model number that defines how many of the different topic groups are to be created by the organizational machine model and used to organize the feedback data into, a hyperparameter optimization alpha value that defines how likely a feedback datum in the feedback data is to be included in a single topic group of the different topic groupings or multiple topic groups of the different topic groupings, and a hyperparameter optimization beta value that defines how broadly each of the different topic groups are defined relative to the feedback data.

    Automated intervention system based on channel-agnostic intervention model
    27.
    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
    29.
    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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