MACHINE-LEARNING MODEL GENERATION
    1.
    发明公开

    公开(公告)号:US20240282452A1

    公开(公告)日:2024-08-22

    申请号:US18653547

    申请日:2024-05-02

    CPC classification number: G16H50/20 G06N20/00 G06Q50/22 G16H10/60

    Abstract: A computer-implemented method for generating one or more summarizations of a large volume of feedback data includes obtaining the feedback data. The feedback data is provided from disparate sources. The method includes separating the feedback data into a set of sentences, generating feedback embeddings of the set of sentences by providing the set of sentences to a set of machine models, providing topic input data to the set of machine models, computing sentence similarity of the feedback embeddings, calculating an importance score for each sentence of the set of sentences, ranking the set of sentences according to their respective importance scores, selecting one or more subsets of the set of sentences to generate the one or more summarizations, generating a visual representation of the one or more summarizations, and displaying the visual representation in an interactive user interface.

    Medical processing systems and methods

    公开(公告)号:US12033731B2

    公开(公告)日:2024-07-09

    申请号:US17168745

    申请日:2021-02-05

    CPC classification number: G16H10/60

    Abstract: A content analysis system includes a processor executing instructions from memory. The instructions include, in response to receiving a request signal from a user device, obtaining feedback items, each having a source indicator; identifying unique source indicators; and, for each source indicator, aggregating corresponding ones of the feedback items. A set of filtered feedback items is generated according to either first or second access levels associated with a user of the user device. A subset of filtered feedback items is selected according to a date range specified by the request signal, a set of automated rules is applied, and natural language processing is performed based on frequency of presence of salient terms to identify themes. A control signal is transmitted to a user interface of the user device instructing display of a representation that indicates a change in the frequency of the identified themes over the specified date range.

    Machine model generation systems and methods

    公开(公告)号:US11848101B2

    公开(公告)日:2023-12-19

    申请号:US17363605

    申请日:2021-06-30

    CPC classification number: G16H50/20 G06N20/00 G06Q50/22 G16H10/60

    Abstract: A method includes defining model attributes of a machine model that organizes feedback data into topic groups based on similarities in concepts in the feedback data. The model attributes include a topic model number that defines how many topic groups are to be created, a hyperparameter optimization alpha value, and/or a hyperparameter optimization beta value. The method also includes generating the machine model using the model attributes that are defined and the feedback data, and applying the machine model to the feedback data to divide different portions of the feedback data into the different topic groups based on contents of the feedback data, the topic model number, the hyperparameter optimization alpha value, and/or the hyperparameter optimization beta value.

    MEDICAL PROCESSING SYSTEMS AND METHODS

    公开(公告)号:US20210158919A1

    公开(公告)日:2021-05-27

    申请号:US17168745

    申请日:2021-02-05

    Abstract: A content analysis system includes a processor executing instructions from memory. The instructions include, in response to receiving a request signal from a user device, obtaining feedback items, each having a source indicator; identifying unique source indicators; and, for each source indicator, aggregating corresponding ones of the feedback items. A set of filtered feedback items is generated according to either first or second access levels associated with a user of the user device. A subset of filtered feedback items is selected according to a date range specified by the request signal, a set of automated rules is applied, and natural language processing is performed based on frequency of presence of salient terms to identify themes. A control signal is transmitted to a user interface of the user device instructing display of a representation that indicates a change in the frequency of the identified themes over the specified date range.

    ITERATED TRAINING OF MACHINE MODELS WITH DEDUPLICATION

    公开(公告)号:US20240120103A1

    公开(公告)日:2024-04-11

    申请号:US18543804

    申请日:2023-12-18

    CPC classification number: G16H50/20 G06N20/00 G06Q50/22 G16H10/60

    Abstract: A computer-implemented method includes defining model attributes including a training iteration value that defines a set of training iterations to be used in machine learning to associate portions of feedback data with a set of topic groups based on similarities in concepts conveyed in the feedback data. The method includes removing at least some of the confidential information from the feedback data. The method includes receiving a topic model number selection that indicates a subset of the set of topic groups. The method includes using machine learning to train a machine model based on the model attributes and the topic model number selection. The method includes generating a display showing at least one of a topic cluster graph or a word cloud based on the machine model.

    Systems and methods for user interface adaptation for per-user metrics

    公开(公告)号:US11513821B2

    公开(公告)日:2022-11-29

    申请号:US17121943

    申请日:2020-12-15

    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

    公开(公告)号:US11301630B1

    公开(公告)日:2022-04-12

    申请号:US16575821

    申请日:2019-09-19

    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

    公开(公告)号:US20210255880A1

    公开(公告)日:2021-08-19

    申请号:US17307502

    申请日:2021-05-04

    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.

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