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    Method and system for smart detection of business hot spots
    1.
    发明授权
    Method and system for smart detection of business hot spots 有权

    公开(公告)号：US11645564B2

    公开(公告)日：2023-05-09

    申请号：US17404356

    申请日：2021-08-17

    Applicant: INTUIT INC.

    Inventor： Grace Wu ,  Shashank Shashikant Rao ,  Susrutha Gongalla ,  Ngoc Nhung Ho ,  Carly Wood ,  Vaibhav Sharma

    IPC: G06Q10/04 ,  G06N5/048

    CPC classification number: G06N5/048 ,  G06Q10/04

    Abstract: Aspects of the present disclosure provide techniques for classifying a trip. Embodiments include receiving, from a plurality of users, a plurality of historical trip records. Each of the plurality of historical trip records may comprise one or more historical trip attributes and historical classification information. Embodiments include training a predictive model, using the plurality of historical trip records, to classify trips based on trip records. Training the predictive model may comprise determining a plurality of hot spots based on the historical trip records, each of the plurality of hot spots comprising a region encompassing one or more locations, and associating, in the predictive model, the plurality of hot spots with historical classification information. Embodiments include receiving, from a user, a new trip record comprising a plurality of trip attributes related to a trip and using the predictive model to predict a classification for the trip based on the trip record.

    Method and system for recommending assistance offerings
    3.
    发明授权
    Method and system for recommending assistance offerings 有权

    公开(公告)号：US11574315B2

    公开(公告)日：2023-02-07

    申请号：US17125131

    申请日：2020-12-17

    Applicant: Intuit Inc.

    Inventor： Linxia Liao ,  Ngoc Nhung Ho ,  Bei Huang ,  Meng Chen

    IPC: G06N20/00 ,  G06Q30/00 ,  G06Q40/00

    Abstract: A method and system identify assistance offerings that are likely to be relevant to users of a data management system. The method and system utilize a multivariate random forest regression machine learning process to train an assistance offerings recommendation model to recommend relevant assistance offerings to users of the data management system. The multivariate random forest regression machine learning process replaces zero values in the training set data with negative numbers to increase the accuracy of the machine learning process.

    Personalized grouping of travel data for review through a user interface
    4.
    发明授权
    Personalized grouping of travel data for review through a user interface 有权

    公开(公告)号：US11429881B1

    公开(公告)日：2022-08-30

    申请号：US16510195

    申请日：2019-07-12

    Applicant: INTUIT INC.

    Inventor： Grace Wu ,  Shashank Shashikant Rao ,  Susrutha Gongalla ,  Ngoc Nhung Ho

    IPC: G06N20/00 ,  G06F16/28 ,  G06N5/04 ,  G06F9/451

    Abstract: Certain aspects of the present disclosure provide techniques for providing personalized groups of travel data for review through a user interface. Embodiments include receiving trip records associated with a user from an application running on a remote device, providing the trip records to a prediction model, and receiving a plurality of groups from the prediction model, each group of the plurality of groups comprising a subset of the trip records. Embodiments include providing each group of the plurality of groups to a personalization model, the personalization model having been trained based on user feedback to determine personalization scores for each group of the plurality of groups. Embodiments include receiving a personalization score for each group of the plurality of groups from the personalization model and transmitting one or more groups selected based on the personalization scores to the application to be displayed via the user interface.

    Method for predicting trip purposes using unsupervised machine learning techniques
    6.
    发明授权
    Method for predicting trip purposes using unsupervised machine learning techniques 有权

    公开(公告)号：US11526811B1

    公开(公告)日：2022-12-13

    申请号：US16510225

    申请日：2019-07-12

    Applicant: INTUIT INC.

    Inventor： Grace Wu ,  Shashank Shashikant Rao ,  Susrutha Gongalla ,  Ngoc Nhung Ho

    IPC: G06N20/00 ,  G06N20/20 ,  H04W4/029 ,  G06V30/262 ,  G06F40/289

    Abstract: Certain aspects of the present disclosure provide techniques for recommending trip purposes to users of an application. Embodiments include receiving labeled travel data from the application running on a remote device including a plurality of trip purposes. Embodiments include building a topic model representing words associated with a plurality of topics. Embodiments include training a topic prediction model, using the plurality of topics and one or more features derived from each of the plurality of trip records, to output a topic based on an input trip record. Embodiments include training a purpose prediction model, using the topic model and the plurality of trip purposes, to output a trip purpose based on an input topic. The trip purpose may be recommended to a user via a user interface of the application running on the remote device.

    Account prediction using machine learning
    7.
    发明授权
    Account prediction using machine learning 有权

    公开(公告)号：US12299551B2

    公开(公告)日：2025-05-13

    申请号：US16927655

    申请日：2020-07-13

    Applicant: INTUIT INC.

    Inventor： Shanshan Tuo ,  Neo Yuchen ,  Divya Beeram ,  Valentin Vrzheshch ,  Tomer Tal ,  Ngoc Nhung Ho

    IPC: G06N20/20 ,  G06F16/9535 ,  G06F18/214 ,  G06N3/049

    Abstract: Aspects of the present disclosure provide techniques for training a machine learning model. Embodiments include receiving a historical support record comprising time-stamped actions, a support initiation time, and an account indication. Embodiments include determining features of the historical support record based at least on differences between times of the time-stamped actions and the support initiation time. Embodiments include determining a label for the features based on the account indication. Embodiments include training an ensemble model, using training data comprising the features and the label, to determine an indication of an account in response to input features, wherein the ensemble model comprises a plurality of tree-based models and a ranking model.

    METHOD AND SYSTEM FOR SMART DETECTION OF BUSINESS HOT SPOTS
    8.
    发明申请
    METHOD AND SYSTEM FOR SMART DETECTION OF BUSINESS HOT SPOTS 有权

    公开(公告)号：US20220067560A1

    公开(公告)日：2022-03-03

    申请号：US17404356

    申请日：2021-08-17

    Applicant: INTUIT, INC.

    Inventor： Grace Wu ,  Shashank Shashikant Rao ,  Susrutha Gongalla ,  Ngoc Nhung Ho ,  Carly Wood ,  Vaibhav Sharma

    IPC: G06N5/04 ,  G06Q10/04

    Abstract: Aspects of the present disclosure provide techniques for classifying a trip. Embodiments include receiving, from a plurality of users, a plurality of historical trip records. Each of the plurality of historical trip records may comprise one or more historical trip attributes and historical classification information. Embodiments include training a predictive model, using the plurality of historical trip records, to classify trips based on trip records. Training the predictive model may comprise determining a plurality of hot spots based on the historical trip records, each of the plurality of hot spots comprising a region encompassing one or more locations, and associating, in the predictive model, the plurality of hot spots with historical classification information. Embodiments include receiving, from a user, a new trip record comprising a plurality of trip attributes related to a trip and using the predictive model to predict a classification for the trip based on the trip record.

    Method and system for smart detection of business hot spots
    9.
    发明授权
    Method and system for smart detection of business hot spots 有权

    公开(公告)号：US11120349B1

    公开(公告)日：2021-09-14

    申请号：US15913812

    申请日：2018-03-06

    Applicant: INTUIT INC.

    Inventor： Grace Wu ,  Shashank Shashikant Rao ,  Susrutha Gongalla ,  Ngoc Nhung Ho ,  Carly Wood ,  Vaibhav Sharma

    IPC: G06N5/04 ,  G06Q10/04

    Abstract: Aspects of the present disclosure provide techniques for classifying a trip. Embodiments include receiving, from a plurality of users, a plurality of historical trip records. Each of the plurality of historical trip records may comprise one or more historical trip attributes and historical classification information. Embodiments include training a predictive model, using the plurality of historical trip records, to classify trips based on trip records. Training the predictive model may comprise determining a plurality of hot spots based on the historical trip records, each of the plurality of hot spots comprising a region encompassing one or more locations, and associating, in the predictive model, the plurality of hot spots with historical classification information. Embodiments include receiving, from a user, a new trip record comprising a plurality of trip attributes related to a trip and using the predictive model to predict a classification for the trip based on the trip record.

    METHOD AND SYSTEM FOR RECOMMENDING ASSISTANCE OFFERINGS
    10.
    发明申请
    METHOD AND SYSTEM FOR RECOMMENDING ASSISTANCE OFFERINGS 有权

    公开(公告)号：US20210103935A1

    公开(公告)日：2021-04-08

    申请号：US17125131

    申请日：2020-12-17

    Applicant: Intuit Inc.

    Inventor： Linxia Liao ,  Ngoc Nhung Ho ,  Bei Huang ,  Meng Chen

    IPC: G06Q30/00 ,  G06N20/00

    Abstract: A method and system identify assistance offerings that are likely to be relevant to users of a data management system. The method and system utilize a multivariate random forest regression machine learning process to train an assistance offerings recommendation model to recommend relevant assistance offerings to users of the data management system. The multivariate random forest regression machine learning process replaces zero values in the training set data with negative numbers to increase the accuracy of the machine learning process.

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