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公开(公告)号：US11645564B2
公开(公告)日：2023-05-09
申请号：US17404356
申请日：2021-08-17
申请人： INTUIT INC.
发明人： Grace Wu , Shashank Shashikant Rao , Susrutha Gongalla , Ngoc Nhung Ho , Carly Wood , Vaibhav Sharma
摘要： 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.
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公开(公告)号：US10977663B1
公开(公告)日：2021-04-13
申请号：US15905630
申请日：2018-02-26
申请人： Intuit Inc.
发明人： Linxia Liao , Ngoc Nhung Ho , Bei Huang , Meng Chen
摘要： 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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公开(公告)号：US11574315B2
公开(公告)日：2023-02-07
申请号：US17125131
申请日：2020-12-17
申请人： Intuit Inc.
发明人： Linxia Liao , Ngoc Nhung Ho , Bei Huang , Meng Chen
摘要： 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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公开(公告)号：US11429881B1
公开(公告)日：2022-08-30
申请号：US16510195
申请日：2019-07-12
申请人： INTUIT INC.
摘要： 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.
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公开(公告)号：US11693888B1
公开(公告)日：2023-07-04
申请号：US16508224
申请日：2019-07-10
申请人： INTUIT INC.
发明人： Grace Wu , Shashank Shashikant Rao , Susrutha Gongalla , Ngoc Nhung Ho , Carly Wood , Brooke Henderer , Vaibhav Sharma , Prasannavenkatesh Chandrasekar
CPC分类号： G06F16/287 , G06F16/29 , G06N5/04 , G06N5/046 , G06Q40/12 , G06Q40/123 , G06Q50/14 , G06N20/00 , H04W88/02
摘要： Certain aspects of the present disclosure provide techniques for intelligent grouping of travel data for review through a user interface. In one embodiment, a method for providing grouped travel data to a user interface of an application, comprises: receiving a plurality of trip records from an application running on a remote device; providing a first subset of the plurality of trip records to a prediction model; providing a second subset of the plurality of trip records to a model training module; receiving labels for each trip record of the first subset of the plurality of trip records from the prediction model; grouping the first subset of the plurality of trip records based on the received labels; and transmitting the grouped first subset of the plurality of trip records to the application running on the remote device.
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公开(公告)号：US11526811B1
公开(公告)日：2022-12-13
申请号：US16510225
申请日：2019-07-12
申请人： INTUIT INC.
IPC分类号： G06N20/00 , G06N20/20 , H04W4/029 , G06V30/262 , G06F40/289
摘要： 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.
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公开(公告)号：US20220067560A1
公开(公告)日：2022-03-03
申请号：US17404356
申请日：2021-08-17
申请人： INTUIT, INC.
发明人： Grace Wu , Shashank Shashikant Rao , Susrutha Gongalla , Ngoc Nhung Ho , Carly Wood , Vaibhav Sharma
摘要： 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.
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公开(公告)号：US11120349B1
公开(公告)日：2021-09-14
申请号：US15913812
申请日：2018-03-06
申请人： INTUIT INC.
发明人： Grace Wu , Shashank Shashikant Rao , Susrutha Gongalla , Ngoc Nhung Ho , Carly Wood , Vaibhav Sharma
摘要： 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.
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公开(公告)号：US20210103935A1
公开(公告)日：2021-04-08
申请号：US17125131
申请日：2020-12-17
申请人： Intuit Inc.
发明人： Linxia Liao , Ngoc Nhung Ho , Bei Huang , Meng Chen
摘要： 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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公开(公告)号：US11907864B2
公开(公告)日：2024-02-20
申请号：US18194679
申请日：2023-04-03
申请人： INTUIT INC.
发明人： Grace Wu , Shashank Shashikant Rao , Susrutha Gongalla , Ngoc Nhung Ho , Carly Wood , Vaibhav Sharma
摘要： 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.
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