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公开(公告)号：US11922310B1
公开(公告)日：2024-03-05
申请号：US18194018
申请日：2023-03-31
Applicant: INTUIT INC.
Inventor： Bor-Chau Juang , Eyal Shafran , Pratyush Kumar Panda , Divya Beeram , Linxia Liao , Nicholas Johnson , Christiana Mei Hui Chen
IPC: G06N3/08
CPC classification number: G06N3/08
Abstract: Certain aspects of the present disclosure provide techniques for predicting activity within a software application using a machine learning model. An example method generally includes generating a multidimensional time-series data set from time-series data associated with activity within a software application. The multidimensional time-series data set generally includes the time-series data organized based on a plurality of time granularities. Using a machine learning model and the generated multidimensional time-series data set, activity within the software application is predicted for one or more time granularities of the plurality of time granularities. Computing resources are allocated to execute operations using the software application based on the predicted activity within the software application.
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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
Applicant: Intuit Inc.
Inventor： Linxia Liao , Ngoc Nhung Ho , Bei Huang , Meng Chen
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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Patent Agency Ranking
3.发明申请公开(公告)号：US20210149671A1
公开(公告)日：2021-05-20
申请号：US16688697
申请日：2019-11-19
Applicant: Intuit Inc.
Inventor： Oren Sar Shalom , Meng Chen , Linxia Liao , Yehezkel Shraga Resheff
Abstract: A machine learning method. A source domain data structure and a target domain data structure are combined into a unified data structure. First data in the source domain data structure are latent with respect to second data in the target domain data structure. The unified data structure includes user vectors that combine the first data and the second data. The user vectors are transformed into a transformed data structure by applying a mapping function to the user vectors. The mapping function relates, using at least one parameter, first relationships in the source domain data structure to second relationships in the target domain data structure. The at least one parameter is based on a combination of affinity scores relating items with which the user interacted and did not interact. The transformed data structure is input into a machine learning model, from which is obtained a recommendation relating to the target domain.
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公开(公告)号：US11425250B1
公开(公告)日：2022-08-23
申请号：US17543453
申请日：2021-12-06
Applicant: Intuit Inc.
Inventor： Joseph Brian Cessna , Yaxian Li , Linxia Liao , Kenneth Grant Yocum , Nicholas R. Johnson , Christiana Mei Hui Chen
Abstract: Systems and methods for call handling optimization of a call center are disclosed. A system is configured to: obtain a plurality of agent demand variables; obtain an indication of available agents that are associated with a plurality of skills; generate a multi-skill routing matrix depicting a routing problem from the agent demand variables and the available agents; deconstruct the multi-skill matrix into one or more band routing matrices; and identify, for each band routing matrix, a number of agents from the available agents to be used in solving the band routing matrix. Identifying the number of agents includes recursively simulating a plurality of possible routing solutions, measuring a quality metric for each simulation, and reducing the range of available agents based on the quality metric. The system may indicate the number of agents to be used for each routing sub-problem (which may be at the staff group level).
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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
Applicant: Intuit Inc.
Inventor： Linxia Liao , Ngoc Nhung Ho , Bei Huang , Meng Chen
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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公开(公告)号：US11908023B1
公开(公告)日：2024-02-20
申请号：US16525287
申请日：2019-07-29
Applicant: INTUIT INC.
Inventor： Meng Chen , Lei Pei , Yueyue Gu , Zhicheng Xue , Linxia Liao
IPC: G06Q40/12 , H04L67/306 , G06Q40/02 , G06F17/18
CPC classification number: G06Q40/128 , G06F17/18 , G06Q40/02 , H04L67/306
Abstract: Certain aspects of the present disclosure provide techniques for generating a user interface to prompt users of a software application to perform an action in the software application. The method generally includes generating historical transaction time gap data for transactions in the account. A probability distribution is generated based on the historical time gap data. The probability distribution represents a probability that a transaction related to the account has been performed after an elapsed time from a previous transaction. A probability that an unrecorded transaction exists for an account based on the probability distribution and a time difference between a most recent transaction and a current time. The probability that an unrecorded transaction exists is determined to exceed a threshold probability, and a user interface is generated and displayed to a user of the software application including a prompt for the user to enter new transactions for the account.
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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
Applicant: Intuit Inc.
Inventor： Linxia Liao , Ngoc Nhung Ho , Bei Huang , Meng Chen
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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