INTELLIGENT PARITY SERVICE WITH DATABASE QUERY OPTIMIZATION

    公开(公告)号:US20230244661A1

    公开(公告)日:2023-08-03

    申请号:US17589869

    申请日:2022-01-31

    申请人: Intuit Inc.

    IPC分类号: G06F16/2453 G06F16/2455

    CPC分类号: G06F16/24537 G06F16/24554

    摘要: A method for performing a parity check of a table by a software application may include obtaining, from a data lake, data lake records stored in the table during a time interval, obtaining partitioning information used to partition the table in a database during the time interval, extracting, from the data lake records and for the partitioning information, partition identifiers stored in the table during the time interval, generating a partition-specific database query including a partition identifier, executing the partition-specific database query to obtain database records stored in the table in a partition of the database during the time interval, extracting a subset of the data lake records that include the partition identifier, and performing a parity comparison on the subset of the data lake records and the database records to generate a parity result.

    DATABASE AUTO-DOCUMENTATION SYSTEMS AND METHODS

    公开(公告)号:US20230244658A1

    公开(公告)日:2023-08-03

    申请号:US17649459

    申请日:2022-01-31

    申请人: INTUIT INC.

    摘要: Systems and methods are described for automatically documenting queries and dynamically populating interactive graphical user interfaces with query recommendations. A computing system receives an initial query from an interactive graphical user interface and asynchronously parses the query for strings matching predetermined phrases. In response to determining that the initial query recites strings matching the predetermined phrases, the system extracts metadata and identifies a table name in the initial query, and modifies a table data in key-value data structure corresponding to the table. Subsequent queries related to the initial query cause the system to asynchronously populate the graphical user interface with query recommendations related to the initial query.

    METHODS AND SYSTEMS FOR TRAINING AND USING PREDICTIVE RISK MODELS IN SOFTWARE APPLICATIONS

    公开(公告)号:US20230230126A1

    公开(公告)日:2023-07-20

    申请号:US17579341

    申请日:2022-01-19

    申请人: INTUIT INC.

    IPC分类号: G06Q30/02 G06Q40/02 G06N20/20

    摘要: Certain aspects of the present disclosure provide techniques for training predictive risk models based on user transaction history. An example method generally includes extracting, from a transaction history data set for a plurality of users of a software application, a plurality of features for each user of the plurality of users having records in the transaction history data set. A training data set is generated based on the extracted plurality of features for each user of the plurality of users. A plurality of predictive risk models is trained to generate a risk propensity score indicating a likelihood that a specified event will occur based on the training data set. Generally, monotonicity of one or more constraints is implemented in the model.

    MACHINE LEARNING FOR IMPROVING MINED DATA QUALITY USING INTEGRATED DATA SOURCES

    公开(公告)号:US20230222524A1

    公开(公告)日:2023-07-13

    申请号:US18180092

    申请日:2023-03-07

    申请人: INTUIT INC.

    IPC分类号: G06Q30/0601 G06Q30/0201

    CPC分类号: G06Q30/0631 G06Q30/0201

    摘要: A processor may receive user interaction data of a user for a plurality of electronically-presented offers. The processor may generate a plurality of labels, the generating comprising generating a label for each respective offer according to a comparison of the quality of the user interactions of the respective offer to the frequency of the user interactions of the respective offer. Each label may be a positive label or a negative label. The processor may determine whether the generating produced both positive and negative labels. The processor may select one of a plurality of available ML models, wherein a two-class ML model is chosen in response to determining that the generating produced both positive and negative labels and a one-class ML model is chosen in response to determining that the generating did not produce both positive and negative labels. The selected ML model may be trained and/or may be used to process user profile data and provide recommendations.

    METHODS AND SYSTEMS FOR GENERATING PROBLEM DESCRIPTION

    公开(公告)号:US20230222292A1

    公开(公告)日:2023-07-13

    申请号:US18180089

    申请日:2023-03-07

    申请人: INTUIT INC.

    摘要: A computing system identifies an incoming voice call from a user device to an agent device associated with the computing system. The computing system generates a transcription of the incoming voice call using one or more natural language processing techniques. The computing system extracts a problem description from the transcription. The problem description indicates a topic for the incoming voice call. A first machine learning model estimates a situation vector from the problem description. A second machine learning model identifies a pre-existing situation vector that closely matches the estimated situation vector. The computing system retrieves a situation description that corresponds to the identified pre-existing situation vector.

    INSTANT CONFERENCING SYSTEM
    89.
    发明公开

    公开(公告)号:US20230179643A1

    公开(公告)日:2023-06-08

    申请号:US18104204

    申请日:2023-01-31

    申请人: Intuit Inc.

    摘要: A method including receiving, at a platform and from a first user using a first user device, selection of a uniform resource indicator (URI) unique to a second user using a second user device. The method also includes generating, automatically by the platform in response to receiving the URI, a conference session unique to the first user and the second user. The method also includes transmitting, automatically by the platform, a message to the second user, the message indicating that the conference session is initiated. The method also includes receiving, by the platform, an indication from the second user device that the second user joins the conference session. The method also includes joining, automatically by the platform, the first user device and the second user device in the conference session.