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    System to convert natural-language financial questions into database queries
    1.
    发明授权
    System to convert natural-language financial questions into database queries 有权

    公开(公告)号：US11334565B1

    公开(公告)日：2022-05-17

    申请号：US15337324

    申请日：2016-10-28

    Applicant: INTUIT INC.

    Inventor： Lulu Cheng ,  Meng Chen ,  Jing Zhang ,  Wenting Cai ,  Crystal Meng

    IPC: G06F16/2452 ,  G06F16/242 ,  G06F16/2457

    Abstract: Systems of the present disclosure generate database queries for financial information requested in a natural-language form. A natural-language processing (NLP) financial aggregator receives a request for financial information and extracts NLP features of the request, including keywords. The NLP financial aggregator determines a type of the request based on the features and creates a query in a database-query language for the financial information based on the type and on the features. The NLP financial aggregator submits the query to a database where the financial information is stored. The software then receives the financial information from the database and sends the information to the user.

    METHOD FOR PREDICTING BUSINESS INCOME FROM USER TRANSACTION DATA
    2.
    发明申请
    METHOD FOR PREDICTING BUSINESS INCOME FROM USER TRANSACTION DATA 有权

    公开(公告)号：US20210217102A1

    公开(公告)日：2021-07-15

    申请号：US17218855

    申请日：2021-03-31

    Applicant: Intuit Inc.

    Inventor： Meng Chen ,  Lei Pei ,  Zachary Grove Jennings ,  Ngoc Nhung Thi Ho

    IPC: G06Q40/00 ,  G06F16/2458 ,  G06N20/00

    Abstract: A method that predicts business income from user transaction data. A multinomial classifier is trained, using a vector of features from data related to a historical transaction and a label associated with the historical transaction, to generate a probability that the historical transaction belongs to a specific classification with respect to income. Data related to a new transaction is split into a set of unigrams. A new vector of features is generated from the data related to the new transaction. The new vector includes a set of values that correspond and are assigned to the set of unigrams. A classification with respect to income is determined for the new transaction by applying the multinomial classifier to the new vector. The new transaction is labeled with the classification. One or more fields of a form that is maintained by an online service is populated using the classification.

    METHOD AND SYSTEM FOR AUTOMATICALLY CATEGORIZING FINANCIAL TRANSACTION DATA
    3.
    发明申请
    METHOD AND SYSTEM FOR AUTOMATICALLY CATEGORIZING FINANCIAL TRANSACTION DATA 审中-公开

    公开(公告)号：US20180018734A1

    公开(公告)日：2018-01-18

    申请号：US15213096

    申请日：2016-07-18

    Applicant: Intuit Inc.

    Inventor： Ngoc Nhung Ho ,  Meng Chen ,  Lei Pei

    IPC: G06Q40/06

    CPC classification number: G06Q40/06

    Abstract: Financial transaction data representing a current financial transaction is processed and divided into financial transaction data segments of one of more words or symbols. A financial transaction data segment in the current financial transaction is assigned a financial transaction data segment score based on an analysis of historical financial transaction categorizations of historical financial transactions containing the same financial transaction data segment. The calculated financial transaction data segment score is then compared with a defined threshold financial transaction data segment score and, if the calculated financial transaction data segment score is greater than the threshold financial transaction data segment score, the financial transaction containing the financial transaction data segment is categorized, at least temporarily, as being a first financial transaction category financial transaction.

    Method for predicting business income from user transaction data
    5.
    发明授权
    Method for predicting business income from user transaction data 有权

    公开(公告)号：US11562440B2

    公开(公告)日：2023-01-24

    申请号：US17218855

    申请日：2021-03-31

    Applicant: Intuit Inc.

    Inventor： Meng Chen ,  Lei Pei ,  Zachary Grove Jennings ,  Ngoc Nhung Thi Ho

    IPC: G06Q40/00 ,  G06F16/2458 ,  G06N20/00

    Abstract: A method that predicts business income from user transaction data. A multinomial classifier is trained, using a vector of features from data related to a historical transaction and a label associated with the historical transaction, to generate a probability that the historical transaction belongs to a specific classification with respect to income. Data related to a new transaction is split into a set of unigrams. A new vector of features is generated from the data related to the new transaction. The new vector includes a set of values that correspond and are assigned to the set of unigrams. A classification with respect to income is determined for the new transaction by applying the multinomial classifier to the new vector. The new transaction is labeled with the classification. One or more fields of a form that is maintained by an online service is populated using the classification.

    Composite machine-learning system for label prediction and training data collection
    6.
    发明授权
    Composite machine-learning system for label prediction and training data collection 有权

    公开(公告)号：US10984340B2

    公开(公告)日：2021-04-20

    申请号：US15476647

    申请日：2017-03-31

    Applicant: INTUIT INC.

    Inventor： Yu-Chung Hsiao ,  Lei Pei ,  Meng Chen ,  Nhung Ho

    IPC: G06N20/00 ,  G06N5/04 ,  G06Q40/00 ,  G06Q30/04 ,  G06N20/20

    Abstract: The present disclosure provides a composite machine-learning system for a transaction labeling service. A transaction labeling service receives at least one descriptive string describing a transaction associated with a user. The service identifies a preliminary grouping from a generalized scheme. The service extracts a set of N-grams from the descriptive string and converts the N-grams and the preliminary grouping into a set of features. A machine-learning model determines a label from a labeling scheme for the transaction based on the features. User input related to the label includes an accuracy indicator and a reliability indicator. If the reliability indicator satisfies a reliability condition, a set of training data for the machine-learning model is updated based on the descriptive string and the label. The machine-learning model is then trained against the updated set of training data.

    METHOD AND SYSTEM FOR RECOMMENDING ASSISTANCE OFFERINGS
    7.
    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.

    Domain-specific sentiment keyword extraction with weighted labels
    8.
    发明授权
    Domain-specific sentiment keyword extraction with weighted labels 有权

    公开(公告)号：US10373067B1

    公开(公告)日：2019-08-06

    申请号：US14458876

    申请日：2014-08-13

    Applicant: INTUIT INC.

    Inventor： Meng Chen ,  Giovanni Seni

    IPC: G06N5/04 ,  G06N20/00 ,  G06F16/2457

    Abstract: The disclosed embodiments provide a system for facilitating sentiment analysis. During operation, the system obtains a set of training data that includes a first set of content items containing words associated with a domain, a set of sentiment scores for the first set of content items, and a set of outcomes associated with the first set of content items. Next, the system uses the training data to train a statistical model for performing sentiment analysis that is specific to the domain. The system then enables use of the statistical model in generating a set of domain-specific sentiment scores for a second set of content items containing words associated with the domain.

    Composite machine learning system for label prediction and training data collection
    9.
    发明授权
    Composite machine learning system for label prediction and training data collection 有权

    公开(公告)号：US11816544B2

    公开(公告)日：2023-11-14

    申请号：US17232455

    申请日：2021-04-16

    Applicant: INTUIT INC.

    Inventor： Yu-Chung Hsiao ,  Lei Pei ,  Meng Chen ,  Nhung Ho

    IPC: G06N20/00 ,  G06N5/04 ,  G06Q40/12 ,  G06Q30/04 ,  G06N20/20

    CPC classification number: G06N20/00 ,  G06N5/04 ,  G06N20/20 ,  G06Q30/04 ,  G06Q40/123

    Abstract: The present disclosure provides a composite machine learning system for a transaction labeling service. A transaction labeling service receives at least one descriptive string describing a transaction associated with a user. The service identifies a preliminary grouping from a generalized scheme. The service extracts a set of N-grams from the descriptive string and converts the N-grams and the preliminary grouping into a set of features. A machine learning model determines a label from a labeling scheme for the transaction based on the features. User input related to the label includes an accuracy indicator and a reliability indicator. If the reliability indicator satisfies a reliability condition, a set of training data for the machine learning model is updated based on the descriptive string and the label. The machine learning model is then trained against the updated set of training data.

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