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公开(公告)号：US11816544B2
公开(公告)日：2023-11-14
申请号：US17232455
申请日：2021-04-16
申请人： INTUIT INC.
发明人： Yu-Chung Hsiao , Lei Pei , Meng Chen , Nhung Ho
CPC分类号： G06N20/00 , G06N5/04 , G06N20/20 , G06Q30/04 , G06Q40/123
摘要： 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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公开(公告)号：US20220245731A1
公开(公告)日：2022-08-04
申请号：US17162365
申请日：2021-01-29
申请人： Intuit Inc.
摘要： A method may include executing a baseline classifier on unreviewed transaction features of an unreviewed transaction record to obtain a baseline account identifier, and executing a comparison model on (i) an unreviewed transaction vector of the unreviewed transaction record and (ii) reviewed transaction vectors to obtain comparison scores. The reviewed transaction vectors may correspond to reviewed transaction records each having a user-approved account identifier. The method may further include selecting, using the comparison scores, a reviewed transaction record. The reviewed transaction record may correspond to a comparison score. The comparison score may correspond to a user-approved account identifier of the reviewed transaction record. The method may further include selecting, using the comparison score, one of the baseline account identifier and the user-approved account identifier to obtain a selected account identifier, and presenting the selected account identifier for the unreviewed transaction record.
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3.发明授权公开(公告)号：US11244340B1
公开(公告)日：2022-02-08
申请号：US15875202
申请日：2018-01-19
申请人： Intuit Inc.
摘要： User data from users/consumers is transformed into machine learning training data including historical offer attribute model training data, historical offer performance model training data, and user attribute model training data associated with two or more users/consumers, and, in some cases, millions, tens of millions, or hundreds of millions or more, users/consumers. The machine learning training data is then used to train one or more offer/attribute matching models in an offline training environment. A given current user's data and current offer data are then provided as input data to the offer/attribute matching models in an online runtime/execution environment to identify current offers predicted to have a threshold level of user interest. Recommendation data representing these offers is then provided to the user and the current user's actions with respect to the recommended offers is monitored and used as online training data.
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公开(公告)号：US20210217102A1
公开(公告)日：2021-07-15
申请号：US17218855
申请日：2021-03-31
申请人： Intuit Inc.
发明人： Meng Chen , Lei Pei , Zachary Grove Jennings , Ngoc Nhung Thi Ho
IPC分类号： G06Q40/00 , G06F16/2458 , G06N20/00
摘要： 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.
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公开(公告)号：US20180018734A1
公开(公告)日：2018-01-18
申请号：US15213096
申请日：2016-07-18
申请人： Intuit Inc.
发明人： Ngoc Nhung Ho , Meng Chen , Lei Pei
IPC分类号： G06Q40/06
CPC分类号： G06Q40/06
摘要： 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.
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公开(公告)号：US11562440B2
公开(公告)日：2023-01-24
申请号：US17218855
申请日：2021-03-31
申请人： Intuit Inc.
发明人： Meng Chen , Lei Pei , Zachary Grove Jennings , Ngoc Nhung Thi Ho
IPC分类号： G06Q40/00 , G06F16/2458 , G06N20/00
摘要： 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.
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公开(公告)号：US20220318925A1
公开(公告)日：2022-10-06
申请号：US17218079
申请日：2021-03-30
申请人： Intuit Inc.
发明人： Lei Pei , Juan Liu , Ruobing Lu , Ying Sun , Heather Elizabeth Simpson , Nhung Ho
摘要： A method utilizes a framework for transaction categorization personalization. A transaction record is received. a baseline model is selected from a plurality of machine learning models. An account identifier, corresponding to the transaction record using the baseline model, is selected. The account identifier for the transaction record is presented.
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公开(公告)号：US10984340B2
公开(公告)日：2021-04-20
申请号：US15476647
申请日：2017-03-31
申请人： INTUIT INC.
发明人： Yu-Chung Hsiao , Lei Pei , Meng Chen , Nhung Ho
摘要： 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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公开(公告)号：US10706453B1
公开(公告)日：2020-07-07
申请号：US15866005
申请日：2018-01-09
申请人： Intuit Inc.
摘要： Big data analysis methods and machine learning based models are used to provide offer recommendations to consumers that are probabilistically determined to be relevant to a given consumer. Machine learning based matching of user attributes and offer attributes is first performed to identify potentially relevant offers for a given consumer. A de-duplication process is then used to identify and eliminate any offers represented in the offer data that the consumer has already seen, has historically shown no interest in, has already accepted, that are directed to product or service types the user/consumer already owns, for which the user does not qualify, or that are otherwise deemed to be irrelevant to the consumer.
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公开(公告)号：US11935135B2
公开(公告)日：2024-03-19
申请号：US17162365
申请日：2021-01-29
申请人： Intuit Inc.
IPC分类号： G06Q40/00 , G06N3/04 , G06N3/08 , G06Q30/018 , G06Q40/12
CPC分类号： G06Q40/12 , G06N3/04 , G06N3/08 , G06Q30/018
摘要： A method may include executing a baseline classifier on unreviewed transaction features of an unreviewed transaction record to obtain a baseline account identifier, and executing a comparison model on (i) an unreviewed transaction vector of the unreviewed transaction record and (ii) reviewed transaction vectors to obtain comparison scores. The reviewed transaction vectors may correspond to reviewed transaction records each having a user-approved account identifier. The method may further include selecting, using the comparison scores, a reviewed transaction record. The reviewed transaction record may correspond to a comparison score. The comparison score may correspond to a user-approved account identifier of the reviewed transaction record. The method may further include selecting, using the comparison score, one of the baseline account identifier and the user-approved account identifier to obtain a selected account identifier, and presenting the selected account identifier for the unreviewed transaction record.
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