公开(公告)号：US20240232898A1

公开(公告)日：2024-07-11

申请号：US18151258

申请日：2023-01-06

Applicant: INTUIT INC.

Inventor： Lior TABORI ,  Ido Meir MINTZ ,  Hagay FINE ,  Alex ZICHAREVICH

IPC: G06Q20/40 ,  G06N20/00

CPC classification number: G06Q20/405 ,  G06N20/00

Abstract: The present disclosure provides techniques for recommending vendors using machine learning models. One example method includes generating a set of features based on a sequence of recurring transactions associated with a user, a payee, and a transaction amount, predicting simultaneously, using a machine learning model based on the set of features, that the sequence of recurring transactions will continue with a subsequent transaction and a time window within which the subsequent transaction of the sequence will occur, receiving electronic transaction data indicative of a transaction associated with the user, the payee, and the transaction amount, indicating that the transaction is the subsequent transaction in the sequence of recurring transactions based on the transaction and the prediction, and automatically creating one or more future recurring transactions based on the indication.

公开(公告)号：US20250021844A1

公开(公告)日：2025-01-16

申请号：US18222353

申请日：2023-07-14

Applicant: Intuit, Inc.

Inventor： Omer ZALMANSON ,  Yair HORESH ,  Lior TABORI

IPC: G06N5/046 ,  G06N5/022

Abstract: Certain aspects of the disclosure provide a method for training a machine learning model to predict text containing sensitive information. The method includes extracting one or more features from a historical data set. The method further includes anonymizing the historical data set, including determining, for each feature of the extracted one or more features, tokens containing personally identifiable information (sensitive information); assigning a category placeholder to each of the tokens containing sensitive information; and generating a new data set where each token containing sensitive information is replaced with the assigned category placeholder. The method further includes determining a probability associated with each token containing sensitive information; and training a generalized model to predict anonymized text given the one or more features.

公开(公告)号：US20240028973A1

公开(公告)日：2024-01-25

申请号：US17869780

申请日：2022-07-20

Applicant: INTUIT INC.

Inventor： Sheer DANGOOR ,  Daniel BEN DAVID ,  Ido Meir MINTZ ,  Alexander ZHICHAREVICH ,  Lior TABORI

IPC: G06Q30/04 ,  G06N20/20

CPC classification number: G06Q30/04 ,  G06N20/20

Abstract: A method including training, using training data including a first ontological hierarchical level, trained machine learning models (MLMs) to predict a first output type including a second ontological hierarchical level different than the first ontological hierarchical level. The method also includes generating instances of the first output type by executing the trained MLMs on unknown data including the first ontological hierarchical level. Outputs of the trained MLMs include the instances at the second ontological hierarchical level. The method also includes training, using the instances, a voting classifier MLM to predict a selected instance from the instances. The voting classifier MLM is trained to predict the selected instance to satisfy a criterion including a third ontological hierarchical level different than the first ontological hierarchal level and the second ontological hierarchical level.