公开(公告)号：US11868598B2

公开(公告)日：2024-01-09

申请号：US18086630

申请日：2022-12-21

Applicant: Intuit Inc.

Inventor： Bradley Stephen Daily ,  Jacob Davidson ,  Lara Adrian Hercules ,  Stephanie Coleman ,  Alexandra Grace Kelly ,  Natalie Irene Ung

IPC: G06F3/04845 ,  G06F8/65 ,  G06F3/0482

CPC classification number: G06F3/04845 ,  G06F3/0482 ,  G06F8/65

Abstract: A content editor for generating content including root blocks and nested blocks is disclosed. The content editor can generate a deployment that includes the content. The content editor can generate user interface code configured to edit the content. The content editor can receive updates to the content and update the root blocks and nested blocks. The updated root blocks and nested blocks can be used to generate updated content for editing and/or can be deployed to end-users.

公开(公告)号：US20240005651A1

公开(公告)日：2024-01-04

申请号：US18135046

申请日：2023-04-14

Applicant: Intuit Inc.

Inventor： Miriam Hanna Manevitz ,  Aviv Ben Arie

IPC: G06V10/82 ,  G06N3/045 ,  G06V10/774

CPC classification number: G06V10/82 ,  G06N3/045 ,  G06V10/774

Abstract: A method includes training, using first real data objects, a generative adversarial network having a generator model and a discriminator model to create a trained generator model that generates realistic data, and training, using adversarial data objects and second real data objects, the discriminator model to output an authenticity binary class for the adversarial data objects and the second real data objects. The method further includes deploying the discriminator model to a production system. In the production system, the discriminator model outputs the authenticity binary class to a system classifier model.

公开(公告)号：US20240005084A1

公开(公告)日：2024-01-04

申请号：US17809658

申请日：2022-06-29

Applicant: INTUIT INC.

Inventor： Omer ZALMANSON ,  Yair HORESH

IPC: G06F40/166 ,  G06N5/04 ,  G06N5/02

CPC classification number: G06F40/166 ,  G06N5/04 ,  G06N5/022

Abstract: Aspects of the present disclosure relate to electronic document creation assistance. Embodiments include determining a current time related to creation of a document by a user and providing inputs to a machine learning model based on the current time. Embodiments include receiving output from the machine learning model based on the inputs and selecting, based on the output, a first recommended item from a plurality of items for inclusion in the document. Embodiments include determining a likelihood of each additional item of the plurality of items co-occurring with the first recommended item based on historical item co-occurrence data. Embodiments include selecting, based on the output and the likelihood of each additional item of the plurality of items co-occurring with the first recommended item, a second recommended item for inclusion in the document and providing, via a user interface, the first recommended item and the second recommended item to the user.

公开(公告)号：US11861633B2

公开(公告)日：2024-01-02

申请号：US18180092

申请日：2023-03-07

Applicant: INTUIT INC.

Inventor： Vijay Manikandan Janakiraman ,  Kevin Michael Furbish ,  Nirmala Ranganathan ,  Kymm K. Kause

IPC: G06Q30/02 ,  G06Q30/0201 ,  G06F16/215 ,  G06N20/10 ,  G06Q30/0207 ,  G06F18/24 ,  G06Q30/0601

CPC classification number: G06Q30/0201 ,  G06F16/215 ,  G06F18/24 ,  G06N20/10 ,  G06Q30/0239 ,  G06Q30/0631

Abstract: 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.

公开(公告)号：US11861308B2

公开(公告)日：2024-01-02

申请号：US16849797

申请日：2020-04-15

Applicant: INTUIT INC.

Inventor： Sricharan Kallur Palli Kumar ,  Cynthia Joann Osmon ,  Conrad De Peuter ,  Roger C. Meike ,  Gregory Kenneth Coulombe ,  Pavlo Malynin

IPC: G06F40/279 ,  G06N20/00 ,  G06F16/24 ,  G06N5/02

CPC classification number: G06F40/279 ,  G06F16/24 ,  G06N5/02 ,  G06N20/00

Abstract: Certain aspects of the present disclosure provide techniques for processing natural language utterances in a knowledge graph. An example method generally includes receiving a long-tail query comprising a natural language utterance from a user of an application. Operands and operators are extracted from the natural language utterance using a natural language model. Operands may be mapped to nodes in a knowledge graph, the nodes representing values calculated from data input into the application, and operators may be mapped to operations to be performed on data extracted from the knowledge graph. The functions associated with the operators are executed using data extracted from the nodes in the knowledge graph associated with the operands to generate a query result. The query result is returned as a response to the received long-tail query.

公开(公告)号：US11860949B2

公开(公告)日：2024-01-02

申请号：US17568573

申请日：2022-01-04

Applicant: INTUIT INC.

Inventor： Yair Horesh ,  Yehezkel Shraga Resheff ,  Oren Sar Shalom ,  Alexander Zhicharevich

IPC: G06F17/00 ,  G06F16/903 ,  G06F16/93 ,  G06N20/00

CPC classification number: G06F16/90344 ,  G06F16/93 ,  G06N20/00

Abstract: Automatic keyphrase labeling and machine learning training may include a processor extracting a plurality of keywords from at least one search query that resulted in a selection of a document appearing in a search result. For each of the plurality of keywords, the processor may determine a probability that the keyword describes the document. The processor may generate one or more keyphrases by performing processing including selecting each of the plurality of keywords having a probability greater than a predetermined threshold value for insertion into at least one of the one or more keyphrases and assembling the one or more keyphrases from the selected plurality of keywords. The processor may label the document with the keyphrase.

公开(公告)号：US11853448B1

公开(公告)日：2023-12-26

申请号：US18161966

申请日：2023-01-31

Applicant: INTUIT INC.

Inventor： Ranadeep Bhuyan ,  Steven Michael Saxon ,  Aminish Sharma

IPC: G06F11/00 ,  G06F21/62 ,  G06F11/07 ,  G06F11/34

CPC classification number: G06F21/6218 ,  G06F11/0793 ,  G06F11/3452

Abstract: The present disclosure provides techniques for recommending vendors using machine learning models. One example method includes generating a dependency graph based on one or more microservices, computing, for each microservice of the one or more microservices, a complexity score using the dependency graph, identifying a subset of the one or more microservices, wherein each microservice in the subset of the one or more microservices has a complexity score meeting a threshold value, and applying a transactional lock on each microservice in the subset of the one or more microservices.

公开(公告)号：US11831633B1

公开(公告)日：2023-11-28

申请号：US18299702

申请日：2023-04-12

Applicant: INTUIT INC.

Inventor： Snezana Sahter ,  Kumar Govind Jha ,  Saurabh Mistry ,  Mukesh Garg ,  Sivaraman Sathyamurthy

IPC: H04L9/40

CPC classification number: H04L63/0815 ,  H04L63/0807

Abstract: A federation link is used to facilitate bi-directional identity federation between software applications. The federation link is created to include user and account identity information for software applications having respective authentication providers. The federation link is created by one of the software applications and shared, for example, with the authentication provider of the other software application. The federation link can be utilized by both software applications to facilitate automated user authentication when navigating in either direction between the software applications.

公开(公告)号：US11829894B2

公开(公告)日：2023-11-28

申请号：US16917656

申请日：2020-06-30

Applicant: Intuit Inc.

Inventor： Shlomi Medalion ,  Yehezkel Shraga Resheff ,  Sigalit Bechler ,  Elik Sror

IPC: G06F7/00 ,  G06N5/04 ,  G06N20/00 ,  G06F16/23

CPC classification number: G06N5/04 ,  G06F16/2379 ,  G06N20/00

Abstract: A method for classifying organizations involves obtaining, for an unknown organization, transactional data representing a multitude of transactions. The transactional data comprises a descriptive text for each of the multitude of transactions. The method further involves processing the descriptive text for each of the multitude of transactions to obtain one vector representing the unknown organization, categorizing the unknown organization using a classifier applied to the vector, and identifying a software service for the unknown organization, according to the categorization.

公开(公告)号：US11829866B1

公开(公告)日：2023-11-28

申请号：US15855702

申请日：2017-12-27

Applicant: Intuit Inc.

Inventor： Efraim Feinstein ,  Riley F. Edmunds

IPC: G06N3/047 ,  G06N3/045 ,  G06N3/08 ,  H04L9/40

CPC classification number: G06N3/08 ,  G06N3/045 ,  G06N3/047 ,  H04L63/0272 ,  H04L63/1425

Abstract: A method and system distinguish between anomalous members of a majority group and members of a target group. The system and method utilize a neural network architecture that attends to each level of a classification hierarchy. The system and method chain a semi-supervised autoencoder with a supervised classifier neural network. The autoencoder is trained in a semi-supervised manner with a machine learning process to identify user profile data that are typical of a majority class. The classifier neural network is trained in a supervised manner with a machine learning process to distinguish between user profile data that are anomalous members of the majority class and user profile data that are members of the target class.