公开(公告)号：US11887579B1

公开(公告)日：2024-01-30

申请号：US17955412

申请日：2022-09-28

Applicant: Intuit Inc.

Inventor： Jianxiang Chang ,  Sayan Paul

IPC: G10L13/02

CPC classification number: G10L13/02

Abstract: This disclosure relates to generating a comprehensive set of synthetic utterances. An example system is configured to provide an input utterance to a plurality of synthetic utterance generation pipelines in parallel. Each of the plurality of synthetic utterance generation pipelines include one or more utterance synthesizers. For example, one or more pipelines may use a synthesizer chain that includes a plurality of synthesizers in parallel. The plurality of synthetic utterance generation pipelines generates synthetic utterances, which may be stored in a database after evaluating the similarity between the original input utterance and each resulting synthetic utterance. For example, a synthetic utterance may be retained if the cosine similarity between the input and synthetic utterances is less than a predetermined threshold. Additionally, the synthetic utterances may be fed back at input utterances based on the similarity evaluation and the feedback loop repeated until a desired number of utterances are generated.

公开(公告)号：US11886818B2

公开(公告)日：2024-01-30

申请号：US18090229

申请日：2022-12-28

Applicant: Intuit Inc.

Inventor： Liora Braunstein ,  Keren Cohavi ,  Yoav Spector ,  Kiril Lashchiver

IPC: G06F40/284 ,  G06F40/205 ,  G06F40/237 ,  G06V30/414 ,  G06F18/21

CPC classification number: G06F40/284 ,  G06F18/21 ,  G06F40/205 ,  G06F40/237 ,  G06V30/414

Abstract: A method including isolating a protocol language of a data set comprising a text structure representing data regarding a network communication procedure between a plurality of user devices, wherein the protocol language comprises a pattern for implementing the network communication procedure; generating a document from the data set, wherein the document includes a text structure, organizing, in light of the protocol language, the text structure into a natural language scheme; and detecting, using the natural language scheme, insights in the document.

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.

公开(公告)号：US11875123B1

公开(公告)日：2024-01-16

申请号：US18362890

申请日：2023-07-31

Applicant: Intuit Inc.

Inventor： Daniel Ben David ,  Kenneth Grant Yocum

IPC: G06F40/30 ,  G06N20/00 ,  G06F40/40 ,  G06F40/103

CPC classification number: G06F40/30 ,  G06F40/103 ,  G06F40/40 ,  G06N20/00

Abstract: The one or more embodiments provide for a method, system, and computer program product, an intent, generated by a large language model from a text, is received from a user device as a first input to an advice planner. A state of an account is received as a second input to the advice planner. The advice planner classifieds the intent into a domain corresponding to the intent, and generates, as output, a plan comprising a first set of action logic associated with the domain. Each action logic is a discrete step in an ordered sequence for achieving a desired state of the account. The advice planner forwards the plan to the large language model (LLM). The large language model receives the plan as input and generates advice in a natural language format as output. The advice is then forwarded to the user device.

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.