公开(公告)号：US20250165726A1

公开(公告)日：2025-05-22

申请号：US18954409

申请日：2024-11-20

Applicant: Intuit Inc.

Inventor： Anu SREEPATHY ,  Sudhir AGARWAL

IPC: G06F40/49 ,  G06F40/35

Abstract: Certain aspects of the disclosure provide techniques for translating a user request (e.g., posed in natural language) into a structured input using intermediate representations, to resolve the user request as a planning problem. A method generally includes generating a first intermediate representation of a first user request using a large language model (LLM), wherein the first intermediate representation comprises one or more first facts and/or one or more rules in a declarative language; generating a first materialized representation of the first user request based on the first intermediate representation and a domain description using a logic reasoner, wherein the first materialized representation comprises one or more second facts in the declarative language, and wherein the one or more second facts comprise a subset of the one or more first facts and one or more inferred facts; and generating a task description based on the first materialized representation.

公开(公告)号：US20250156911A1

公开(公告)日：2025-05-15

申请号：US18510541

申请日：2023-11-15

Applicant: Intuit, Inc.

Inventor： Jineet Hiren DOSHI ,  Maya Vered LIVSHITS ,  Apurva SWARNAKAR ,  Jingyuan ZHANG

IPC: G06Q30/0282 ,  G06F40/20

Abstract: Certain aspects of the disclosure provide a method for providing user support by generating recommended response for a customer verbatim with an ensemble of machine learning models. The method includes processing a customer verbatim with a topic model trained to identify a topic associated with the customer verbatim. The method further includes processing the customer verbatim with a sentiment model trained to determine a sentiment of the customer verbatim. The method further includes processing the customer verbatim with an actionability model trained to assign an actionability score to the customer verbatim. The method includes processing the topic, the sentiment, and the actionability score with a recommendation model to generate the recommended response to the customer verbatim.

公开(公告)号：US12299551B2

公开(公告)日：2025-05-13

申请号：US16927655

申请日：2020-07-13

Applicant: INTUIT INC.

Inventor： Shanshan Tuo ,  Neo Yuchen ,  Divya Beeram ,  Valentin Vrzheshch ,  Tomer Tal ,  Ngoc Nhung Ho

IPC: G06N20/20 ,  G06F16/9535 ,  G06F18/214 ,  G06N3/049

Abstract: Aspects of the present disclosure provide techniques for training a machine learning model. Embodiments include receiving a historical support record comprising time-stamped actions, a support initiation time, and an account indication. Embodiments include determining features of the historical support record based at least on differences between times of the time-stamped actions and the support initiation time. Embodiments include determining a label for the features based on the account indication. Embodiments include training an ensemble model, using training data comprising the features and the label, to determine an indication of an account in response to input features, wherein the ensemble model comprises a plurality of tree-based models and a ranking model.

公开(公告)号：US20250148508A1

公开(公告)日：2025-05-08

申请号：US19017382

申请日：2025-01-10

Applicant: Intuit Inc.

Inventor： Ivan SHEVCHENKO ,  Tatiana SUKHOVA

IPC: G06Q30/0241 ,  G06F40/106 ,  G06F40/186

Abstract: A method implements brand engine for extracting and presenting brand data with user interfaces. The method includes receiving a blueprint with a set of structure blocks extracted from a selected content. A structure block of the set of structure blocks includes a set of style parameter requests for a section of the selected content. The method further includes processing the set of structure blocks with a first set of smart blocks to generate a set of scores. A smart block of the first set of smart blocks includes brand data with style parameter selections. The method further includes selecting a second set of smart blocks, for the set of structure blocks, from the first set of smart blocks, using the set of scores. The method further includes presenting the second set of smart blocks with the brand data.

公开(公告)号：US20250139367A1

公开(公告)日：2025-05-01

申请号：US18495641

申请日：2023-10-26

Applicant: Intuit, Inc.

Inventor： Esmé MANANDISE ,  Anu SREEPATHY ,  Sudhir AGARWAL

IPC: G06F40/284 ,  G06F40/211 ,  G06F40/226

Abstract: Certain aspects of the disclosure provide techniques for translating a prompt into a structured input to resolve the natural langue query as a planning problem. A method generally includes identifying and classifying tokens in a prompt using a large language model (LLM); extracting from a domain description in a planning domain definition language (PDDL): object types used to categorize objects; and predicates identifying relationships between the objects that may be true or false; categorizing at least one token in the prompt as one or more of the objects, one or more of the object types, or one or more of the predicates based on the classification of the at least one token determined by the LLM; and generating a task description in the PDDL based on the categorization, the task description comprising a translation of the prompt into a structured input for a planner.

公开(公告)号：US12287793B1

公开(公告)日：2025-04-29

申请号：US18742806

申请日：2024-06-13

Applicant: Intuit Inc.

Inventor： Saikiran Sri Thunuguntla ,  Vishal Reddy Baddam ,  Suman Ghosh ,  Rajendra Tiwari

IPC: G06F16/2455 ,  G06F16/23

Abstract: Systems and methods are described for batch materialization of an incremental change data capture (CDC) changeset with full row changes. The primary keys are extracted from the incremental CDC changeset and an indication of the extracted primary keys are broadcast to a plurality of executors. The primary keys may be added to Bloom filter or a plurality of Bloom filters that are broadcast to the executors. Each executor filters a baseline data table based on the extracted primary keys to generate a baseline match dataframe with all primary keys matching the extracted primary keys, and a baseline unmatched dataframe with all primary keys not matching the extracted primary keys. Each executor receives full row changes from a partitioned incremental CDC changeset and combines the changes with the baseline unmatched dataframe to produce a final changed baseline data table.

公开(公告)号：US20250123919A1

公开(公告)日：2025-04-17

申请号：US18484613

申请日：2023-10-11

Applicant: Intuit Inc.

Inventor： John SAMUEL ,  Sandeep MEWARA ,  Murari LAL

IPC: G06F11/07 ,  G06F11/34

Abstract: Systems and methods for detecting errors in a data transfer uses a machine learning model to identify potential anomalies in the data transfer based on metadata. Mismatches between input data from the data transfer and output data after importing the data transfer may additionally be identified. User review and correction of data errors and potential anomalies identified using the machine learning model may be proactively prompted to ensure any errors or discrepancies are addressed before finalizing the import of the data transfer. User corrections are further used to retrain the machine learning model to enable continuous improvement and learning from the data transfer process.

公开(公告)号：US12271878B1

公开(公告)日：2025-04-08

申请号：US16180268

申请日：2018-11-05

Applicant: INTUIT INC.

Inventor： Bala Dutt ,  Prabhat Hegde ,  Ajay Karthik

IPC: G06Q20/14 ,  G06N20/00

Abstract: Certain aspects of the present disclosure provide techniques for providing smart content to a user of an application. Embodiments include receiving a request from a client for content. The request may include context data. Embodiments include identifying a content template for the content based on the request. Embodiments include identifying a rule associated with the content template. Embodiments include evaluating the rule based on the context data in order to determine a value of a variable. Embodiments include generating personalized content based on the content template and the value of the variable. Embodiments include providing the personalized content to the client.

公开(公告)号：US20250110948A1

公开(公告)日：2025-04-03

申请号：US18375234

申请日：2023-09-29

Applicant: Intuit Inc.

Inventor： Tin Nguyen ,  Sayan Paul ,  Lin Tao

IPC: G06F16/242 ,  G06F16/28

Abstract: Systems and methods are disclosed for converting natural language queries to a query instruction set for searching a data warehouse. To generate a query instruction set from a natural language query, a system iteratively uses a generative artificial intelligence (AI) model and database query tools to generate a query instruction set in a stepwise manner. The system and generative AI model do not require a priori knowledge of data table contents in the data warehouse, which may include sensitive information. In addition, the system does not require access to the data warehouse to generate the query instruction set. Instead, the system is implemented to use structure information from the data warehouse, including table lists (such as table names) and table format information (such as column names) of tables in the data warehouse, and the generative AI model is a generally trained model to generate the query instruction set.

公开(公告)号：US20250110760A1

公开(公告)日：2025-04-03

申请号：US18375383

申请日：2023-09-29

Applicant: Intuit Inc.

Inventor： Ismail ABDUL RAWOOF ,  Himanshu SHARMA ,  Yunqian LI ,  Falguni TRIVEDI ,  Sarah FERRIDGE

IPC: G06F9/451 ,  G06F16/34

Abstract: A method including generating a revised prompt from user customization data for customizing a user interface of an application, a pre-engineered prompt, and an application artifact from the application. The method also includes generating an output by executing a large language model on the revised prompt. The method also includes receiving a modified template generated from the user customization data and at least one of a set of templates. The method also includes transforming the output of the large language model and the modified template into both a consumable user interface component and a user interface artifact. The method also includes modifying a user interface of the application by applying the consumable user interface component and the user interface artifact to the application.