公开(公告)号：US11783605B1

公开(公告)日：2023-10-10

申请号：US17855469

申请日：2022-06-30

Applicant: INTUIT INC.

Inventor： Amogha Sekhar ,  Eric Vanoeveren ,  Deepankar Mohapatra ,  Tharathorn Rimchala ,  Priyadarshini Rajendran

IPC: G06K9/00 ,  G06V30/14 ,  G06V30/19

CPC classification number: G06V30/1448 ,  G06V30/19173

Abstract: Certain aspects of the present disclosure provide techniques for training and using machine learning models to extract key-value sets from a document. An example method generally includes identifying regions of a document including key-value sets corresponding to inputs to a data processing application based on a first machine learning model and an electronic version of the document. One or more keys and one or more values are identified in the document based on a second machine learning model. One or more key-value sets are generated based on matching keys of the one or more keys and values of the one or more values in the region of the document. The one or more key-value sets are provided to a data processing application for processing.

公开(公告)号：US20230316157A1

公开(公告)日：2023-10-05

申请号：US18328041

申请日：2023-06-02

Applicant: INTUIT INC.

Inventor： Terrence J. TORRES ,  Tharathorn Rimchala ,  Andrew Mattarella-Micke

IPC: G06N20/20 ,  G06F40/126 ,  G06F40/284

CPC classification number: G06N20/20 ,  G06F40/126 ,  G06F40/284

Abstract: A machine learning system executed by a processor may generate predictions for a variety of natural language processing (NLP) tasks. The machine learning system may include a single deployment implementing a parameter efficient transfer learning architecture. The machine learning system may use adapter layers to dynamically modify a base model to generate a plurality of fine-tuned models. Each fine-tuned model may generate predictions for a specific NLP task. By transferring knowledge from the base model to each fine-tuned model, the ML system achieves a significant reduction in the number of tunable parameters required to generate a fine-tuned NLP model and decreases the fine-tuned model artifact size. Additionally, the ML system reduces training times for fine-tuned NLP models, promotes transfer learning across NLP tasks with lower labeled data volumes, and enables easier and more computationally efficient deployments for multi-task NLP.

公开(公告)号：US11775504B2

公开(公告)日：2023-10-03

申请号：US17855693

申请日：2022-06-30

Applicant: Intuit Inc.

Inventor： Vitor R. Carvalho ,  Janani Kalyanam ,  Leah Zhao ,  Peter Ouyang

IPC: G06F16/23 ,  G06F16/2458 ,  G06F16/22

CPC classification number: G06F16/2365 ,  G06F16/2246 ,  G06F16/2462

Abstract: A method for computer estimations based on statistical tree structures involves obtaining a statistical tree structure for reference elements. The statistical tree structure includes leaf nodes segmenting a statistic for a data label according to data features in the reference elements, and intermediate nodes connecting a first node to the leaf nodes. Each of the first node and the intermediate nodes provide a branching based on one of the data features. The method further includes obtaining target data, including values for the data features, and a value for the data label. The method also includes selecting the first node, associated with a first data feature, traversing the statistical tree structure to a leaf node by matching the values of the data features to the branching of the intermediate nodes, and assessing the value for the data label in the target data based on the statistic associated with the leaf node.

公开(公告)号：US11768813B1

公开(公告)日：2023-09-26

申请号：US17958186

申请日：2022-09-30

Applicant: Intuit Inc.

Inventor： Smit Shah ,  Raymond Chan ,  Suresh Muthu ,  Snezana Sahter

IPC: G06F15/16 ,  G06F16/21 ,  G06Q10/067

CPC classification number: G06F16/211 ,  G06Q10/067

Abstract: A method may include selecting a cohort of entities for migration from a source storage repository to a target storage repository, obtaining a mapping between a source storage schema of the source storage repository to a target storage schema of the target storage repository, and migrating data for the entities in the cohort. Migrating the data of an entity may include copying, without locking the data in the source storage repository and in the target storage repository, the data from the source storage repository to the target storage repository, verifying, while the data is locked, that the data in the source storage repository is the same as the data in the target storage repository, changing, while the data in the source storage repository and the target storage repository is locked, an entity pointer for the entity to the target storage repository based on the verifying, and unlocking the data.

公开(公告)号：US20230298051A1

公开(公告)日：2023-09-21

申请号：US17700223

申请日：2022-03-21

Applicant: INTUIT INC.

Inventor： Aviv BEN ARIE ,  Yair HORESH

IPC: G06Q30/02

CPC classification number: G06Q30/0201

Abstract: Certain aspects of the present disclosure provide techniques for a personalized reporting service. Software applications can provide relevant reports that aggregate time series data to users that meet certain baseline values, including a threshold, timeframe, and cadence. The baseline values can be determined by a trained machine-learning model that identifies “interesting” trend components in the time series data. The reporting service can receive configurations from the user including feedback that are utilized by the machine-learning model to update the baseline values and provide relevant reports to the user.

公开(公告)号：US20230289359A1

公开(公告)日：2023-09-14

申请号：US18200445

申请日：2023-05-22

Applicant: Intuit Inc.

Inventor： Jayanth Saimani ,  Ajay Karthik Nama Nagaraj

IPC: G06F16/248 ,  G06F16/22 ,  G06F16/245

CPC classification number: G06F16/248 ,  G06F16/2246 ,  G06F16/245

Abstract: A method including receiving a first command including both a data extraction expression and a first report configuration expression. The data extraction expression includes program code for extracting fields of a dataset of a data source. The first report configuration expression includes program code configured to populate cells of first dimensions of a first report and to generate a first tree including subset nodes including records of the dataset. The first command is executed by executing the data extraction expression on the dataset to generate the records. Executing the first command also includes executing the first report configuration expression on the records to generate the first tree. Executing the first command also includes populating, using the first report configuration expression and the first tree, the cells. Executing the first command also includes generating, in response to receiving the first command and by traversing the first tree, the first report.

公开(公告)号：US11755846B1

公开(公告)日：2023-09-12

申请号：US18050973

申请日：2022-10-28

Applicant: INTUIT INC.

Inventor： Itay Margolin ,  Yair Horesh

IPC: G06F40/40 ,  G06F16/35 ,  G06F40/284

CPC classification number: G06F40/40 ,  G06F16/35 ,  G06F40/284

Abstract: Methods and systems for efficiently generating tagged training data for machine learning models. In conventional systems, all of the raw data (e.g., each sentence) has to be manually tagged. Instead, the methods and systems generate a representative sample for multiple portions of raw data, e.g., a representative sentence for multiple, similar sentences. Only the representative sample is tagged and used for training, thereby realizing a significant efficiency in both tagging the data and training the machine learning models.

公开(公告)号：US11755774B1

公开(公告)日：2023-09-12

申请号：US17876698

申请日：2022-07-29

Applicant: INTUIT INC.

Inventor： Sangeetha Uthamalingam Santharam

IPC: G06F21/62 ,  H04L51/08 ,  G06V30/413 ,  G06V30/19 ,  G06F3/0484

CPC classification number: G06F21/6245 ,  G06F3/0484 ,  G06V30/19 ,  G06V30/413 ,  H04L51/08

Abstract: Certain aspects of the present disclosure provide techniques and systems for screening chat attachments. A chat attachment screening system monitors a chat window of a first computing device associated with a first user during an interaction session between the first user and a second user. An upload of an attachment is detected based on the monitoring. Access to the attachment from a second computing device associated with the second user is blocked, in response to detecting the upload. Content from the attachment is identified and extracted. A type of the attachment is determined based on the content. A determination is made as to whether the second user is authorized to access the type of the attachment. An indication of the determination is presented on at least one of the first computing device or the second computing device during the interaction session.

公开(公告)号：US20230281399A1

公开(公告)日：2023-09-07

申请号：US17653426

申请日：2022-03-03

Applicant: INTUIT INC.

Inventor： Prarit LAMBA ,  Clifford GREEN ,  Tomer TAL ,  Andrew MATTARELLA-MICKE

IPC: G06F40/58 ,  G06F40/56 ,  G06K9/62

CPC classification number: G06F40/58 ,  G06F40/56 ,  G06K9/6257

Abstract: Embodiments disclosed herein provide language-agnostic routing prediction models. The routing prediction models input text queries in any language and generate a routing prediction for the text queries. For a language that may have sparse training text data, the models, which are machine learning models, are trained using a machine translation to a prevalent language (e.g., English) to the language having sparse training text data -with the original text corpus and the translated text corpus being an input to multi-language embedding layers. The trained machine learning model makes routing predictions for text queries for the language having sparse training text data.

公开(公告)号：US11749006B2

公开(公告)日：2023-09-05

申请号：US17551236

申请日：2021-12-15

Applicant: INTUIT INC.

Inventor： Sameeksha Khillan ,  Prajwal Prakash Vasisht

IPC: G06V30/19 ,  G06V30/162 ,  G06V30/26 ,  G06V30/12

CPC classification number: G06V30/133 ,  G06V30/162 ,  G06V30/19113 ,  G06V30/26

Abstract: A processor may receive an image and determine a number of foreground pixels in the image. The processor may obtain a result of optical character recognition (OCR) processing performed on the image. The processor may identify at least one bounding box surrounding at least one portion of text in the result and overlay the at least one bounding box on the image to form a masked image. The processor may determine a number of foreground pixels in the masked image and a decrease in the number of foreground pixels in the masked image relative to the number of foreground pixels in the image. Based on the decrease, the processor may modify an aspect of the OCR processing for subsequent image processing.