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.

公开(公告)号：US20230376764A1

公开(公告)日：2023-11-23

申请号：US18362094

申请日：2023-07-31

Applicant: INTUIT INC.

Inventor： William T. LAASER

IPC: G06N3/08 ,  G06F17/11 ,  G06N20/00 ,  G06F18/21 ,  G06F18/214

CPC classification number: G06N3/08 ,  G06F17/11 ,  G06N20/00 ,  G06F18/217 ,  G06F18/2155

Abstract: Systems and methods of the present disclosure provide processes for determining how much to adjust machine-learning parameter values in a direction of a gradient for gradient-descent steps in training processes for machine-learning models. Current parameter values of a machine-learning model are vector components that define an initial estimate for a local extremum of a cost function used to measure how well the machine-learning model performs. The initial estimate and the gradient of the cost function for the initial estimate are used to define an auxiliary function. A root estimate is determined for the auxiliary function of the gradient. The parameters are adjusted in the direction of the gradient by an amount specified by the root estimate.

公开(公告)号：US11822563B2

公开(公告)日：2023-11-21

申请号：US17387115

申请日：2021-07-28

Applicant: Intuit Inc.

Inventor： Naveen Kumar Kaveti ,  Sravya Sri Garapati ,  Vignesh Thirukazhukundram Subrahmaniam

IPC: G06F16/2458 ,  G06N20/00

CPC classification number: G06F16/2474 ,  G06N20/00

Abstract: Systems and methods for scoring potential actions are disclosed. An example method may be performed by one or more processors of a system and include training a machine learning model based at least in part on a sequential database and retention data, identifying an action subsequence executed by a user, generating, for each of a plurality of potential actions, using the machine learning model, a first value indicating a probability that the user will execute the potential action immediately after executing the action subsequence, a second value indicating a probability that the user will continue to use the system if the user executes the potential action immediately after executing the action subsequence, and a confidence score indicating a likelihood that recommending the potential action to the user will result in the user continuing to use the system, the confidence score generated based on the first value and the second value.

公开(公告)号：US20230368169A1

公开(公告)日：2023-11-16

申请号：US17742086

申请日：2022-05-11

Applicant: Intuit Inc.

Inventor： Alexander ZICHAREVICH ,  Ido Meir MINTZ ,  Yair HORESH

IPC: G06Q20/14 ,  G06Q30/04

CPC classification number: G06Q20/14 ,  G06Q30/04

Abstract: Systems and methods of optimizing cash flow are disclosed. A system obtains bill information regarding a plurality of bills and invoice information regarding a plurality of invoices, and the system pairs one or more bills to one or more invoices. Pairing the one or more bills includes, for each bill, generating one or more potential pairs of the bill to an invoice. For each potential pair, the system calculates a matching score associated with the potential pair based on the bill information of the bill and the invoice information of the invoice, identifies a subset of potential pairs of the one or more potential pairs associated with a threshold matching score, and selects a pair of a paired invoice to the bill from the subset of potential pairs. The system generates instructions to automatically pay the one or more bills, with payment scheduled based on the pairings.

公开(公告)号：US11817088B1

公开(公告)日：2023-11-14

申请号：US18299700

申请日：2023-04-12

Applicant: INTUIT INC.

Inventor： Shrutendra Harsola ,  Sourav Prosad ,  Viswa Datha Polavarapu

IPC: G10L15/16 ,  G10L15/197 ,  G06N3/045 ,  G06N3/047

CPC classification number: G10L15/16 ,  G06N3/045 ,  G06N3/047 ,  G10L15/197

Abstract: An ensemble of machine learning models used for real-time prediction of text for an electronic chat with an expert user. A global machine learning model, e.g., a transformer model, trained with domain specific knowledge makes a domain specific generalized prediction. Another machine learning model, e.g., an n-gram model, learns the specific style of the expert user as the expert user types to generate more natural, more expert user specific text. If specific words cannot be predicted with a desired probability level, another word level machine learning model, e.g., a word completion model, completes the words as the characters are being typed. The ensemble therefore produces real-time, natural, and accurate text that is provided to the expert user. Continuous feedback of the acceptance/rejection of predictions by the expert is used to fine tune one or more machine learning models of the ensemble in real time.

公开(公告)号：US20230360145A1

公开(公告)日：2023-11-09

申请号：US18353715

申请日：2023-07-17

Applicant: INTUIT INC.

Inventor： Aveer Ratan THAKUR ,  Sameer BALASUBRAHMANYAM ,  Dipesh KHAKHKHAR

IPC: G06Q40/12 ,  G06F16/23 ,  G06Q10/10 ,  G06Q20/40 ,  G06Q40/03

CPC classification number: G06Q40/12 ,  G06F16/2379 ,  G06Q10/10 ,  G06Q20/405 ,  G06Q40/03

Abstract: Certain aspects of the present disclosure provide techniques for processing transactions in a computing system. An example method generally includes receiving a request to perform an operation with respect to an object included in the request. A system identifies an archetype defining properties of the object included in the request. Based on the identified archetype, the system identifies data repositories to commit data to in order to perform the requested operation and rules for performing the operation with respect to the object. One or more actions are executed against the identified data repositories according to the identified rules.

公开(公告)号：US11809980B1

公开(公告)日：2023-11-07

申请号：US18309470

申请日：2023-04-28

Applicant: INTUIT INC.

Inventor： Vignesh Radhakrishnan

IPC: G06N3/045 ,  G06N3/08 ,  G06F21/62 ,  G06F16/28 ,  G06N3/00

CPC classification number: G06N3/045 ,  G06F16/285 ,  G06F21/6245 ,  G06N3/00 ,  G06N3/08

Abstract: Aspects of the present disclosure provide techniques for automated data classification through machine learning. Embodiments include providing first inputs to a first machine learning model based on a column header of a column from a table and receiving a first output from the first machine learning model in response to the first inputs, wherein the first output indicates a first likelihood that the column relates to a given classification. Embodiments include providing second inputs to a second machine learning model based on a value from the column and receiving a second output from the second machine learning model in response to the second inputs, wherein the second output indicates a second likelihood that the value relates to the given classification. Embodiments include determining whether to associate the value with the given classification based on the first output and the second output.