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.

公开(公告)号：US11809477B1

公开(公告)日：2023-11-07

申请号：US17994854

申请日：2022-11-28

Applicant: Intuit Inc.

Inventor： Pallabi Ghosh ,  Sparsh Gupta

IPC: G06F17/00 ,  G06F16/38

CPC classification number: G06F16/38

Abstract: This disclosure relates to extracting entities from unstructured text. The unstructured text is segmented into structured segments with one or more instances, that belong to different topics, with a topic segmentation model. Each instances of the structured segment is operated on by an entity extraction model to extract entities, and the extracted entities associated with each topic is produced in a computer-readable format. The relations between extracted entities associated with each topic may be identified.

公开(公告)号：US11797891B1

公开(公告)日：2023-10-24

申请号：US18049286

申请日：2022-10-24

Applicant: INTUIT INC.

Inventor： Yashwanth Musiboyina ,  Dawn-Marie Chantel Miesner ,  Mustapha Harb ,  Nan Jiang ,  Shahram Mohrehkesh ,  Zachary Dorsch ,  Suman Sundaresh ,  Grace Wu

IPC: G06N20/00

CPC classification number: G06N20/00

Abstract: The instant systems and methods are directed to a contextual bandits machine learning model configured to enable granular synchronized ecosystem personalization and optimization. The system and methods determine an objective and feed the objective and one more lifecycle model propensity scores as inputs to the contextual bandits machine learning model. The contextual bandits machine learning model then generates one or more potential weighted model rewards, wherein each potential weighted model reward includes at least a desired user action, a weight, a channel, and an expected change to the objective, and selects a weighted model reward that optimizes the objective. An action recommendation is subsequently transmitted to a user device based on the weighted model reward, wherein the action recommendation is presented in a selected channel associated with the weighted model reward. Feedback associated with the action recommendation is collected and used in training and fine-tuning of the model.

公开(公告)号：US11797527B2

公开(公告)日：2023-10-24

申请号：US17935238

申请日：2022-09-26

Applicant: INTUIT INC.

Inventor： Andreas Mavrommatis ,  Pankaj Rastogi ,  Sumanth Venkatasubbaiah ,  Qingbo Hu ,  Karthik Prakash ,  Nicholas Jeffrey Hoh ,  Frank Wisniewski ,  Abhishek Jain ,  Caio Vinicius Soares ,  Yuwen Wu

IPC: G06F16/23 ,  G06N20/00 ,  G06N5/02 ,  G06F9/54

CPC classification number: G06F16/2379 ,  G06F9/541 ,  G06N5/02 ,  G06N20/00

Abstract: Certain aspects of the present disclosure provide techniques for operation of a feature management platform. A feature management platform is an end-to-end platform developed to manage the full lifecycle of data features. For example, to create a stateful feature, the feature management platform can receive a processing artifact from a computing device. The processing artifact defines the stateful feature, including the data source to retrieve event data from, when to retrieve the event data, the type of transform to apply, etc. Based on the processing artifact, the feature management system generates a processing job (e.g., the API defines a pipeline), which when initiated generates a vector that encapsulates the stateful feature. The vector is transmitted to the computing device that locally hosts a model, which generates a prediction that is transmitted to the feature management platform. Subsequently, the predication and stateful feature can be transmitted to other computing devices.

公开(公告)号：US20230325693A1

公开(公告)日：2023-10-12

申请号：US18194679

申请日：2023-04-03

Applicant: INTUIT INC.

Inventor： Grace WU ,  Shashank SHASHIKANT RAO ,  Susrutha GONGALLA ,  Ngoc Nhung HO ,  Carly WOOD ,  Vaibhav SHARMA

IPC: G06N5/048 ,  G06Q10/04

CPC classification number: G06N5/048 ,  G06Q10/04

Abstract: Aspects of the present disclosure provide techniques for classifying a trip. Embodiments include receiving, from a plurality of users, a plurality of historical trip records. Each of the plurality of historical trip records may comprise one or more historical trip attributes and historical classification information. Embodiments include training a predictive model, using the plurality of historical trip records, to classify trips based on trip records. Training the predictive model may comprise determining a plurality of hot spots based on the historical trip records, each of the plurality of hot spots comprising a region encompassing one or more locations, and associating, in the predictive model, the plurality of hot spots with historical classification information. Embodiments include receiving, from a user, a new trip record comprising a plurality of trip attributes related to a trip and using the predictive model to predict a classification for the trip based on the trip record.