Intuit, Inc.

Prateek Vasant Kakirwar

Avinash Thekkumpat

Neha Kumari

Jeffrey Chen

Mahesh Subramanian

Kishore Nene

Lulu Cheng

Siddhartha Misra

Tarek Rabbani

Anilkumar Pillai

Nageswara Prasad Cherukuri

Computerized systems and methods for efficiently refreshing financial data for financial accounts for login identifications of users at respective financial institutions in a batch data pull via a communication network. The login identifications are prioritized for being refreshed based on determining a probability that each login identification has a new transaction since the last successful refresh of the login identification using a learning algorithm utilizing past financial data. The login identifications are then refreshed in a batch data pull from the financial institutions in an order from highest probability to lowest probability of having a new transaction since the last batch data pull.

Methods and systems for efficiently retrieving financial data for financial accounts via a comunication network

The present disclosure relates to processing domain-specific natural language commands. An example method generally includes receiving a natural language command. A command processor compares the received natural language command to a corpus of known commands to identify a probable matching command to the received natural language command, wherein the matching is based on an edition error and a recognition error. The corpus of known commands comprises a plurality of domain-specific commands, each of which is mapped to one or more domain-specific entities. Based on the comparison, the command processor identifies one or more entities in the received natural language command to perform an action on based on the mapping of the one or more domain-specific entities in the probable matching command and executes a domain-specific action included in the natural language command on the identified entity.

Extracting domain-specific actions and entities in natural language commands recognized based on edition and recognition scores

The present disclosure relates to processing domain-specific natural language commands. An example method generally includes receiving a natural language command. A command processor compares the received natural language command to a corpus of known commands to identify a probable matching command in the corpus of known commands to the received natural language command. The corpus of known commands comprises a plurality of domain-specific commands, each of which is mapped to a domain-specific action. Based on the comparison, the command processor identifies the domain-specific action associated with the probable matching command to perform in response to the received command and executes the identified domain-specific action.

Extracting domain-specific actions and entities in natural language commands

The invention relates to a method for identifying a life event affecting a user. The method includes obtaining a classification model. The classification model models how financial data of a user relates to life events. Also, the method includes gathering the financial data of the user. The financial data of the user matches at least a subset of the classification model. In addition, the method includes identifying a life event affecting the user by applying the classification model to the financial data of the user. Still yet, the method includes customizing a user interface of an application that is utilized by the user. The customization is based on the identified life event.

Utilizing financial data of a user to identify a life event affecting the user

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Newark, CA, United States of America

Prateek Vasant Kakirwar