Intuit, Inc.

Preetam Kumar Ojha

Massimo Mascaro

Yao H Morin

Dharin L Nanavati

Wade P Fuller

Ojas R Sitapara

Joel R Minton

Ravindra Bindumadhav Kulkarni

Anqi Chen

Daniel Edward Reeves

A method and system identifies users who benefit from filing itemized deductions over standardized deductions to reduce an average time consumed for users preparing tax returns with a tax return preparation system, according to one embodiment. The method and system receives user data that is associated with a user, and applies the user data to a predictive model to cause the predictive model to determine a likelihood that the user will decrease his/her taxable income by filing an itemized deduction, according to one embodiment. The method and system deemphasizes and/or postpones the presentation of tax return questions that are related to the itemized deduction, if the likelihood that the user will decrease his/her taxable income by filing the itemized deduction is below a threshold, to reduce a quantity of time consumed by the user to prepare his/her tax return with a tax return preparation system, according to one embodiment.

Method and system for identifying users who benefit from filing itemized deductions to reduce an average time consumed for users preparing tax returns with a tax return preparation system

A method and system applies analytics models to a tax return preparation system to determine a likelihood of qualification for an earned income tax credit by a user, according to one embodiment. The method and system receive user data and applying the user data to a predictive model to cause the predictive model to determine, at least partially based on the user data, a likelihood of qualification for an earned income tax credit for the user, according to one embodiment. The method and system display, for the user, an estimated tax return benefit to the user, at least partially based on the likelihood of qualification for the earned income tax credit exceeding a predetermined threshold, to reduce delays in presenting estimated earned income tax credit benefits to the user during a tax return preparation session in a tax return preparation system, according to one embodiment.

Method and system for applying analytics models to a tax return preparation system to determine a likelihood of receiving earned income tax credit by a user

The disclosed embodiments provide a system that facilitates user interaction. During operation, the system obtains user data for a first user and a second user connected to the first user in a social network. Next, the system uses the user data to calculate one or more influence scores between the first and second users. The system then constructs an influence graph of a set of users comprising the first and second users by creating a first node representing the first user, creating a second node representing the second user, and using the one or more influence scores as edge weights of directed edges between the first and second nodes. Finally, the system uses the influence graph to facilitate interaction among the users.

Determining influence across social networks

Disclosed are methods, systems, and articles of manufactures for analyzing user generated content items in social media networks with trained intelligent systems to create or enhance direct marketing opportunities. The method or the system monitors user generated content items in social media networks and identifies a relevant user generated content item that may be materialized into a direct marketing opportunity. The method or system further performs language processing on the relevant user generated content item and uses the processing results to prepare a response which is subsequently transmitted to the user to materialize the direct marketing opportunity. The method or system uses various intelligent logic processes or modules that may be further enhanced by machine learning techniques with human expert reviews and intervention to improve their respective accuracy, reliability, or confidence level.

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Boston, MA, United States of America

Preetam Kumar Ojha