Intuit, Inc.

Shankar Sankararaman

Jaspreet Singh

Pragya Tripathi

Lan Jin

Shivani Gowrishankr

A processor may receive user information for a request payload from an external device and data describing a plurality of user interface (UI) elements configured to be presented in a UI of the external device. The processor may select a machine learning (ML) model from a plurality of ML models using a contextual bandit ML model that is trained based on the user information. The processor determines at least one recommended user interface (UI) element with a selected ML model, based on the user information and the data describing the plurality of UI elements. The at least one recommended UI element may be presented in the UI of the external device. The processor may receive event data indicating a user interaction with the at least one recommended UI element in the UI of the external device. The contextual bandit ML model may be re-trained based on the event data.

Contextual bandit for multiple machine learning models for content delivery

A method for automatically recommending to a user of a software application one or more categories of a plurality of different categories of tax deductible expenses includes providing input data to a trained machine learning model and receiving output from the trained machine learning model based on the input data. The output includes a recommendation for the user that includes (i) one or more categories of the plurality of different categories of tax deductible expenses; and (ii) a plurality of examples of tax deductible expenses for each of the one or more categories. The method includes receiving feedback from the user on the recommendation and generating updated training data for the trained machine learning model based on the feedback.

Machine learning based approach for recommending different categories of tax deductible expenses and related examples of tax deductible expenses for each category

Systems and methods for tracking anonymous visitors of an online website or mobile app are disclosed. The browsing activity by an anonymous visitor of the online website or mobile app is converted into features and a visitor-identifier associated with the browsing activity generated by the anonymous visitor is determined. The features are stored with the visitor-identifier in a super-visitor-state before the visitor-identifier is associated with a super-visitor-identifier. After the visitor-identifier is associated with the super-visitor-identifier, the features are stored with the super-visitor-identifier in the super-visitor-state. After the visitor-identifier is associated with the super-visitor-identifier, the features associated with the visitor-identifier in the super-visitor-state may be combined, e.g., aggregated, with the features associated with the super-visitor-identifier and the visitor-identifier may be removed from the super-visitor-state.

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Burlingame, CA, United States of America

Shankar Sankararaman