Intuit, Inc.

Tomer Tal

Neo Yuchen

Shanshan Tuo

Divya Beeram

Meng Chen

Nivethitha Kumar

Wan Yu Zhang

Kavita Sundar

Ngoc Nhung Ho

Valentin Vrzheshch

Certain embodiments of the present disclosure provide techniques training a user detection model to identify a user of a software application based on voice recognition. The method generally includes receiving a data set including a plurality of voice interactions with users of a software application. For each respective recording in the data set, a spectrogram representation is generated based on the respective recording. A plurality of voice recognition models are trained. Each of the plurality of voice recognition models is trained based on the spectrogram representation for each of the plurality of voice recordings in the data set. The plurality of voice recognition models are deployed to an interactive voice response system.

Method and system for user voice identification using ensembled deep learning algorithms

Aspects of the present disclosure provide techniques for training a machine learning model. Embodiments include receiving a historical support record comprising time-stamped actions, a support initiation time, and an account indication. Embodiments include determining features of the historical support record based at least on differences between times of the time-stamped actions and the support initiation time. Embodiments include determining a label for the features based on the account indication. Embodiments include training an ensemble model, using training data comprising the features and the label, to determine an indication of an account in response to input features, wherein the ensemble model comprises a plurality of tree-based models and a ranking model.

Account prediction using machine learning

Growing community of inventors

Cupertino, CA, United States of America

Tomer Tal