Intuit, Inc.

Divya Beeram

Neo Yuchen

Shanshan Tuo

Tomer Tal

Meng Chen

Nivethitha Kumar

Wan Yu Zhang

Kavita Sundar

Linxia Liao

Ngoc Nhung Ho

Christiana Mei Hui Chen

Valentin Vrzheshch

Nicholas R Johnson

Bor-Chau Juang

Pratyush Kumar Panda

Eyal Shafran

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

Certain aspects of the present disclosure provide techniques for predicting activity within a software application using a machine learning model. An example method generally includes generating a multidimensional time-series data set from time-series data associated with activity within a software application. The multidimensional time-series data set generally includes the time-series data organized based on a plurality of time granularities. Using a machine learning model and the generated multidimensional time-series data set, activity within the software application is predicted for one or more time granularities of the plurality of time granularities. Computing resources are allocated to execute operations using the software application based on the predicted activity within the software application.

Forecasting activity in software applications using machine learning models and multidimensional time-series data

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Newwark, CA, United States of America

Divya Beeram