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公开(公告)号:US12299551B2
公开(公告)日:2025-05-13
申请号:US16927655
申请日:2020-07-13
Applicant: INTUIT INC.
Inventor: Shanshan Tuo , Neo Yuchen , Divya Beeram , Valentin Vrzheshch , Tomer Tal , Ngoc Nhung Ho
IPC: G06N20/20 , G06F16/9535 , G06F18/214 , G06N3/049
Abstract: 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.
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公开(公告)号:US11929078B2
公开(公告)日:2024-03-12
申请号:US17183006
申请日:2021-02-23
Applicant: INTUIT INC.
Inventor: Shanshan Tuo , Divya Beeram , Meng Chen , Neo Yuchen , Wan Yu Zhang , Nivethitha Kumar , Kavita Sundar , Tomer Tal
Abstract: 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.
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