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公开(公告)号:US20220198367A1
公开(公告)日:2022-06-23
申请号:US17189812
申请日:2021-03-02
Applicant: INTUIT INC.
Inventor: Quang Nguyen , Divya Beeram , Yunqi Li , Steven James Brown , Neo Yuchen
Abstract: Aspects of the present disclosure provide techniques for expert matching though workload intelligence. Embodiments include receiving a request for a support engagement. Embodiments include receiving workload data of a plurality of experts. Embodiments include determining a workload capacity of each respective expert based on the respective workload data for the respective expert. Embodiments include determining a respective estimated completion time for the support engagement for each of the plurality of experts using a machine learning model. Embodiments include determining match scores for the support engagement and each of the plurality of experts based on the estimated completion times and the workload capacities. Embodiments include selecting a given expert of the plurality of experts to handle the support engagement based on the match scores.
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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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公开(公告)号:US11563846B1
公开(公告)日:2023-01-24
申请号:US17829111
申请日:2022-05-31
Applicant: Intuit Inc.
Inventor: Andrew Mattarella-Micke , Neo Yuchen , Xiaoyu Zeng , Manisha Panta
Abstract: A method including receiving an incoming call from a calling device of a caller and determining identification information for the calling device. The method also includes receiving voice audio data of the caller from the calling device, converting the voice audio data to caller phones, and identifying a customer account associated with the identification information. The method further includes obtaining user phones for multiple candidate users associated with the identified customer account, comparing the caller phones to the user phones for the multiple candidate users, and determining the identity of the caller based on the comparison.
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公开(公告)号:US11429834B1
公开(公告)日:2022-08-30
申请号:US16125212
申请日:2018-09-07
Applicant: INTUIT INC.
Inventor: Zijun Xue , Jessica Ting-Yu Ko , Neo Yuchen , Ming-Kuang Daniel Wu , Chucheng Hsieh
IPC: G06N3/00 , G06N3/08 , G06N3/04 , G06F40/30 , G06F40/284
Abstract: Certain aspects of the present disclosure provide techniques for providing automated intelligence in a support session. In one example, a method includes generating a set of tokens based on a text-based query posted by a support agent to a live chat thread; generating a set of vectors based on the set of tokens; extracting a set of features based on the set of tokens; generating a query vector based on the set of vectors and the set of features; determining a predicted intent of the text-based query based on the query vector, wherein the predicted intent is one of a plurality of predefined intents; determining a predicted answer to the text-based query based on: the query vector; and the predicted intent; and providing the predicted answer to the text-based query in the live chat thread.
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