Intuit, Inc.

Wan Yu Zhang

Meng Chen

Zhewen Fan

Byungkyu Kang

Shanshan Tuo

Neo Yuchen

Nivethitha Kumar

Divya Beeram

Tomer Tal

Carlos A Oliveira

Kavita Sundar

Runhua Zhao

Farzaneh Khoshnevisan

Shivakumara Narayanaswamy

Kate Elizabeth Swift-Spong

Wenxin Xiao

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

A method implements calibrated risk scoring and sampling. Features are extracted from a record. A risk score, associated with the record, is generated from the features using a machine learning model. The record is mapped to a risk bucket using the risk score. The risk bucket may include multiple risk bucket records. The record is selected from the risk bucket records with a sampling threshold corresponding to the risk bucket. A form prepopulated with values from the record is presented to a client device.

Calibrated risk scoring and sampling

A processor may receive a call transcript including text and form a text string including at least a portion of the text. The processor may generate a situation description of the call transcript, which may comprise processing the text string using a transformer-based machine learning model. The processor may generate a trouble description of the call transcript, which may comprise creating a sentence embedding of the situation description, creating sentence embeddings for a plurality of utterances within the portion of the text, determining respective similarities between the sentence embedding of the situation description and each of the sentence embeddings for each respective one of the plurality of utterances, and selecting at least one of the plurality of utterances having at least one highest determined respective similarity as the trouble description. The processor may store a call summary comprising the situation description and the trouble description in a non-transitory memory.

Methods and systems for automatic call data generation

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Milpitas, CA, United States of America

Wan Yu Zhang