Machine learning system for customer utterance intent prediction

Number of patents in Portfolio can not be more than 2000

United States of America

PATENT NO 12254871
APP PUB NO 20230215426A1
SERIAL NO

18183695

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Abstract

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A method of operating a customer utterance analysis system includes obtaining a subset of utterances from among a first set of utterances. The method includes encoding, by a sentence encoder, the subset of utterances into multi-dimensional vectors. The method includes generating reduced-dimensionality vectors by reducing a dimensionality of the multi-dimensional vectors. Each vector of the reduced-dimensionality vectors corresponds to an utterance from among the subset of utterances. The method includes performing clustering on the reduced-dimensionality vectors. The method includes, based on the clustering performed on the reduced-dimensionality vectors, arranging the subset of utterances into clusters. The method includes obtaining labels for a least two clusters from among the clusters. The method includes generating training data based on the obtained labels. The method includes training a neural network model to predict an intent of an utterance based on the training data.

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Patent Owner(s)

  • CHARLES SCHWAB & CO., INC.

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Inventor(s)

Inventor Name Address # of filed Patents Total Citations
Ahlstrom, Logan Sommers Ann Arbor, US 19 9
Filios, Stephen Canton, US 9 1
Nair, Abhilash Krishnankutty Ann Arbor, US 8 0
Narregudem, Dayanand Ann Arbor, US 9 3
Riassetto, Drew David Valley Park, US 2 0
Saberian, Nafiseh Ann Arbor, US 6 0
Sim, Amaris Yuseon Ann Arbor, US 2 0
Tappeta, Venkata Ravindra Reddy Novi, US 16 7

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