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US Patent 11954597 Using embedding functions with a deep network

Patent 11954597 was granted and assigned to Google on April, 2024 by the United States Patent and Trademark Office.

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Contents

Is a
Patent
Patent
0

Patent attributes

Patent Applicant
Google
Google
0
Current Assignee
Google
Google
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Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
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Patent Number
119545970
Patent Inventor Names
David W. Sculley, II0
Kai Chen0
Julian P. Grady0
Jeffrey A. Dean0
Gregory S. Corrado0
Gary R. Holt0
Sharat Chikkerur0
Date of Patent
April 9, 2024
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Patent Application Number
179724660
Date Filed
October 24, 2022
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Patent Citations
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US Patent 7689006 Biometric convolution using multiple biometrics
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US Patent 7428524 Large scale data storage in sparse tables
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US Patent 7548928 Data compression of large scale data stored in sparse tables
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US Patent 7567973 Storing a sparse table using locality groups
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US Patent 7624074 Methods for feature selection in a learning machine
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US Patent 7661128 Secure login credentials for substantially anonymous users
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US Patent 8234228 Method for training a learning machine having a deep multi-layered network with labeled and unlabeled training data
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US Patent 8484351 Associating application-specific methods with tables used for data storage
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Patent Primary Examiner
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Kamran Afshar
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CPC Code
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G06F 2207/483
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G06F 15/00
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G06F 7/483
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G06F 15/76
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G06K 9/6255
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G06K 9/6268
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G06F 17/16
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G06N 99/00
0
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Patent abstract

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using embedded function with a deep network. One of the methods includes receiving an input comprising a plurality of features, wherein each of the features is of a different feature type; processing each of the features using a respective embedding function to generate one or more numeric values, wherein each of the embedding functions operates independently of each other embedding function, and wherein each of the embedding functions is used for features of a respective feature type; processing the numeric values using a deep network to generate a first alternative representation of the input, wherein the deep network is a machine learning model composed of a plurality of levels of non-linear operations; and processing the first alternative representation of the input using a logistic regression classifier to predict a label for the input.

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