Patents by Inventor Julian P. Grady
Julian P. Grady has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Patent number: 11954597Abstract: 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.Type: GrantFiled: October 24, 2022Date of Patent: April 9, 2024Assignee: Google LLCInventors: Gregory S. Corrado, Kai Chen, Jeffrey A. Dean, Gary R. Holt, Julian P. Grady, Sharat Chikkerur, David W. Sculley, II
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Publication number: 20230325657Abstract: 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.Type: ApplicationFiled: October 24, 2022Publication date: October 12, 2023Inventors: Gregory S. Corrado, Kai Chen, Jeffrey A. Dean, Gary R. Holt, Julian P. Grady, Sharat Chikkerur, David W. Sculley, II
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Patent number: 11481631Abstract: 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.Type: GrantFiled: June 8, 2020Date of Patent: October 25, 2022Assignee: Google LLCInventors: Gregory S. Corrado, Kai Chen, Jeffrey A. Dean, Gary R. Holt, Julian P Grady, Sharat Chikkerur, David W. Sculley, II
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Patent number: 10679124Abstract: 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.Type: GrantFiled: December 2, 2016Date of Patent: June 9, 2020Assignee: Google LLCInventors: Gregory S. Corrado, Kai Chen, Jeffrey A. Dean, Gary R. Holt, Julian P. Grady, Sharat Chikkerur, David W. Sculley, II
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Patent number: 9514404Abstract: 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.Type: GrantFiled: September 21, 2015Date of Patent: December 6, 2016Assignee: Google Inc.Inventors: Gregory S. Corrado, Kai Chen, Jeffrey A. Dean, Gary R. Holt, Julian P. Grady, Sharat Chikkerur, David W. Sculley, II
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Patent number: 9141916Abstract: 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.Type: GrantFiled: March 14, 2013Date of Patent: September 22, 2015Assignee: Google Inc.Inventors: Gregory S. Corrado, Kai Chen, Jeffrey A. Dean, Gary R. Holt, Julian P. Grady, Sharat Chikkerur, David W. Sculley