Patents by Inventor Robert Kaplow
Robert Kaplow 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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Publication number: 20230351265Abstract: Methods, systems, and apparatus, including computer programs encoded on one or more computer storage devices, for training predictive models. Multiple training data records are received that each include an input data portion and an output data portion. A training data type is determined that corresponds to the training data. For example, a training data type can be determined by inputting the output data portions into one or more trained predictive classifiers. In other example, the training data type can be determined by comparison of the output data portions to data formats. Based on the determined training data type, a set of training functions are identified that are compatible with the training data of the determined training data type. The training data and the identified set of training functions are used to train multiple predictive models.Type: ApplicationFiled: July 6, 2023Publication date: November 2, 2023Inventors: Jordan M. Breckenridge, Travis H. K. Green, Robert Kaplow, Wei-Hao Lin, Gideon S. Mann
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Patent number: 11734609Abstract: Methods, systems, and apparatus, including computer programs encoded on one or more computer storage devices, for training predictive models. Multiple training data records are received that each include an input data portion and an output data portion. A training data type is determined that corresponds to the training data. For example, a training data type can be determined by inputting the output data portions into one or more trained predictive classifiers. In other example, the training data type can be determined by comparison of the output data portions to data formats. Based on the determined training data type, a set of training functions are identified that are compatible with the training data of the determined training data type. The training data and the identified set of training functions are used to train multiple predictive models.Type: GrantFiled: June 17, 2021Date of Patent: August 22, 2023Assignee: Google LLCInventors: Jordan M. Breckenridge, Travis H. K. Green, Robert Kaplow, Wei-Hao Lin, Gideon S. Mann
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Patent number: 11093860Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for obtaining a plurality of model representations of predictive models, each model representation associated with a respective user and expresses a respective predictive model, and selecting a model implementation for each of the model representations based on one or more system usage properties associated with the user associated with the corresponding model representation.Type: GrantFiled: December 3, 2018Date of Patent: August 17, 2021Assignee: Google LLCInventors: Wei-Hao Lin, Travis H. K. Green, Robert Kaplow, Gang Fu, Gideon S. Mann
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Patent number: 11042809Abstract: Methods, systems, and apparatus, including computer programs encoded on one or more computer storage devices, for training predictive models. Multiple training data records are received that each include an input data portion and an output data portion. A training data type is determined that corresponds to the training data. For example, a training data type can be determined by inputting the output data portions into one or more trained predictive classifiers. In other example, the training data type can be determined by comparison of the output data portions to data formats. Based on the determined training data type, a set of training functions are identified that are compatible with the training data of the determined training data type. The training data and the identified set of training functions are used to train multiple predictive models.Type: GrantFiled: May 16, 2016Date of Patent: June 22, 2021Assignee: Google LLCInventors: Jordan M. Breckenridge, Travis H. K. Green, Robert Kaplow, Wei-Hao Lin, Gideon S. Mann
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Patent number: 10515313Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a plurality of different types of predictive models using training data, wherein each of the predictive models implements a different machine learning technique. One or more weights are obtained wherein each weight is associated with an answer category in the plurality of examples. A weighted accuracy is calculated for each of the predictive models using the one or more weights.Type: GrantFiled: October 29, 2014Date of Patent: December 24, 2019Assignee: Google LLCInventors: Robert Kaplow, Wei-Hao Lin, Gideon S. Mann, Travis H. K. Green, Gang Fu, Robbie A. Haertel
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Patent number: 10504024Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for score normalization. One of the methods includes receiving initial training data, the initial training data comprising initial training records, each initial training record identifying input data as input and a category as output. The method includes generating a first trained predictive model using the initial training data and a training function. The method includes generating intermediate training records by inputting input data of the initial training records to a second trained predictive model, the second trained predictive model generated using the training function, each intermediate training record having a score. The method also includes generating a score normalization model using a score normalization training function and the intermediate training records.Type: GrantFiled: June 28, 2016Date of Patent: December 10, 2019Assignee: Google LLCInventors: Wei-Hao Lin, Travis H. K. Green, Robert Kaplow, Gang Fu, Gideon S. Mann
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Patent number: 10157343Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for obtaining a plurality of model representations of predictive models, each model representation associated with a respective user and expresses a respective predictive model, and selecting a model implementation for each of the model representations based on one or more system usage properties associated with the user associated with the corresponding model representation.Type: GrantFiled: June 16, 2015Date of Patent: December 18, 2018Assignee: Google LLCInventors: Wei-Hao Lin, Travis H. K. Green, Robert Kaplow, Gang Fu, Gideon S. Mann
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Publication number: 20160307099Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for score normalization. One of the methods includes receiving initial training data, the initial training data comprising initial training records, each initial training record identifying input data as input and a category as output. The method includes generating a first trained predictive model using the initial training data and a training function. The method includes generating intermediate training records by inputting input data of the initial training records to a second trained predictive model, the second trained predictive model generated using the training function, each intermediate training record having a score. The method also includes generating a score normalization model using a score normalization training function and the intermediate training records.Type: ApplicationFiled: June 28, 2016Publication date: October 20, 2016Inventors: Wei-Hao Lin, Travis H. K. Green, Robert Kaplow, Gang Fu, Gideon S. Mann
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Patent number: 9406019Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for score normalization. One of the methods includes receiving initial training data, the initial training data comprising initial training records, each initial training record identifying input data as input and a category as output. The method includes generating a first trained predictive model using the initial training data and a training function. The method includes generating intermediate training records by inputting input data of the initial training records to a second trained predictive model, the second trained predictive model generated using the training function, each intermediate training record having a score. The method also includes generating a score normalization model using a score normalization training function and the intermediate training records.Type: GrantFiled: February 1, 2013Date of Patent: August 2, 2016Assignee: Google Inc.Inventors: Wei-Hao Lin, Travis H. K. Green, Robert Kaplow, Gang Fu, Gideon S. Mann
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Patent number: 9342798Abstract: Methods, systems, and apparatus, including computer programs encoded on one or more computer storage devices, for training predictive models. Multiple training data records are received that each include an input data portion and an output data portion. A training data type is determined that corresponds to the training data. For example, a training data type can be determined by inputting the output data portions into one or more trained predictive classifiers. In other example, the training data type can be determined by comparison of the output data portions to data formats. Based on the determined training data type, a set of training functions are identified that are compatible with the training data of the determined training data type. The training data and the identified set of training functions are used to train multiple predictive models.Type: GrantFiled: June 4, 2014Date of Patent: May 17, 2016Assignee: Google Inc.Inventors: Jordan M. Breckenridge, Travis H. K. Green, Robert Kaplow, Wei-Hao Lin, Gideon S. Mann
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Patent number: 9239986Abstract: A system includes a computer(s) coupled to a data storage device(s) that stores a training data repository and a predictive model repository. The training data repository includes retained data samples from initial training data and from previously received data sets. The predictive model repository includes at least one updateable trained predictive model that was trained with the initial training data and retrained with the previously received data sets. A new data set is received. A richness score is assigned to each of the data samples in the set and to the retained data samples that indicates how information rich a data sample is for determining accuracy of the trained predictive model. A set of test data is selected based on ranking by richness score the retained data samples and the new data set. The trained predictive model is accuracy tested using the test data and an accuracy score determined.Type: GrantFiled: August 20, 2013Date of Patent: January 19, 2016Assignee: Google Inc.Inventors: Wei-Hao Lin, Travis H. K. Green, Robert Kaplow, Gang Fu, Gideon S. Mann
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Publication number: 20150186800Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a plurality of different types of predictive models using training data, wherein each of the predictive models implements a different machine learning technique. One or more weights are obtained wherein each weight is associated with an answer category in the plurality of examples. A weighted accuracy is calculated for each of the predictive models using the one or more weights.Type: ApplicationFiled: October 29, 2014Publication date: July 2, 2015Inventors: Robert Kaplow, Wei-Hao Lin, Gideon S. Mann, Travis H.K. Green, Gang Fu, Robbie A. Haertel
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Patent number: 9070089Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for obtaining a plurality of model representations of predictive models, each model representation associated with a respective user and expresses a respective predictive model, and selecting a model implementation for each of the model representations based on one or more system usage properties associated with the user associated with the corresponding model representation.Type: GrantFiled: October 7, 2013Date of Patent: June 30, 2015Assignee: Google Inc.Inventors: Wei-Hao Lin, Travis H. K. Green, Robert Kaplow, Gang Fu, Gideon S. Mann
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Publication number: 20150170048Abstract: A computer-implemented method includes receiving, in a system of one or more computers, training data for predictive modeling, the training data including a plurality of categories; determining, by the system, one or more attributes of the training data; identifying, by the system in a mapping of attributes to types of predictive models, a type of predictive model that is mapped to at least one of the one or more attributes; obtaining a utility function for the predictive model of the identified type, the utility function specifying importance of the plurality of categories relative to each other; and generating, based on the training data and the utility function, a predictive model of the identified type.Type: ApplicationFiled: August 12, 2011Publication date: June 18, 2015Inventors: Wei-Hao Lin, Travis H.K. Green, Robert Kaplow, Gang Fu, Gideon S. Mann
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Publication number: 20150170056Abstract: Methods, systems, and apparatus, including computer programs encoded on one or more computer storage devices, for training predictive models. Multiple training data records are received that each include an input data portion and an output data portion. A training data type is determined that corresponds to the training data. For example, a training data type can be determined by inputting the output data portions into one or more trained predictive classifiers. In other example, the training data type can be determined by comparison of the output data portions to data formats. Based on the determined training data type, a set of training functions are identified that are compatible with the training data of the determined training data type. The training data and the identified set of training functions are used to train multiple predictive models.Type: ApplicationFiled: June 4, 2014Publication date: June 18, 2015Applicant: Google Inc.Inventors: Jordan M. Breckenridge, Travis H. K. Green, Robert Kaplow, Wei-Hao Lin, Gideon S. Mann
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Patent number: 9020861Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for utilizing predictive models from an application scripting language.Type: GrantFiled: June 1, 2012Date of Patent: April 28, 2015Assignee: Google Inc.Inventors: Wei-Hao Lin, Travis H. K. Green, Robert Kaplow, Gang Fu, Gideon S. Mann
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Patent number: 8909564Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a plurality of different types of predictive models using training data, wherein each of the predictive models implements a different machine learning technique. One or more weights are obtained wherein each weight is associated with an answer category in the plurality of examples. A weighted accuracy is calculated for each of the predictive models using the one or more weights.Type: GrantFiled: September 1, 2011Date of Patent: December 9, 2014Assignee: Google Inc.Inventors: Robert Kaplow, Wei-Hao Lin, Gideon S. Mann, Travis H. K. Green, Gang Fu, Robbie A. Haertel
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Patent number: 8868472Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for receiving a plurality of first training examples, training a first predictive model using the first training examples, for each example in the first training examples, providing the first features of the example to the trained first predictive model to generate a respective first prediction, generating a second training example for each of the first training examples, wherein the second training example comprises the first features of the first training example and an answer that indicates whether the first answer of the first training example matches the respective first prediction of the first training example, training a second predictive model using the second training examples, and using the trained second predictive model to determine a confidence score for a prediction generated by the trained first predictive model.Type: GrantFiled: October 12, 2011Date of Patent: October 21, 2014Assignee: Google Inc.Inventors: Wei-Hao Lin, Travis H. K. Green, Robert Kaplow, Gang Fu, Gideon S. Mann
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Patent number: 8843427Abstract: In general, a method includes receiving a training data set that includes a plurality of examples, wherein each example includes one or more features and an answer, generating a plurality of modified training data sets by applying one or more filters to the training data set, each of the plurality of modified training data sets being based on a different combination of the one or more filters, training a plurality of predictive models, each of the plurality of predictive models being trained using a different modified training data set of the plurality of modified training data sets, determining a respective accuracy for each of the plurality of predictive models, identifying a most accurate predictive model based on the determined accuracies, and specifying an association between the training data set and the combination of filters used to generate the modified training data set that was used to train the most accurate predictive model.Type: GrantFiled: August 1, 2011Date of Patent: September 23, 2014Assignee: Google Inc.Inventors: Wei-Hao Lin, Travis H. K. Green, Robert Kaplow, Gang Fu, Gideon S. Mann
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Patent number: 8762299Abstract: Methods, systems, and apparatus, including computer programs encoded on one or more computer storage devices, for training predictive models. Multiple training data records are received that each include an input data portion and an output data portion. A training data type is determined that corresponds to the training data. For example, a training data type can be determined by inputting the output data portions into one or more trained predictive classifiers. In other example, the training data type can be determined by comparison of the output data portions to data formats. Based on the determined training data type, a set of training functions are identified that are compatible with the training data of the determined training data type. The training data and the identified set of training functions are used to train multiple predictive models.Type: GrantFiled: June 27, 2011Date of Patent: June 24, 2014Assignee: Google Inc.Inventors: Jordan M. Breckenridge, Travis H. K. Green, Robert Kaplow, Wei-Hao Lin, Gideon S. Mann