Patents by Inventor Jordan M. Breckenridge
Jordan M. Breckenridge 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: 11282004Abstract: A global-level manager access a work order from a client and parameters associated with the work order. A service level agreement to meet the work order parameters is determined. The service level agreement includes a price. An indication is received from the client that the service level agreement is accepted. The one or more input files are partitioned into multiple shards, and the work order into multiple jobs. The jobs are distributed among a plurality of clusters to be processed using underutilized computing resources in the clusters. The job outputs are combined to form the work order output. The jobs are monitored to insure that the deadline for completion of the work order will be met.Type: GrantFiled: December 21, 2018Date of Patent: March 22, 2022Assignee: Google LLCInventors: David Konerding, Jordan M. Breckenridge, Daniel Belov
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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: 10169728Abstract: A global-level manager access a work order from a client and parameters associated with the work order. A service level agreement to meet the work order parameters is determined. The service level agreement includes a price. An indication is received from the client that the service level agreement is accepted. The one or more input files are partitioned into multiple shards, and the work order into multiple jobs. The jobs are distributed among a plurality of clusters to be processed using underutilized computing resources in the clusters. The job outputs are combined to form the work order output. The jobs are monitored to insure that the deadline for completion of the work order will be met.Type: GrantFiled: November 28, 2016Date of Patent: January 1, 2019Assignee: Google LLCInventors: David Konerding, Jordan M. Breckenridge, Daniel Belov
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Patent number: 9535765Abstract: A global-level manager access a work order from a client and parameters associated with the work order. A service level agreement to meet the work order parameters is determined. The service level agreement includes a price. An indication is received from the client that the service level agreement is accepted. The one or more input files are partitioned into multiple shards, and the work order into multiple jobs. The jobs are distributed among a plurality of clusters to be processed using underutilized computing resources in the clusters. The job outputs are combined to form the work order output. The jobs are monitored to insure that the deadline for completion of the work order will be met.Type: GrantFiled: March 28, 2012Date of Patent: January 3, 2017Assignee: Google Inc.Inventors: David Konerding, Jordan M. Breckenridge, Daniel Belov
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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: 9218217Abstract: A global-level manager accesses a work order referencing at least one executable file and one or more input files. The one or more input files are partitioned into multiple shards. The work order is partitioned into multiple jobs. The jobs are distributed among a plurality of to be processed by a task level manager at each of the plurality of clusters. The executable file is loaded into the native client environment through a validator. The validator is configured to insure that the executable file does not include one or more of a defined set of instructions, does not call instructions outside of the executable file, and does not read or write data outside of a data region associated with the executable file.Type: GrantFiled: March 28, 2012Date of Patent: December 22, 2015Assignee: Google Inc.Inventors: David Konerding, Jordan M. Breckenridge, Daniel Belov
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Patent number: 9189747Abstract: Methods, systems, and apparatus, including computer programs encoded on one or more computer storage devices, for training a predictive model. In one aspect, a method includes receiving over a network predictive modeling training data from a client computing system. The training data and multiple training functions obtained from a repository of training functions are used to train multiple predictive models. A score is generated for each of the trained predictive models, where each score represents an estimation of the effectiveness of the respective trained predictive model. A first trained predictive model is selected from among the trained predictive models based on the generated scores. Access to the first trained predictive model is provided over the network.Type: GrantFiled: July 20, 2012Date of Patent: November 17, 2015Assignee: Google Inc.Inventors: Gideon S. Mann, Jordan M. Breckenridge, Wei-Hao Lin
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Publication number: 20150170049Abstract: Methods, systems, and apparatus, including computer programs encoded on one or more computer storage devices, for training a predictive model. In one aspect, a method includes receiving over a network predictive modeling training data from a client computing system. The training data and multiple training functions obtained from a repository of training functions are used to train multiple predictive models. A score is generated for each of the trained predictive models, where each score represents an estimation of the effectiveness of the respective trained predictive model. A first trained predictive model is selected from among the trained predictive models based on the generated scores. Access to the first trained predictive model is provided over the network.Type: ApplicationFiled: July 20, 2012Publication date: June 18, 2015Inventors: Gideon S. Mann, Jordan M. Breckenridge, Wei-Hao Lin
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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: 8983960Abstract: A global-level manager receives a work order referencing at least one executable file and one or more input files. The one or more input files include multiple input resources. A first type of input resource is identified in the one or more input files and a second type of input resource in the one or more input files. The first type of input resource is split into a plurality of first-type input shards. The second type of input resource is split into a plurality of second-type input shards. The plurality of second-type input shards are associated with each of the first-type input shards. For each of the first-type input shards, the global-level manager distributes the first-type input shards, the associated second-type input shards, and the executable file to a single.Type: GrantFiled: March 28, 2012Date of Patent: March 17, 2015Assignee: Google Inc.Inventors: David Konerding, Jordan M. Breckenridge, Daniel Belov
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Patent number: 8935318Abstract: A central storage configured to store one or more input files and an executable file. A work order frontend is configured to receive, from the client, a representational state transfer (RESTful) message that contains a reference to the one or more input files in the central storage. The work order frontend is further configured to transmit, to a global-level manager, a work order. The global-level manager is configured to access the work order. The global-level manager is further configured to partition the one or more input files into multiple shards, the work order into multiple jobs, each job being associated with one or more of the multiple shards and the executable file. The global-level manager is further configured to distribute the jobs among a plurality of clusters.Type: GrantFiled: March 28, 2012Date of Patent: January 13, 2015Assignee: Google Inc.Inventors: David Konerding, Jordan M. Breckenridge, Daniel Belov
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Patent number: 8909568Abstract: Methods, systems, and apparatus, including computer programs encoded on one or more computer storage devices, for training a predictive model. In one aspect, a method includes receiving over a network predictive modeling training data from a client computing system. The training data and multiple training functions obtained from a repository of training functions are used to train multiple predictive models. A score is generated for each of the trained predictive models, where each score represents an estimation of the effectiveness of the respective trained predictive model. A first trained predictive model is selected from among the trained predictive models based on the generated scores. Access to the first trained predictive model is provided to the client computing system.Type: GrantFiled: March 4, 2014Date of Patent: December 9, 2014Assignee: Google Inc.Inventors: Gideon S. Mann, Jordan M. Breckenridge, Wei-Hao Lin
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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
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Patent number: 8706659Abstract: Methods, systems, and apparatus, including computer programs encoded on one or more computer storage devices, for training a predictive model. In one aspect, a method includes receiving over a network predictive modeling training data from a client computing system. The training data and multiple training functions obtained from a repository of training functions are used to train multiple predictive models. A score is generated for each of the trained predictive models, where each score represents an estimation of the effectiveness of the respective trained predictive model. A first trained predictive model is selected from among the trained predictive models based on the generated scores. Access to the first trained predictive model is provided to the client computing system.Type: GrantFiled: May 3, 2013Date of Patent: April 22, 2014Assignee: Google Inc.Inventors: Gideon S. Mann, Jordan M. Breckenridge, Wei-Hao Lin
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Publication number: 20140046880Abstract: Methods, systems, and apparatus, including computer programs encoded on one or more computer storage devices, for training and retraining predictive models. A series of training data sets are received and added to a training data queue. In response to a first condition being satisfied, multiple retrained predictive models are generated using the training data queue, multiple updateable trained predictive models obtained from a repository of trained predictive models, and multiple training functions. In response to a second condition being satisfied, multiple new trained predictive models are generated using the training data queue, at least some training data stored in a training data repository and training functions. The new trained predictive models include static trained predictive models and updateable trained predictive models. The repository of trained predictive models is updated with at least some of the retrained predictive models and new trained predictive models.Type: ApplicationFiled: October 23, 2013Publication date: February 13, 2014Applicant: Google Inc.Inventors: Jordan M. Breckenridge, Travis H.K. Green, Robert Kaplow, Wei-Hao Lin, Gideon S. Mann
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Patent number: 8595154Abstract: Methods, systems, and apparatus, including computer programs encoded on one or more computer storage devices, for training and retraining predictive models. A series of training data sets are received and added to a training data queue. In response to a first condition being satisfied, multiple retrained predictive models are generated using the training data queue, multiple updateable trained predictive models obtained from a repository of trained predictive models, and multiple training functions. In response to a second condition being satisfied, multiple new trained predictive models are generated using the training data queue, at least some training data stored in a training data repository and training functions. The new trained predictive models include static trained predictive models and updateable trained predictive models. The repository of trained predictive models is updated with at least some of the retrained predictive models and new trained predictive models.Type: GrantFiled: January 26, 2011Date of Patent: November 26, 2013Assignee: Google Inc.Inventors: Jordan M. Breckenridge, Travis Green, Robert Kaplow, Wei-Hao Lin, Gideon S. Mann
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Patent number: 8533222Abstract: Methods, systems, and apparatus, including computer programs encoded on one or more computer storage devices, for training and retraining predictive models. A series of training data sets for predictive modeling can be received, e.g., over a network from a client computing system. The training data included in the training data sets is different from initial training data that was used with multiple training functions to train multiple trained predictive models stored in a predictive model repository. The series of training data sets are used with multiple trained updateable predictive models obtained from the predictive model repository and multiple training functions to generate multiple retrained predictive models. An effectiveness score is generated for each of the retrained predictive models. A first trained predictive model is selected from among the trained predictive models included in the predictive model repository and the retrained predictive models based on their respective effectiveness scores.Type: GrantFiled: January 26, 2011Date of Patent: September 10, 2013Assignee: Google Inc.Inventors: Jordan M. Breckenridge, Travis Green, Robert Kaplow, Wei-Hao Lin, Gideon S. Mann
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Patent number: 8489632Abstract: Methods, systems, and apparatus, including computer programs encoded on one or more computer storage devices, for receiving training data for predictive modeling and executing multiple processes simultaneously to generate multiple trained predictive models using the training data and training functions. After executing the processes for an initial runtime, a convergence status of each process is determined that indicates a likelihood that the training function being executed will converge on the training data. Based on the determination, training functions are identified that are not likely to converge and processes that are executing these training functions are terminated. After an ultimate runtime has expired, processes that are still executing training functions that have not yet converged are terminated. An effectiveness score is generated for each of the trained predictive models that were successfully generated and a trained predictive model is selected based on the effectiveness scores.Type: GrantFiled: June 28, 2011Date of Patent: July 16, 2013Assignee: Google Inc.Inventors: Jordan M. Breckenridge, Travis H. K. Green, Wei-Hao Lin, Gideon S. Mann