Patents by Inventor Jeremiah Harmsen
Jeremiah Harmsen 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: 11900263Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for augmenting a neural network with additional operations.Type: GrantFiled: October 24, 2022Date of Patent: February 13, 2024Assignee: Google LLCInventors: Sherry Moore, Jeremiah Harmsen, Noah Fiedel
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Publication number: 20240037096Abstract: Systems and techniques are disclosed for generating entries for a searchable index based on rules generated by one or more machine-learned models. The index entries can include one or more tokens correlated with an outcome and an outcome probability. A subset of tokens can be identified based on the characteristics of an event. The index may be searched for outcomes and their respective probabilities that correspond to tokens that are similar to or match the subset of tokens based on the event.Type: ApplicationFiled: October 6, 2023Publication date: February 1, 2024Inventors: Jeremiah Harmsen, Tushar Deepak Chandra, Marcus Fontoura
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Patent number: 11782915Abstract: Systems and techniques are disclosed for generating entries for a searchable index based on rules generated by one or more machine-learned models. The index entries can include one or more tokens correlated with an outcome and an outcome probability. A subset of tokens can be identified based on the characteristics of an event. The index may be searched for outcomes and their respective probabilities that correspond to tokens that are similar to or match the subset of tokens based on the event.Type: GrantFiled: November 30, 2020Date of Patent: October 10, 2023Assignee: Google LLCInventors: Jeremiah Harmsen, Tushar Deepak Chandra, Marcus Fontoura
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Publication number: 20230119229Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for augmenting a neural network with additional operations.Type: ApplicationFiled: October 24, 2022Publication date: April 20, 2023Inventors: Sherry Moore, Jeremiah Harmsen, Noah Fiedel
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Patent number: 11481638Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for augmenting a neural network with additional operations.Type: GrantFiled: September 12, 2018Date of Patent: October 25, 2022Assignee: Google LLCInventors: Sherry Moore, Jeremiah Harmsen, Noah Fiedel
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Publication number: 20210149890Abstract: Systems and techniques are disclosed for generating entries for a searchable index based on rules generated by one or more machine-learned models. The index entries can include one or more tokens correlated with an outcome and an outcome probability. A subset of tokens can be identified based on the characteristics of an event. The index may be searched for outcomes and their respective probabilities that correspond to tokens that are similar to or match the subset of tokens based on the event.Type: ApplicationFiled: November 30, 2020Publication date: May 20, 2021Inventors: Jeremiah Harmsen, Tushar Deepak Chandra, Marcus Fontoura
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Patent number: 10853360Abstract: Systems and techniques are disclosed for generating entries for a searchable index based on rules generated by one or more machine-learned models. The index entries can include one or more tokens correlated with an outcome and an outcome probability. A subset of tokens can be identified based on the characteristics of an event. The index may be searched for outcomes and their respective probabilities that correspond to tokens that are similar to or match the subset of tokens based on the event.Type: GrantFiled: March 27, 2019Date of Patent: December 1, 2020Assignee: Google LLCInventors: Jeremiah Harmsen, Tushar Deepak Chandra, Marcus Fontoura
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Patent number: 10789544Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for batching inputs to machine learning models. One of the methods includes receiving a stream of requests, each request identifying a respective input for processing by a first machine learning model; adding the respective input from each request to a first queue of inputs for processing by the first machine learning model; determining, at a first time, that a count of inputs in the first queue as of the first time equals or exceeds a maximum batch size and, in response: generating a first batched input from the inputs in the queue as of the first time so that a count of inputs in the first batched input equals the maximum batch size, and providing the first batched input for processing by the first machine learning model.Type: GrantFiled: April 5, 2016Date of Patent: September 29, 2020Inventors: Noah Fiedel, Christopher Olston, Jeremiah Harmsen
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Patent number: 10762422Abstract: A system includes one or more computers and one or more storage devices storing instructions that when executed by the one or more computers cause the computers to implement a combined machine learning model for processing an input including multiple features to generate a predicted output for the machine learning input. The combined model includes: a deep machine learning model configured to process the features to generate a deep model output; a wide machine learning model configured to process the features to generate a wide model output; and a combining layer configured to process the deep model output generated by the deep machine learning model and the wide model output generated by the wide machine learning model to generate the predicted output, in which the deep model and the wide model have been trained jointly on training data to generate the deep model output and the wide model output.Type: GrantFiled: December 29, 2016Date of Patent: September 1, 2020Assignee: Google LLCInventors: Tal Shaked, Rohan Anil, Hrishikesh Balkrishna Aradhye, Mustafa Ispir, Glen Anderson, Wei Chai, Mehmet Levent Koc, Jeremiah Harmsen, Xiaobing Liu, Gregory Sean Corrado, Tushar Deepak Chandra, Heng-Tze Cheng
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Publication number: 20200210851Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for augmenting a neural network with additional operations.Type: ApplicationFiled: September 12, 2018Publication date: July 2, 2020Inventors: Sherry Moore, Jeremiah Harmsen, Noah Fiedel
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Publication number: 20190220460Abstract: Systems and techniques are disclosed for generating entries for a searchable index based on rules generated by one or more machine-learned models. The index entries can include one or more tokens correlated with an outcome and an outcome probability. A subset of tokens can be identified based on the characteristics of an event. The index may be searched for outcomes and their respective probabilities that correspond to tokens that are similar to or match the subset of tokens based on the event.Type: ApplicationFiled: March 27, 2019Publication date: July 18, 2019Inventors: Jeremiah Harmsen, Tushar Deepak Chandra, Marcus Fontoura
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Patent number: 10255319Abstract: Systems and techniques are disclosed for generating entries for a searchable index based on rules generated by one or more machine-learned models. The index entries can include one or more tokens correlated with an outcome and an outcome probability. A subset of tokens can be identified based on the characteristics of an event. The index may be searched for outcomes and their respective probabilities that correspond to tokens that are similar to or match the subset of tokens based on the event.Type: GrantFiled: May 2, 2014Date of Patent: April 9, 2019Assignee: Google LLCInventors: Jeremiah Harmsen, Tushar Deepak Chandra, Marcus Fontoura
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Patent number: 10198744Abstract: Structured data sets including one or more attributes are identified, each structured data set associated with, for example, a particular user. Values corresponding the at least one of the one or more attributes in each structured data set are identified. A condition established by a content provider, for instance, an advertiser, is compared to the identified values to determine if the condition is satisfied. When the condition is satisfied, one or more content items are identified to the users associated with the structured data sets containing identified values satisfying the condition.Type: GrantFiled: September 14, 2012Date of Patent: February 5, 2019Assignee: Google LLCInventors: Mayur Datar, Jason C. Miller, Michael Hochberg, Bahman Rabii, Megan Nance, Julie Tung, Jeremiah Harmsen, Tomasz J. Tunguz-Zawislak, Andres S. Perez-Bergquist
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Patent number: 10102482Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a factorization model to learning features of model inputs of a trained model such that the factorization model is predictive of outcome for which the machine learned model is trained.Type: GrantFiled: August 7, 2015Date of Patent: October 16, 2018Assignee: Google LLCInventors: Heng-Tze Cheng, Jeremiah Harmsen, Alexandre Tachard Passos, David Edgar Lluncor, Shahar Jamshy, Tal Shaked, Tushar Deepak Chandra
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Publication number: 20170300814Abstract: A system includes one or more computers and one or more storage devices storing instructions that when executed by the one or more computers cause the computers to implement a combined machine learning model for processing an input including multiple features to generate a predicted output for the machine learning input. The combined model includes: a deep machine learning model configured to process the features to generate a deep model output; a wide machine learning model configured to process the features to generate a wide model output; and a combining layer configured to process the deep model output generated by the deep machine learning model and the wide model output generated by the wide machine learning model to generate the predicted output, in which the deep model and the wide model have been trained jointly on training data to generate the deep model output and the wide model output.Type: ApplicationFiled: December 29, 2016Publication date: October 19, 2017Inventors: Tal Shaked, Rohan Anil, Hrishikesh Balkrishna Aradhye, Mustafa Ispir, Glen Anderson, Wei Chai, Mehmet Levent Koc, Jeremiah Harmsen, Xiaobing Liu, Gregory Sean Corrado, Tushar Deepak Chandra, Heng-Tze Cheng
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Publication number: 20170286864Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for batching inputs to machine learning models. One of the methods includes receiving a stream of requests, each request identifying a respective input for processing by a first machine learning model; adding the respective input from each request to a first queue of inputs for processing by the first machine learning model; determining, at a first time, that a count of inputs in the first queue as of the first time equals or exceeds a maximum batch size and, in response: generating a first batched input from the inputs in the queue as of the first time so that a count of inputs in the first batched input equals the maximum batch size, and providing the first batched input for processing by the first machine learning model.Type: ApplicationFiled: April 5, 2016Publication date: October 5, 2017Inventors: Noah Fiedel, Christopher Olston, Jeremiah Harmsen
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Publication number: 20170213252Abstract: The subject matter of this document generally relates to reducing noise in aggregated data using frequency analysis. In some implementations, a system for reducing data noise using frequency analysis includes a data storage device that stores content and a network association processor in data communication with the data storage device. The network association processor aggregates, for a given group, content of one or more additional groups that each have overlapping members with the given group. The network association processor reduces noise in the aggregated content of the one or more additional groups using frequency analysis by determining, for each portion of content in the aggregated content, a frequency of occurrence of the portion of content within the aggregated content and filtering, from the aggregated content, each portion of content that has a frequency of occurrence that is less than a threshold.Type: ApplicationFiled: February 13, 2017Publication date: July 27, 2017Inventors: Terrence Rohan, Tomasz J. Tunguz-Zawislak, Jeremiah Harmsen, Sverre Sundsdal, Thomas M. Annau, Megan Nance, Mayur Dhondu Datar, Julie Tung, Bahman Rabii, Jason C. Miller, Michael Hochberg, Andres S. Perez-Bergquist
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Publication number: 20170039483Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a factorization model to learning features of model inputs of a trained model such that the factorization model is predictive of outcome for which the machine learned model is trained.Type: ApplicationFiled: August 7, 2015Publication date: February 9, 2017Inventors: Heng-Tze Cheng, Jeremiah Harmsen, Alexandre Tachard Passos, David Edgar Lluncor, Shahar Jamshy, Tal Shaked, Tushar Deepak Chandra
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Patent number: 9390382Abstract: Systems and techniques are disclosed for training a machine learning model based on one or more regularization penalties associated with one or more features. A template having a lower regularization penalty may be given preference over a template having a higher regularization penalty. A regularization penalty may be determined based on domain knowledge. A restrictive regularization penalty may be assigned to a template based on determining that a template occurrence is below a stability threshold and may be modified if the template occurrence meets or exceeds the stability threshold.Type: GrantFiled: December 30, 2013Date of Patent: July 12, 2016Assignee: Google Inc.Inventors: Yoram Singer, Tal Shaked, Tushar Deepak Chandra, Tze Way Eugene Ie, James Vincent McFadden, Jeremiah Harmsen, Kristen Riedt LeFevre
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Publication number: 20150317357Abstract: Systems and techniques are disclosed for generating entries for a searchable index based on rules generated by one or more machine-learned models. The index entries can include one or more tokens correlated with an outcome and an outcome probability. A subset of tokens can be identified based on the characteristics of an event. The index may be searched for outcomes and their respective probabilities that correspond to tokens that are similar to or match the subset of tokens based on the event.Type: ApplicationFiled: May 2, 2014Publication date: November 5, 2015Applicant: Google Inc.Inventors: Jeremiah Harmsen, Tushar Deepak Chandra, Marcus Fontoura