Patents by Inventor Daniel Ramage

Daniel Ramage 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).

  • Publication number: 20240012540
    Abstract: The present disclosure is directed to input suggestion. In particular, the methods and systems of the present disclosure can: receive, from a first application executed by one or more computing devices, data indicating information that has been presented by and/or input into the first application; generate, based at least in part on the received data, one or more suggested candidate inputs for a second application executed by the computing device(s); provide, in association with the second application, an interface comprising one or more options to select at least one suggested candidate input of the suggested candidate input(s); and responsive to receiving data indicating a selection of a particular suggested candidate input of the suggested candidate input(s) via the interface, communicate, to the second application, data indicating the particular suggested candidate input.
    Type: Application
    Filed: September 21, 2023
    Publication date: January 11, 2024
    Inventors: Tim Wantland, Julian Odell, Seungyeon Kim, Iulia Turc, Daniel Ramage, Wei Huang, Kaikai Wang
  • Patent number: 11803290
    Abstract: The present disclosure is directed to input suggestion. In particular, the methods and systems of the present disclosure can: receive, from a first application executed by one or more computing devices, data indicating information that has been presented by and/or input into the first application; generate, based at least in part on the received data, one or more suggested candidate inputs for a second application executed by the computing device(s); provide, in association with the second application, an interface comprising one or more options to select at least one suggested candidate input of the suggested candidate input(s); and responsive to receiving data indicating a selection of a particular suggested candidate input of the suggested candidate input(s) via the interface, communicate, to the second application, data indicating the particular suggested candidate input.
    Type: Grant
    Filed: January 25, 2021
    Date of Patent: October 31, 2023
    Assignee: GOOGLE LLC
    Inventors: Tim Wantland, Julian Odell, Seungyeon Kim, Iulia Turc, Daniel Ramage, Wei Huang, Kaikai Wang
  • Patent number: 11726769
    Abstract: Systems and methods for learning differentially private machine-learned models are provided. A computing system can include one or more server computing devices comprising one or more processors and one or more non-transitory computer-readable media that collectively store instructions that, when executed by the one or more processors cause the one or more server computing devices to perform operations. The operations can include selecting a subset of client computing devices from a pool of available client computing devices; providing a machine-learned model to the selected client computing devices; receiving, from each selected client computing device, a local update for the machine-learned model; determining a differentially private aggregate of the local updates; and determining an updated machine-learned model based at least in part on the data-weighted average of the local updates.
    Type: Grant
    Filed: October 12, 2022
    Date of Patent: August 15, 2023
    Assignee: GOOGLE LLC
    Inventors: Hugh Brendan McMahan, Kunal Talwar, Li Zhang, Daniel Ramage
  • Publication number: 20230222551
    Abstract: Systems and methods are shown for providing private local sponsored content selection and improving intelligence models through distribution among mobile devices. This allows greater data gathering capabilities through the use of the sensors of the mobile devices as well as data stored on data storage components of the mobile devices to create predicted models while offering better opportunities to preserve privacy. Locally stored profiles comprising machine intelligence models may also be used to determine the relevance of the data gathered and in improving an aggregated model for identifying the relevance of data and the selection of sponsored content items. Distributed optimization is used in conjunction with privacy techniques to create the improved machine intelligence models. Publishers may also benefit from the improved privacy by protecting the statistics of type or volume of sponsored content items shown with publisher content.
    Type: Application
    Filed: March 21, 2023
    Publication date: July 13, 2023
    Inventors: Keith Bonawitz, Daniel Ramage, David Petrou
  • Publication number: 20230222542
    Abstract: The present disclosure provides systems and methods for content quasi-personalization or anonymized content retrieval via aggregated browsing history of a large plurality of devices, such as millions or billions of devices. A sparse matrix may be constructed from the aggregated browsing history, and dimensionally reduced, reducing entropy and providing anonymity for individual devices. Relevant content may be selected via quasi-personalized clusters representing similar browsing histories, without exposing individual device details to content providers.
    Type: Application
    Filed: August 22, 2022
    Publication date: July 13, 2023
    Inventors: Michael Kleber, Gang Wang, Daniel Ramage, Charlie Harrison, Josh Karlin, Moti Yung
  • Patent number: 11610231
    Abstract: Systems and methods are shown for providing private local sponsored content selection and improving intelligence models through distribution among mobile devices. This allows greater data gathering capabilities through the use of the sensors of the mobile devices as well as data stored on data storage components of the mobile devices to create predicted models while offering better opportunities to preserve privacy. Locally stored profiles comprising machine intelligence models may also be used to determine the relevance of the data gathered and in improving an aggregated model for identifying the relevance of data and the selection of sponsored content items. Distributed optimization is used in conjunction with privacy techniques to create the improved machine intelligence models. Publishers may also benefit from the improved privacy by protecting the statistics of type or volume of sponsored content items shown with publisher content.
    Type: Grant
    Filed: July 29, 2021
    Date of Patent: March 21, 2023
    Assignee: GOOGLE LLC
    Inventors: Keith Bonawitz, Daniel Ramage, David Petrou
  • Publication number: 20230066545
    Abstract: Systems and methods for learning differentially private machine-learned models are provided. A computing system can include one or more server computing devices comprising one or more processors and one or more non-transitory computer-readable media that collectively store instructions that, when executed by the one or more processors cause the one or more server computing devices to perform operations. The operations can include selecting a subset of client computing devices from a pool of available client computing devices; providing a machine-learned model to the selected client computing devices; receiving, from each selected client computing device, a local update for the machine-learned model; determining a differentially private aggregate of the local updates; and determining an updated machine-learned model based at least in part on the data-weighted average of the local updates.
    Type: Application
    Filed: October 12, 2022
    Publication date: March 2, 2023
    Inventors: Hugh Brendan McMahan, Kunal Talwar, Li Zhang, Daniel Ramage
  • Patent number: 11551153
    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for obtaining a global model for a particular activity, the global model derived based on input data representing multiple observations associated with the particular activity performed by a collection of users; determining, using the global model, expected data representing an expected observation associated with the particular activity performed by a particular user; receiving, by a computing device operated by the particular user, particular data representing an actual observation associated with the particular activity performed by the particular user; determining, by the computing device and using (i) the expected data and (ii) the particular data, residual data of the particular user; and deriving a local model of the particular user based on the residual data.
    Type: Grant
    Filed: October 28, 2020
    Date of Patent: January 10, 2023
    Assignee: Google LLC
    Inventors: Daniel Ramage, Jeremy Gillmor Kahn
  • Publication number: 20220391947
    Abstract: The present disclosure provides systems and methods for content quasi-personalization or anonymized content retrieval via aggregated browsing history of a large plurality of devices, such as millions or billions of devices. A sparse matrix may be constructed from the aggregated browsing history, and dimensionally reduced, reducing entropy and providing anonymity for individual devices. Relevant content may be selected via quasi-personalized clusters representing similar browsing histories, without exposing individual device details to content providers.
    Type: Application
    Filed: August 22, 2022
    Publication date: December 8, 2022
    Inventors: Michael Kleber, Gang Wang, Daniel Ramage, Charles Harrison, Josh Karlin, Moti Yung
  • Patent number: 11502975
    Abstract: A messaging application may automatically analyze content of one or more messages and/or user information to automatically provide suggestions to a user within a messaging application. The suggestions may automatically incorporate particular non-messaging functionality into the messaging application. The automatic suggestions may suggest one or more appropriate responses to be selected by a user to respond in the messaging application, and/or may automatically send one or more appropriate responses on behalf of a user.
    Type: Grant
    Filed: August 21, 2020
    Date of Patent: November 15, 2022
    Assignee: Google LLC
    Inventors: Ori Gershony, Sergey Nazarov, Rodrigo De Castro, Erika Palmer, Daniel Ramage, Adam Rodriguez, Andrei Pascovici
  • Publication number: 20220358385
    Abstract: The present disclosure provides systems and methods for on-device machine learning. In particular, the present disclosure is directed to an on-device machine learning platform and associated techniques that enable on-device prediction, training, example collection, and/or other machine learning tasks or functionality. The on-device machine learning platform can include a context provider that securely injects context features into collected training examples and/or client-provided input data used to generate predictions/inferences. Thus, the on-device machine learning platform can enable centralized training example collection, model training, and usage of machine-learned models as a service to applications or other clients.
    Type: Application
    Filed: July 27, 2022
    Publication date: November 10, 2022
    Inventors: Pannag Sanketi, Wolfgang Grieskamp, Daniel Ramage, Hrishikesh Aradhye
  • Patent number: 11475350
    Abstract: Systems and methods for learning differentially private machine-learned models are provided. A computing system can include one or more server computing devices comprising one or more processors and one or more non-transitory computer-readable media that collectively store instructions that, when executed by the one or more processors cause the one or more server computing devices to perform operations. The operations can include selecting a subset of client computing devices from a pool of available client computing devices; providing a machine-learned model to the selected client computing devices; receiving, from each selected client computing device, a local update for the machine-learned model; determining a differentially private aggregate of the local updates; and determining an updated machine-learned model based at least in part on the data-weighted average of the local updates.
    Type: Grant
    Filed: January 22, 2018
    Date of Patent: October 18, 2022
    Assignee: GOOGLE LLC
    Inventors: Hugh Brendan McMahan, Kunal Talwar, Li Zhang, Daniel Ramage
  • Patent number: 11423441
    Abstract: The present disclosure provides systems and methods for content quasi-personalization or anonymized content retrieval via aggregated browsing history of a large plurality of devices, such as millions or billions of devices. A sparse matrix may be constructed from the aggregated browsing history, and dimensionally reduced, reducing entropy and providing anonymity for individual devices. Relevant content may be selected via quasi-personalized clusters representing similar browsing histories, without exposing individual device details to content providers.
    Type: Grant
    Filed: November 27, 2019
    Date of Patent: August 23, 2022
    Assignee: Google LLC
    Inventors: Michael Kleber, Gang Wang, Daniel Ramage, Charlie Harrison, Josh Karlin, Moti Yung
  • Patent number: 11403540
    Abstract: The present disclosure provides systems and methods for on-device machine learning. In particular, the present disclosure is directed to an on-device machine learning platform and associated techniques that enable on-device prediction, training, example collection, and/or other machine learning tasks or functionality. The on-device machine learning platform can include a context provider that securely injects context features into collected training examples and/or client-provided input data used to generate predictions/inferences. Thus, the on-device machine learning platform can enable centralized training example collection, model training, and usage of machine-learned models as a service to applications or other clients.
    Type: Grant
    Filed: August 11, 2017
    Date of Patent: August 2, 2022
    Assignee: GOOGLE LLC
    Inventors: Pannag Sanketi, Wolfgang Grieskamp, Daniel Ramage, Hrishikesh Aradhye
  • Publication number: 20220004929
    Abstract: The present disclosure provides systems and methods for on-device machine learning. In particular, the present disclosure is directed to an on-device machine learning platform and associated techniques that enable on-device prediction, training, example collection, and/or other machine learning tasks or functionality. The on-device machine learning platform can include a context provider that securely injects context features into collected training examples and/or client-provided input data used to generate predictions/inferences. Thus, the on-device machine learning platform can enable centralized training example collection, model training, and usage of machine-learned models as a service to applications or other clients.
    Type: Application
    Filed: September 20, 2021
    Publication date: January 6, 2022
    Inventors: Pannag Sanketi, Wolfgang Grieskamp, Daniel Ramage, Hrishikesh Aradhye, Shiyu Hu
  • Publication number: 20210382962
    Abstract: Systems and methods of determining a global model are provided. In particular, one or more local updates can be received from a plurality of user devices. Each local update can be determined by the respective user device based at least in part on one or more data examples stored on the user device. The one or more data examples stored on the plurality of user devices are distributed on an uneven basis, such that no user device includes a representative sample of the overall distribution of data examples. The local updates can then be aggregated to determine a global model.
    Type: Application
    Filed: August 19, 2021
    Publication date: December 9, 2021
    Inventors: Hugh Brendan McMahan, Jakub Konecny, Eider Brantly Moore, Daniel Ramage, Blaise H. Aguera-Arcas
  • Publication number: 20210357986
    Abstract: Systems and methods are shown for providing private local sponsored content selection and improving intelligence models through distribution among mobile devices. This allows greater data gathering capabilities through the use of the sensors of the mobile devices as well as data stored on data storage components of the mobile devices to create predicted models while offering better opportunities to preserve privacy. Locally stored profiles comprising machine intelligence models may also be used to determine the relevance of the data gathered and in improving an aggregated model for identifying the relevance of data and the selection of sponsored content items. Distributed optimization is used in conjunction with privacy techniques to create the improved machine intelligence models. Publishers may also benefit from the improved privacy by protecting the statistics of type or volume of sponsored content items shown with publisher content.
    Type: Application
    Filed: July 29, 2021
    Publication date: November 18, 2021
    Applicant: Google LLC
    Inventors: Keith Bonawitz, Daniel Ramage, David Petrou
  • Patent number: 11159763
    Abstract: The present disclosure provides an image capture, curation, and editing system that includes a resource-efficient mobile image capture device that continuously captures images. In particular, the present disclosure provides low power frameworks for controlling image sensor mode in a mobile image capture device. On example low power frame work includes a scene analyzer that analyzes a scene depicted by a first image and, based at least in part on such analysis, causes an image sensor control signal to be provided to an image sensor to adjust at least one of the frame rate and the resolution of the image sensor.
    Type: Grant
    Filed: July 23, 2020
    Date of Patent: October 26, 2021
    Assignee: Google LLC
    Inventors: Aaron Michael Donsbach, Benjamin Vanik, Jon Gabriel Clapper, Alison Lentz, Joshua Denali Lovejoy, Robert Douglas Fritz, III, Krzysztof Duleba, Li Zhang, Juston Payne, Emily Anne Fortuna, Iwona Bialynicka-Birula, Blaise Aguera-Arcas, Daniel Ramage, Benjamin James McMahan, Oliver Fritz Lange, Jess Holbrook
  • Patent number: 11138517
    Abstract: The present disclosure provides systems and methods for on-device machine learning. In particular, the present disclosure is directed to an on-device machine learning platform and associated techniques that enable on-device prediction, training, example collection, and/or other machine learning tasks or functionality. The on-device machine learning platform can include a context provider that securely injects context features into collected training examples and/or client-provided input data used to generate predictions/inferences. Thus, the on-device machine learning platform can enable centralized training example collection, model training, and usage of machine-learned models as a service to applications or other clients.
    Type: Grant
    Filed: August 11, 2017
    Date of Patent: October 5, 2021
    Assignee: Google LLC
    Inventors: Pannag Sanketi, Wolfgang Grieskamp, Daniel Ramage, Hrishikesh Aradhye, Shiyu Hu
  • Patent number: 11120102
    Abstract: Systems and methods of determining a global model are provided. In particular, one or more local updates can be received from a plurality of user devices. Each local update can be determined by the respective user device based at least in part on one or more data examples stored on the user device. The one or more data examples stored on the plurality of user devices are distributed on an uneven basis, such that no user device includes a representative sample of the overall distribution of data examples. The local updates can then be aggregated to determine a global model.
    Type: Grant
    Filed: August 27, 2020
    Date of Patent: September 14, 2021
    Assignee: Google LLC
    Inventors: Hugh Brendan McMahan, Jakub Konecny, Eider Brantly Moore, Daniel Ramage, Blaise H. Aguera-Arcas