Patents by Inventor Umesh S. Vaishampayan

Umesh S. Vaishampayan 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: 20210289043
    Abstract: Disclosed are systems, methods, and non-transitory computer-readable storage media for efficiently monitoring the operating context of a computing device. In some implementations, the context daemon and/or the context client can be terminated to conserve system resources. For example, if the context daemon and/or the context client are idle, they can be shutdown to conserve battery power or free other system resources (e.g., memory). When an event occurs (e.g., a change in current context) that requires the context daemon and/or the context client to be running, the context daemon and/or the context client can be restarted to handle the event. Thus, system resources can be conserved while still providing relevant context information collection and callback notification features.
    Type: Application
    Filed: March 9, 2021
    Publication date: September 16, 2021
    Applicant: Apple Inc.
    Inventors: Alexander Barraclough Brown, Umesh S. Vaishampayan
  • Patent number: 11055492
    Abstract: Embodiments described herein provide techniques to encode sequential data in a privacy preserving manner before the data is sent to a sequence learning server. The server can then determine aggregate trends within an overall set of users, without having any specific knowledge about the contributions of individual users. The server can be used to learn new words generated by user client devices in a crowdsourced manner while maintaining local differential privacy of client devices. The server can also learn other sequential data including typed, autocorrected, revised text sequences, sequences of application launches, sequences of purchases on an application store, or other sequences of activities that can be performed on an electronic device.
    Type: Grant
    Filed: February 8, 2019
    Date of Patent: July 6, 2021
    Assignee: Apple Inc.
    Inventors: Abhishek Bhowmick, Ryan M. Rogers, Umesh S. Vaishampayan, Kartik R. Venkatraman
  • Patent number: 11042664
    Abstract: One embodiment provides a system that implements a 1-bit protocol for differential privacy for a set of client devices that transmit information to a server. Implementations may leverage specialized instruction sets or engines built into the hardware or firmware of a client device to improve the efficiency of the protocol. For example, a client device may utilize these cryptographic functions to randomize information sent to the server. In one embodiment, the client device may use cryptographic functions such as hashes including SHA or block ciphers including AES to provide an efficient mechanism for implementing differential privacy.
    Type: Grant
    Filed: January 17, 2020
    Date of Patent: June 22, 2021
    Assignee: Apple Inc.
    Inventors: Yannick L. Sierra, Abhradeep Guha Thakurta, Umesh S. Vaishampayan, John C. Hurley, Keaton F. Mowery, Michael Brouwer
  • Patent number: 10986211
    Abstract: Disclosed are systems, methods, and non-transitory computer-readable storage media for efficiently monitoring the operating context of a computing device. In some implementations, the context daemon and/or the context client can be terminated to conserve system resources. For example, if the context daemon and/or the context client are idle, they can be shutdown to conserve battery power or free other system resources (e.g., memory). When an event occurs (e.g., a change in current context) that requires the context daemon and/or the context client to be running, the context daemon and/or the context client can be restarted to handle the event. Thus, system resources can be conserved while still providing relevant context information collection and callback notification features.
    Type: Grant
    Filed: December 26, 2019
    Date of Patent: April 20, 2021
    Assignee: Apple Inc.
    Inventors: Alexander Barraclough Brown, Umesh S. Vaishampayan
  • Publication number: 20200410134
    Abstract: Embodiments described herein provide a privacy mechanism to protect user data when transmitting the data to a server that estimates a frequency of such data amongst a set of client devices. In one embodiment, a differential privacy mechanism is implemented using a count-mean-sketch technique that can reduce resource requirements required to enable privacy while providing provable guarantees regarding privacy and utility. For instance, the mechanism can provide the ability to tailor utility (e.g. accuracy of estimations) against the resource requirements (e.g. transmission bandwidth and computation complexity).
    Type: Application
    Filed: September 14, 2020
    Publication date: December 31, 2020
    Inventors: Abhishek Bhowmick, Andrew H. Vyrros, Umesh S. Vaishampayan, Kevin W. Decker, Conrad Shultz, Steve Falkenburg, Mateusz Rajca
  • Publication number: 20200382616
    Abstract: In an exemplary process for remote execution of machine-learned models, one or more signals from a second electronic device is detected by a first electronic device. The second electronic device includes a machine-learned model associated with an application implemented on the first electronic device. Based on the one or more signals, a communication connection is established with the second electronic device and a proxy to the machine-learned model is generated. Input data is obtained via a sensor of the first electronic device. A representation of the input data is sent to the second electronic device via the proxy and the established communication connection. The representation of the input data is processed through the machine-learned model to generate an output. A result derived from the output is received via the communication connection and a representation of the result is outputted.
    Type: Application
    Filed: August 28, 2019
    Publication date: December 3, 2020
    Inventors: Umesh S. VAISHAMPAYAN, Gaurav KAPOOR, Kit-man WAN
  • Publication number: 20200356685
    Abstract: Embodiments described herein ensure differential privacy when transmitting data to a server that estimates a frequency of such data amongst a set of client devices. The differential privacy mechanism may provide a predictable degree of variance for frequency estimations of data. The system may use a multibit histogram model or Hadamard multibit model for the differential privacy mechanism, both of which provide a predictable degree of accuracy of frequency estimations while still providing mathematically provable levels of privacy.
    Type: Application
    Filed: July 24, 2020
    Publication date: November 12, 2020
    Inventors: Abhishek Bhowmick, Andrew H. Vyrros, Matthew R. Salesi, Umesh S. Vaishampayan
  • Patent number: 10776511
    Abstract: Embodiments described herein provide a privacy mechanism to protect user data when transmitting the data to a server that estimates a frequency of such data amongst a set of client devices. One embodiment uses a differential privacy mechanism to enhance a user experience by inferring potential user preferences from analyzing crowdsourced user interaction data. Based on a statistical analysis of user interactions in relation to various features or events, development efforts with respect to application behavior may be refined or enhanced. For example, user interactions in relation to the presentation of content such as content from online sources may be analyzed. Accordingly, presentation settings or preferences may be defined based on the crowdsourced user interaction data.
    Type: Grant
    Filed: November 7, 2017
    Date of Patent: September 15, 2020
    Assignee: Apple Inc.
    Inventors: Kevin W. Decker, Conrad Shultz, Steve Falkenburg, Mateusz Rajca, Abhishek Bhowmick, Andrew H. Vyrros, Umesh S. Vaishampayan
  • Publication number: 20200257816
    Abstract: One embodiment provides a system that implements a 1-bit protocol for differential privacy for a set of client devices that transmit information to a server. Implementations may leverage specialized instruction sets or engines built into the hardware or firmware of a client device to improve the efficiency of the protocol. For example, a client device may utilize these cryptographic functions to randomize information sent to the server. In one embodiment, the client device may use cryptographic functions such as hashes including SHA or block ciphers including AES to provide an efficient mechanism for implementing differential privacy.
    Type: Application
    Filed: January 17, 2020
    Publication date: August 13, 2020
    Inventors: Yannick L. Sierra, Abhradeep Guha Thakurta, Umesh S. Vaishampayan, John C. Hurley, Keaton F. Mowery, Michael Brouwer
  • Patent number: 10726139
    Abstract: Embodiments described herein ensure differential privacy when transmitting data to a server that estimates a frequency of such data amongst a set of client devices. The differential privacy mechanism may provide a predictable degree of variance for frequency estimations of data. The system may use a multibit histogram model or Hadamard multibit model for the differential privacy mechanism, both of which provide a predictable degree of accuracy of frequency estimations while still providing mathematically provable levels of privacy.
    Type: Grant
    Filed: September 30, 2017
    Date of Patent: July 28, 2020
    Assignee: Apple Inc.
    Inventors: Abhishek Bhowmick, Andrew H. Vyrros, Matthew R. Salesi, Umesh S. Vaishampayan
  • Patent number: 10701042
    Abstract: Systems and methods are disclosed for a server learning new words generated by user client devices in a crowdsourced manner while maintaining local differential privacy of client devices. A client device can determine that a word typed on the client device is a new word that is not contained in a dictionary or asset catalog on the client device. New words can be grouped in classifications such as entertainment, health, finance, etc. A differential privacy system on the client device can comprise a privacy budget for each classification of new words. If there is privacy budget available for the classification, then one or more new terms in a classification can be sent to new term learning server, and the privacy budget for the classification reduced. The privacy budget can be periodically replenished.
    Type: Grant
    Filed: October 12, 2018
    Date of Patent: June 30, 2020
    Assignee: Apple Inc.
    Inventors: Abhradeep Guha Thakurta, Andrew H. Vyrros, Umesh S. Vaishampayan, Gaurav Kapoor, Julien Freudiger, Vivek Rangarajan Sridhar, Doug Davidson
  • Publication number: 20200137193
    Abstract: Disclosed are systems, methods, and non-transitory computer-readable storage media for efficiently monitoring the operating context of a computing device. In some implementations, the context daemon and/or the context client can be terminated to conserve system resources. For example, if the context daemon and/or the context client are idle, they can be shutdown to conserve battery power or free other system resources (e.g., memory). When an event occurs (e.g., a change in current context) that requires the context daemon and/or the context client to be running, the context daemon and/or the context client can be restarted to handle the event. Thus, system resources can be conserved while still providing relevant context information collection and callback notification features.
    Type: Application
    Filed: December 26, 2019
    Publication date: April 30, 2020
    Applicant: Apple Inc.
    Inventors: Alexander Barraclough Brown, Umesh S. Vaishampayan
  • Publication number: 20200104705
    Abstract: Embodiments described herein provide a technique to crowdsource labeling of training data for a machine learning model while maintaining the privacy of the data provided by crowdsourcing participants. Client devices can be used to generate proposed labels for a unit of data to be used in a training dataset. One or more privacy mechanisms are used to protect user data when transmitting the data to a server. The server can aggregate the proposed labels and use the most frequently proposed labels for an element as the label for the element when generating training data for the machine learning model. The machine learning model is then trained using the crowdsourced labels to improve the accuracy of the model.
    Type: Application
    Filed: August 29, 2019
    Publication date: April 2, 2020
    Inventors: Abhishek Bhowmick, Ryan M. Rogers, Umesh S. Vaishampayan, Andrew H. Vyrros
  • Patent number: 10599867
    Abstract: Embodiments described herein provide a privacy mechanism to protect user data when transmitting the data to a server that estimates a frequency of such data amongst a set of client devices. In one embodiment, a differential privacy mechanism is implemented using a count-mean-sketch technique that can reduce resource requirements required to enable privacy while providing provable guarantees regarding privacy and utility. For instance, the mechanism can provide the ability to tailor utility (e.g. accuracy of estimations) against the resource requirements (e.g. transmission bandwidth and computation complexity).
    Type: Grant
    Filed: November 7, 2017
    Date of Patent: March 24, 2020
    Assignee: Apple Inc.
    Inventors: Abhishek Bhowmick, Andrew H. Vyrros, Umesh S. Vaishampayan, Kevin W. Decker, Conrad Shultz, Steve Falkenburg, Mateusz Rajca
  • Patent number: 10599868
    Abstract: Embodiments described herein provide a privacy mechanism to protect user data when transmitting the data to a server that estimates a frequency of such data amongst a set of client devices. One embodiment uses a differential privacy mechanism to enhance a user experience by identifying particular websites that exhibit particular characteristics. In one embodiment, websites that are associated with a high resource consumption are identified. High resource consumption can be identified based on threshold of particular resources such as processor, memory, network bandwidth, and power usage.
    Type: Grant
    Filed: November 7, 2017
    Date of Patent: March 24, 2020
    Assignee: Apple Inc.
    Inventors: Gavin Barraclough, Christophe Dumez, Abhishek Bhowmick, Andrew H. Vyrros, Umesh S. Vaishampayan
  • Patent number: 10594835
    Abstract: Disclosed are systems, methods, and non-transitory computer-readable storage media for efficiently monitoring the operating context of a computing device. In some implementations, the context daemon and/or the context client can be terminated to conserve system resources. For example, if the context daemon and/or the context client are idle, they can be shutdown to conserve battery power or free other system resources (e.g., memory). When an event occurs (e.g., a change in current context) that requires the context daemon and/or the context client to be running, the context daemon and/or the context client can be restarted to handle the event. Thus, system resources can be conserved while still providing relevant context information collection and callback notification features.
    Type: Grant
    Filed: May 10, 2016
    Date of Patent: March 17, 2020
    Assignee: Apple Inc.
    Inventors: Alexander Barraclough Brown, Umesh S. Vaishampayan
  • Publication number: 20200082274
    Abstract: The subject technology provides receiving a neural network (NN) model to be executed on a target platform, the NN model including multiple layers that include operations and some of the operations being executable on multiple processors of the target platform. The subject technology further sorts the operations from the multiple layers in a particular order based at least in part on grouping the operations that are executable by a particular processor of the multiple processors. The subject technology determines, based at least in part on a cost of transferring the operations between the multiple processors, an assignment of one of the multiple processors for each of the sorted operations of each of the layers in a manner that minimizes a total cost of executing the operations. Further, for each layer of the NN model, the subject technology includes an annotation to indicate the processor assigned for each of the operations.
    Type: Application
    Filed: January 30, 2019
    Publication date: March 12, 2020
    Inventors: Francesco ROSSI, Cecile M. FORET, Gaurav KAPOOR, Kit-Man WAN, Umesh S. VAISHAMPAYAN, Etienne BELANGER, Albert ANTONY, Alexey MARINICHEV, Marco ZULIANI, Xiaojin SHI
  • Publication number: 20200082273
    Abstract: The subject technology runs a compiled neural network (NN) model on a particular processor with multiple priority queues for executing different processes, the compiled NN model being assigned to a particular priority queue, and the compiled NN model includes context switch instructions that were previously inserted into a neural network (NN) model from which the compiled NN model was compiled. The subject technology determines that a particular context switch instruction has been executed by the particular processor. The subject technology determines that a different process is waiting to be executed, the different process being assigned to a different priority queue and the different process being a higher priority process than the running compiled NN model. In response to executing the particular context switch instruction, the subject technology performs a context switch to the different process assigned to the different priority queue when the different process is waiting to be executed.
    Type: Application
    Filed: January 30, 2019
    Publication date: March 12, 2020
    Inventors: Francesco ROSSI, Cecile M. FORET, Gaurav KAPOOR, Kit-Man WAN, Umesh S. VAISHAMPAYAN, Etienne BELANGER
  • Patent number: 10552631
    Abstract: One embodiment provides a system that implements a 1-bit protocol for differential privacy for a set of client devices that transmit information to a server. Implementations may leverage specialized instruction sets or engines built into the hardware or firmware of a client device to improve the efficiency of the protocol. For example, a client device may utilize these cryptographic functions to randomize information sent to the server. In one embodiment, the client device may use cryptographic functions such as hashes including SHA or block ciphers including AES to provide an efficient mechanism for implementing differential privacy.
    Type: Grant
    Filed: March 8, 2019
    Date of Patent: February 4, 2020
    Assignee: Apple Inc.
    Inventors: Yannick L. Sierra, Abhradeep Guha Thakurta, Umesh S. Vaishampayan, John C. Hurley, Keaton F. Mowery, Michael Brouwer
  • Publication number: 20190370334
    Abstract: Embodiments described herein provide techniques to encode sequential data in a privacy preserving manner before the data is sent to a sequence learning server. The server can then determine aggregate trends within an overall set of users, without having any specific knowledge about the contributions of individual users. The server can be used to learn new words generated by user client devices in a crowdsourced manner while maintaining local differential privacy of client devices. The server can also learn other sequential data including typed, autocorrected, revised text sequences, sequences of application launches, sequences of purchases on an application store, or other sequences of activities that can be performed on an electronic device.
    Type: Application
    Filed: February 8, 2019
    Publication date: December 5, 2019
    Inventors: ABHISHEK BHOWMICK, RYAN M. ROGERS, UMESH S. VAISHAMPAYAN, KARTIK R. VENKATRAMAN