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: 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
  • 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
  • Patent number: 10491708
    Abstract: Disclosed are systems, methods, and non-transitory computer-readable storage media for notifying context clients of changes to the current context of a computing device. In some implementations, a context client can register to be called back when the context daemon detects specified context. For example, the context client can specify a context in which the context client is interested. When the context daemon detects that the current context of the computing device corresponds to the registered context, the context daemon can notify the context client that the current context matches the context in which the context client is interested. Thus, context clients do not require the programming necessary to independently obtain context updates and detect changes in context that are relevant or of interest to the context client.
    Type: Grant
    Filed: May 10, 2016
    Date of Patent: November 26, 2019
    Assignee: Apple Inc.
    Inventors: Michael R. Siracusa, Joao Pedro Forjaz de Lacerda, Kit-Man Wan, Gaurav Kapoor, Umesh S. Vaishampayan
  • Patent number: 10454962
    Abstract: Systems and methods are disclosed for generating term frequencies of known terms based on crowdsourced differentially private sketches of the known terms. An asset catalog can be updated with new frequency counts for known terms based on the crowdsourced differentially private sketches. Known terms can have a classification. A client device can maintain a privacy budget for each classification of known terms. Classifications can include emojis, deep links, locations, finance terms, and health terms, etc. A privacy budget ensures that a client does not transmit too much information to a term frequency server, thereby compromising the privacy of the client device.
    Type: Grant
    Filed: October 12, 2018
    Date of Patent: October 22, 2019
    Assignee: Apple Inc.
    Inventors: Abhradeep Guha Thakurta, Andrew H. Vyrros, Umesh S. Vaishampayan, Gaurav Kapoor, Julien Freudiger, Vipul Ved Prakash, Arnaud Legendre, Steven Duplinsky
  • Publication number: 20190205561
    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: March 8, 2019
    Publication date: July 4, 2019
    Inventors: Yannick L. Sierra, Abhradeep Guha Thakurta, Umesh S. Vaishampayan, John C. Hurley, Keaton F. Mowery, Michael Brouwer
  • Publication number: 20190097978
    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: Application
    Filed: October 12, 2018
    Publication date: March 28, 2019
    Inventors: Abhradeep Guha Thakurta, Andrew H. Vyrros, Umesh S. Vaishampayan, Gaurav Kapoor, Julien Freudiger, Vivek Rangarajan Sridhar, Doug Davidson
  • Patent number: 10229282
    Abstract: The system described may implement a 1-bit protocol for differential privacy for a set of client devices that transmit information to a server. Implementations of the system 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. Accordingly, the system provides an efficient mechanism for implementing differential privacy.
    Type: Grant
    Filed: September 23, 2016
    Date of Patent: March 12, 2019
    Assignee: Apple Inc.
    Inventors: Yannick L. Sierra, Abhradeep Guha Thakurta, Umesh S. Vaishampayan, John C. Hurley, Keaton F. Mowery, Michael Brouwer
  • Publication number: 20190068628
    Abstract: Systems and methods are disclosed for generating term frequencies of known terms based on crowdsourced differentially private sketches of the known terms. An asset catalog can be updated with new frequency counts for known terms based on the crowdsourced differentially private sketches. Known terms can have a classification. A client device can maintain a privacy budget for each classification of known terms. Classifications can include emojis, deep links, locations, finance terms, and health terms, etc. A privacy budget ensures that a client does not transmit too much information to a term frequency server, thereby compromising the privacy of the client device.
    Type: Application
    Filed: October 12, 2018
    Publication date: February 28, 2019
    Inventors: Abhradeep Guha Thakurta, Andrew H. Vyrros, Umesh S. Vaishampayan, Gaurav Kapoor, Julien Freudiger, Vipul Ved Prakash, Arnaud Legendre, Steven Duplinsky
  • Patent number: 10154054
    Abstract: Systems and methods are disclosed for generating term frequencies of known terms based on crowdsourced differentially private sketches of the known terms. An asset catalog can be updated with new frequency counts for known terms based on the crowdsourced differentially private sketches. Known terms can have a classification. A client device can maintain a privacy budget for each classification of known terms. Classifications can include emojis, deep links, locations, finance terms, and health terms, etc. A privacy budget ensures that a client does not transmit too much information to a term frequency server, thereby compromising the privacy of the client device.
    Type: Grant
    Filed: June 30, 2017
    Date of Patent: December 11, 2018
    Assignee: Apple Inc.
    Inventors: Abhradeep Guha Thakurta, Andrew H. Vyrros, Umesh S. Vaishampayan, Gaurav Kapoor, Julien Freudinger, Vipul Ved Prakash, Arnaud Legendre, Steven Duplinsky
  • Publication number: 20180349638
    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: Application
    Filed: November 7, 2017
    Publication date: December 6, 2018
    Inventors: Gavin Barraclough, Christophe Dumez, Abhishek Bhowmick, Andrew H. Vyrros, Umesh S. Vaishampayan
  • Publication number: 20180349637
    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: Application
    Filed: November 7, 2017
    Publication date: December 6, 2018
    Inventors: Kevin W. Decker, Conrad Shultz, Steve Falkenburg, Mateusz Rajca, Abhishek Bhowmick, Andrew H. Vyrros, Umesh S. Vaishampayan
  • Publication number: 20180349620
    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: September 30, 2017
    Publication date: December 6, 2018
    Inventors: Abhishek Bhowmick, Andrew H. Vyrros, Matthew R. Salesi, Umesh S. Vaishampayan
  • Publication number: 20180349636
    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: November 7, 2017
    Publication date: December 6, 2018
    Inventors: Abhishek Bhowmick, Andrew H. Vyrros, Umesh S. Vaishampayan
  • Patent number: 10133725
    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: April 3, 2017
    Date of Patent: November 20, 2018
    Assignee: Apple Inc.
    Inventors: Abhradeep Guha Thakurta, Andrew H. Vyrros, Umesh S. Vaishampayan, Gaurav Kapoor, Julien Freudiger, Vivek Rangarajan Sridhar, Doug Davidson
  • Patent number: 9942355
    Abstract: Disclosed are systems, methods, and non-transitory computer-readable storage media for monitoring the current context of a computing device. In some implementations, a context daemon can collect context information about the computing device. The context information can include current device hardware state information. The context information can include current software state information. The context can be derived or implied from a combination of hardware state information, software state information, or any other type of state information. For example, the derived context can be a user state (e.g., a user activity, sleeping, running, etc.) derived from or implied by hardware or software state information.
    Type: Grant
    Filed: May 12, 2016
    Date of Patent: April 10, 2018
    Assignee: Apple Inc.
    Inventors: Michael R. Siracusa, Joao Pedro Forjaz de Lacerda, Kit-Man Wan, Gaurav Kapoor, Umesh S. Vaishampayan
  • Patent number: 9894089
    Abstract: Systems and methods are disclosed for generating term frequencies of known terms based on crowdsourced differentially private sketches of the known terms. An asset catalog can be updated with new frequency counts for known terms based on the crowdsourced differentially private sketches. Known terms can have a classification. A client device can maintain a privacy budget for each classification of known terms. Classifications can include emojis, deep links, locations, finance terms, and health terms, etc. A privacy budget ensures that a client does not transmit too much information to a term frequency server, thereby compromising the privacy of the client device.
    Type: Grant
    Filed: June 19, 2017
    Date of Patent: February 13, 2018
    Assignee: Apple Inc.
    Inventors: Abhradeep Guha Thakurta, Andrew H. Vyrros, Umesh S. Vaishampayan, Gaurav Kapoor, Julien Freudinger, Vipul Ved Prakash, Arnaud Legendre, Steven Duplinsky
  • Publication number: 20180039619
    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: Application
    Filed: April 3, 2017
    Publication date: February 8, 2018
    Inventors: Abhradeep Guha Thakurta, Andrew H. Vyrros, Umesh S. Vaishampayan, Gaurav Kapoor, Julien Freudiger, Vivek Rangarajan Sridhar, Doug Davidson
  • Publication number: 20170359363
    Abstract: Systems and methods are disclosed for generating term frequencies of known terms based on crowdsourced differentially private sketches of the known terms. An asset catalog can be updated with new frequency counts for known terms based on the crowdsourced differentially private sketches. Known terms can have a classification. A client device can maintain a privacy budget for each classification of known terms. Classifications can include emojis, deep links, locations, finance terms, and health terms, etc. A privacy budget ensures that a client does not transmit too much information to a term frequency server, thereby compromising the privacy of the client device.
    Type: Application
    Filed: June 19, 2017
    Publication date: December 14, 2017
    Inventors: Abhradeep Guha Thakurta, Andrew H. Vyrros, Umesh S. Vaishampayan, Gaurav Kapoor, Julien Freudinger, Vipul Ved Prakash, Arnaud Legendre, Steven Duplinsky
  • Publication number: 20170359364
    Abstract: Systems and methods are disclosed for generating term frequencies of known terms based on crowdsourced differentially private sketches of the known terms. An asset catalog can be updated with new frequency counts for known terms based on the crowdsourced differentially private sketches. Known terms can have a classification. A client device can maintain a privacy budget for each classification of known terms. Classifications can include emojis, deep links, locations, finance terms, and health terms, etc. A privacy budget ensures that a client does not transmit too much information to a term frequency server, thereby compromising the privacy of the client device.
    Type: Application
    Filed: June 30, 2017
    Publication date: December 14, 2017
    Inventors: Abhradeep Guha Thakurta, Andrew H. Vyrros, Umesh S. Vaishampayan, Gaurav Kapoor, Julien Freudinger, Vipul Ved Prakash, Arnaud Legendre, Steven Duplinsky