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公开(公告)号：US20210192078A1
公开(公告)日：2021-06-24
申请号：US17129579
申请日：2020-12-21
Applicant: Apple Inc.
Inventor： Stephen Cosman , Kalu Onuka Kalu , Marcelo Lotif Araujo , Michael Chatzidakis , Thi Hai Van Do , Alexis Hugo Louis Durocher , Guillaume Tartavel , Sowmya Gopalan , Vignesh Jagadeesh , Abhishek Bhowmick , John Duchi , Julien Freudiger , Gaurav Kapoor , Ryan M. Rogers
Abstract: Embodiments described herein provide for a non-transitory machine-readable medium storing instructions to cause one or more processors to receive, at a client device, a machine learning model from a server, detect a usage pattern for a content item, store an association between the content item and the detected usage pattern in local data, train the machine learning model using local data for the content item with the detected usage pattern to generate a trained machine learning model, generate an update for the machine learning model, privatize the update for the machine learning model, and transmit the privatized update for the machine learning model to the server.
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公开(公告)号：US20180121803A1
公开(公告)日：2018-05-03
申请号：US15648374
申请日：2017-07-12
Applicant: Apple Inc.
Inventor： Hon Yuk Chan , John M. Hörnkvist , Lun Cui , Vipul Ved Prakash , Anubhav Malhotra , Stanley N. Hung , Julien Freudiger
CPC classification number: G06F16/24578 , G06F16/248 , G06F16/335 , G06F16/951 , G06F16/9535 , G06N3/0454 , G06N3/08 , G06N20/00 , G06N20/20
Abstract: A method and apparatus of a device that generates a re-ranking model used to re-rank a plurality of search results on a client device is described. In an exemplary embodiment, the device receives a crowd-sourced intra-domain model from a server, where the intra-domain model is a search result re-ranking model generated based on at least device interactions of a plurality of users interacting with a plurality of other devices. The device further generates a re-ranking model from the crowd-sourced intra-domain model and a local model, where the local model includes private data representing a device user's interaction with that device and the re-ranking model is used to re-rank a plurality of search results.
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公开(公告)号：US09594741B1
公开(公告)日：2017-03-14
申请号：US15275356
申请日：2016-09-24
Applicant: Apple Inc.
Inventor： Abhradeep Guha Thakurta , Andrew H. Vyrros , Umesh S. Vaishampayan , Gaurav Kapoor , Julien Freudiger , Vivek Rangarajan Sridhar , Doug Davidson
CPC classification number: G06F17/2765 , G06F17/16 , G06F17/2705 , G06F17/2735 , G06F17/277 , G06F17/30737 , G06N99/005
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.
Abstract translation: 公开了系统和方法，用于服务器以众包方式学习由用户客户端设备生成的新词，同时保持客户端设备的本地差异隐私。 客户端设备可以确定在客户端设备上键入的单词是不包含在客户端设备上的字典或资产目录中的新单词。 新词可以分类为娱乐，健康，财务等分类。客户端设备上的差异隐私系统可以包括每个新词分类的隐私预算。 如果有可用于分类的隐私预算，则可以将分类中的一个或多个新术语发送到新术语学习服务器，并且减少分类的隐私预算。 隐私预算可以定期补充。
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公开(公告)号：US12052315B2
公开(公告)日：2024-07-30
申请号：US17129579
申请日：2020-12-21
Applicant: Apple Inc.
Inventor： Stephen Cosman , Kalu Onuka Kalu , Marcelo Lotif Araujo , Michael Chatzidakis , Thi Hai Van Do , Alexis Hugo Louis Durocher , Guillaume Tartavel , Sowmya Gopalan , Vignesh Jagadeesh , Abhishek Bhowmick , John Duchi , Julien Freudiger , Gaurav Kapoor , Ryan M. Rogers
IPC: H04L67/1097 , G06F16/2457 , G06F16/438 , G06F16/44 , G06F18/214 , G06F21/62 , G06N3/063 , G06N20/00 , G06V10/774 , G06V10/82 , H04L67/00
CPC classification number: H04L67/1097 , G06F16/24578 , G06F16/438 , G06F16/447 , G06F18/2148 , G06F21/6254 , G06N3/063 , G06N20/00 , G06V10/7747 , G06V10/82 , H04L67/34
Abstract: Embodiments described herein provide for a non-transitory machine-readable medium storing instructions to cause one or more processors to receive, at a client device, a machine learning model from a server, detect a usage pattern for a content item, store an association between the content item and the detected usage pattern in local data, train the machine learning model using local data for the content item with the detected usage pattern to generate a trained machine learning model, generate an update for the machine learning model, privatize the update for the machine learning model, and transmit the privatized update for the machine learning model to the server.
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公开(公告)号：US11256864B2
公开(公告)日：2022-02-22
申请号：US17183008
申请日：2021-02-23
Applicant: Apple Inc.
Inventor： Zeheng Chen , Jessica Aranda , Patrick Coffman , Patrick W. Demasco , Julien Freudiger , Karan Misra , Stephen J. Rhee , Guy L. Tribble
IPC: G06F3/0486 , G06F40/274 , G06F9/54 , G06F3/04886
Abstract: Private and secure autocomplete suggestions are enabled based on a user contacts database, even when an application has not been granted access to the user contacts database. A keyboard process can receive and display suggestions based on input provided via the keyboard. The suggestions are generated based on a contacts database of a user. The suggestions are generated without exposing the contacts database to the application. Suggestions are then displayed to the user without exposing the suggestions to the application. Once a suggestion is selected by a user, the selected suggestion is provided to the application for insertion into a text field.
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Patent Agency Ranking
公开(公告)号：US20210397789A1
公开(公告)日：2021-12-23
申请号：US17183008
申请日：2021-02-23
Applicant: Apple Inc.
Inventor： Zeheng Chen , Jessica Aranda , Patrick Coffman , Patrick W. Demasco , Julien Freudiger , Karan Misra , Stephen J. Rhee , Guy L. Tribble
IPC: G06F40/274 , G06F3/0488 , G06F9/54
Abstract: Private and secure autocomplete suggestions are enabled based on a user contacts database, even when an application has not been granted access to the user contacts database. A keyboard process can receive and display suggestions based on input provided via the keyboard. The suggestions are generated based on a contacts database of a user. The suggestions are generated without exposing the contacts database to the application. Suggestions are then displayed to the user without exposing the suggestions to the application. Once a suggestion is selected by a user, the selected suggestion is provided to the application for insertion into a text field.
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公开(公告)号：US20210397751A1
公开(公告)日：2021-12-23
申请号：US17190995
申请日：2021-03-03
Applicant: Apple Inc.
Inventor： Deepak Iyer , Jessica Aranda , Cindy M. Barrett , Patrick Coffman , Julien Freudiger , Alexander S. Haas , Nahir A. Khan , Behkish J. Manzari , Kevin M. Miller , Brian Pietsch , Stephen J. Rhee , Stefan Stuerke , Eric L. Wilson
IPC: G06F21/83 , G06F21/12 , G06F21/32 , G06F3/0481 , G06F3/0484
Abstract: Embodiments described herein provide a software-based privacy indicator for a camera and microphone that focuses not purely on hardware status (e.g., on or off), but on whether potentially private data is flowing to the system or an application. If based purely on hardware status, the indicator for an electronic device may be shown in scenarios where no data actually flows to the system or applications. The privacy indicator will be enabled if any camera or microphone data is relayed to the operating system or an application that is executed via the operating system. When the device uses the microphone and camera to capture environmental metadata about the surroundings of the device without providing any audio samples, images, or video frames to the system or an application, the privacy indicator will not be enabled.
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公开(公告)号：US10701042B2
公开(公告)日：2020-06-30
申请号：US16159473
申请日：2018-10-12
Applicant: Apple Inc.
Inventor： Abhradeep Guha Thakurta , Andrew H. Vyrros , Umesh S. Vaishampayan , Gaurav Kapoor , Julien Freudiger , Vivek Rangarajan Sridhar , Doug Davidson
IPC: H04L29/06 , G06N20/00 , G06F16/36 , G06N3/12 , G06F40/205 , G06F40/242 , G06F40/279 , G06F40/284 , G06F17/16 , G06F16/35 , G06F7/58 , G06F9/30
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.
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公开(公告)号：US10454962B2
公开(公告)日：2019-10-22
申请号：US16159481
申请日：2018-10-12
Applicant: Apple Inc.
Inventor： Abhradeep Guha Thakurta , Andrew H. Vyrros , Umesh S. Vaishampayan , Gaurav Kapoor , Julien Freudiger , Vipul Ved Prakash , Arnaud Legendre , Steven Duplinsky
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.
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公开(公告)号：US10133725B2
公开(公告)日：2018-11-20
申请号：US15477921
申请日：2017-04-03
Applicant: Apple Inc.
Inventor： Abhradeep Guha Thakurta , Andrew H. Vyrros , Umesh S. Vaishampayan , Gaurav Kapoor , Julien Freudiger , Vivek Rangarajan Sridhar , Doug Davidson
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.
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