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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：US20210191967A1
公开(公告)日：2021-06-24
申请号：US17129673
申请日：2020-12-21
Applicant: Apple Inc.
Inventor： Stephen Cosman , Alexis Hugo Louis Durocher , Andrey Leonov , Tai Ying Chiang
IPC: G06F16/44 , G06F16/438 , G06F16/2457
Abstract: Embodiments described herein provide for a non-transitory machine-readable medium storing instructions to cause one or more processors to select a set of content items from a content item collection based upon a temporal relevance and a contextual relevance to a period of time, rank the set of content items based on at least one of a content item category or a content item predefined relevance score, partition the period of time into a set of time slots to schedule for rendering content in an application, rank the set of time slots based on device usage analysis for the period of time, and schedule the set of content items into the set of time slots in accordance with the rankings.
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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：US11671493B2
公开(公告)日：2023-06-06
申请号：US17129673
申请日：2020-12-21
Applicant: Apple Inc.
Inventor： Stephen Cosman , Alexis Hugo Louis Durocher , Andrey Leonov , Tai Ying Chiang
IPC: G06F17/00 , H04L67/1097 , G06F16/44 , G06F16/2457 , G06F16/438 , G06N20/00 , G06F21/62 , G06N3/063 , H04L67/00 , G06F18/214 , G06V10/774 , G06V10/82
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 select a set of content items from a content item collection based upon a temporal relevance and a contextual relevance to a period of time, rank the set of content items based on at least one of a content item category or a content item predefined relevance score, partition the period of time into a set of time slots to schedule for rendering content in an application, rank the set of time slots based on device usage analysis for the period of time, and schedule the set of content items into the set of time slots in accordance with the rankings.
