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公开(公告)号：US20210201392A1
公开(公告)日：2021-07-01
申请号：US16836236
申请日：2020-03-31
Applicant: Snap Inc.
Inventor： Nima Aghdaii , Riccardo Boscolo , Rodrigo B. Farnham , Jean Luo , Kevin Lee Penner , Vincent Sung
Abstract: The subject technology identifies a first augmented reality content generator from a first merchant and a second augmented reality content generator from a second merchant. The subject technology receives a first bid amount from the first merchant and a second bid amount from the second merchant. The subject technology determines a highest bid amount among the first bid amount and the second bid amount. The subject technology provides the first augmented reality content generator or the second augmented reality content generator to a client device based on the determined highest bid.
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公开(公告)号：US20230156075A1
公开(公告)日：2023-05-18
申请号：US18099087
申请日：2023-01-19
Applicant: Snap Inc.
Inventor： Jason Brewer , Rodrigo B. Farnham , David B. Lue , Nicholas J. Stucky-Mack
CPC classification number: H04L67/10 , H04L67/306 , H04L67/14 , G06N20/00 , H04L67/535 , G06N7/01 , H04L67/01
Abstract: A machine learning engine identifies training data that includes historical user data and historical content data. A machine learning classifier is trained on the training data to generate a relevancy value for each of a plurality of given content items associated with a given user. The relevancy value for each given content item is indicative of a likelihood that the given user will perform a first user device input action and of a likelihood that the given user will perform a second user device input action, in response to being presented with the given content item. The machine learning classifier receives a plurality of candidate content items associated with a first user. The machine learning classifier generates a relevancy value for each candidate content item. At least one of the candidate content items is identified for inclusion in a first content collection based on the generated relevancy values.
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公开(公告)号：US11204949B1
公开(公告)日：2021-12-21
申请号：US16049318
申请日：2018-07-30
Applicant: Snap Inc.
Inventor： Jason Brewer , Rodrigo B. Farnham , Nima Khajehnouri , David B. Lue , Zhuo Xu
IPC: G06F16/31 , G06Q50/00 , G06F17/18 , G06F16/335
Abstract: Disclosed are systems, methods, and computer-readable storage media to present content on an electronic display. In one aspect, a method includes identifying a first candidate content and a second candidate content for presentation on an electronic display, determining a first probability and a second probability that the first candidate content and the second candidate content respectively will elicit a particular type of input response, determining a first weight and a second weight based on the first probability and the second probability respectively, selecting either the first content or the second content based on the first weight and the second weight; and presenting the selected content on the electronic display.
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公开(公告)号：US11582292B2
公开(公告)日：2023-02-14
申请号：US17321711
申请日：2021-05-17
Applicant: Snap Inc.
Inventor： Jason Brewer , Rodrigo B. Farnham , David B. Lue , Nicholas J. Stucky-Mack
Abstract: A content integration system is configured to rapidly select online content for distribution in response to a user-generated request. The content integration system can analyze available online content items and data describing the user to generate one or more numerical likelihoods estimating how the user will interact with each of the given online content items. The highest scoring content can be selected and transmitted to the user without a noticeable delay.
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公开(公告)号：US20210281632A1
公开(公告)日：2021-09-09
申请号：US17321711
申请日：2021-05-17
Applicant: Snap Inc.
Inventor： Jason Brewer , Rodrigo B. Farnham , David B. Lue , Nicholas J. Stucky-Mack
Abstract: A content integration system is configured to rapidly select online content for distribution in response to a user-generated request. The content integration system can analyze available online content items and data describing the user to generate one or more numerical likelihoods estimating how the user will interact with each of the given online content items. The highest scoring content can be selected and transmitted to the user without a noticeable delay.
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Patent Agency Ranking
公开(公告)号：US11025705B1
公开(公告)日：2021-06-01
申请号：US16749961
申请日：2020-01-22
Applicant: Snap Inc.
Inventor： Jason Brewer , Rodrigo B. Farnham , David B. Lue , Nicholas J. Stucky-Mack
Abstract: A content integration system is configured to rapidly select online content for distribution in response to a user-generated request. The content integration system can analyze available online content items and data describing the user to generate one or more numerical likelihoods estimating how the user will interact with each of the given online content items. The highest scoring content can be selected and transmitted to the user without a noticeable delay.
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公开(公告)号：US10581953B1
公开(公告)日：2020-03-03
申请号：US15610301
申请日：2017-05-31
Applicant: Snap Inc.
Inventor： Jason Brewer , Rodrigo B. Farnham , David B. Lue , Nicholas J. Stucky-Mack
Abstract: A content integration system is configured to rapidly select online content for distribution in response to a user-generated request. The content integration system can analyze available online content items and data describing the user to generate one or more numerical likelihoods estimating how the user will interact with each of the given online content items. The highest scoring content can be selected and transmitted to the user without a noticeable delay.
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公开(公告)号：US20240275845A1
公开(公告)日：2024-08-15
申请号：US18646433
申请日：2024-04-25
Applicant: Snap Inc.
Inventor： Jason Brewer , Rodrigo B. Farnham , David B. Lue , Nicholas J. Stucky-Mack
CPC classification number: H04L67/10 , G06N7/01 , G06N20/00 , H04L67/14 , H04L67/306 , H04L67/535 , H04L67/01
Abstract: A content request is received from a device of a user. A plurality of candidate content items is identified. Each candidate content item has a bid value. A relevancy value is automatically generated for each candidate content item. The relevancy value indicates whether the candidate content item is likely to be skipped by the user. For each candidate content item, a combined value is automatically generated by adjusting the bid value using the relevancy value generated for the candidate content item. One or more candidate content items are automatically selected based on the combined value generated for each of the one or more candidate content items. The one or more selected candidate content items are automatically integrated into at least one placeholder area among one or more pre-selected content items as part of the aggregated content. The aggregated content is presented on the device of the user.
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公开(公告)号：US20240273122A1
公开(公告)日：2024-08-15
申请号：US18643404
申请日：2024-04-23
Applicant: Snap Inc.
Inventor： Jason Brewer , Rodrigo B. Farnham , Nima Khajehnouri , David B. Lue , Zhuo Xu
IPC: G06F16/31 , G06F16/335 , G06F17/18 , G06Q50/00
CPC classification number: G06F16/313 , G06F16/335 , G06F17/18 , G06Q50/01
Abstract: Disclosed are systems, methods, and computer-readable storage media to present content on an electronic display. In one aspect, a method includes identifying a first candidate content and a second candidate content for presentation on an electronic display, determining a first probability and a second probability that the first candidate content and the second candidate content respectively will elicit a particular type of input response, determining a first weight and a second weight based on the first probability and the second probability respectively, selecting either the first content or the second content based on the first weight and the second weight; and presenting the selected content on the electronic display.
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公开(公告)号：US12003577B2
公开(公告)日：2024-06-04
申请号：US18099087
申请日：2023-01-19
Applicant: Snap Inc.
Inventor： Jason Brewer , Rodrigo B. Farnham , David B. Lue , Nicholas J. Stucky-Mack
CPC classification number: H04L67/10 , G06N7/01 , G06N20/00 , H04L67/14 , H04L67/306 , H04L67/535 , H04L67/01
Abstract: A machine learning engine identifies training data that includes historical user data and historical content data. A machine learning classifier is trained on the training data to generate a relevancy value for each of a plurality of given content items associated with a given user. The relevancy value for each given content item is indicative of a likelihood that the given user will perform a first user device input action and of a likelihood that the given user will perform a second user device input action, in response to being presented with the given content item. The machine learning classifier receives a plurality of candidate content items associated with a first user. The machine learning classifier generates a relevancy value for each candidate content item. At least one of the candidate content items is identified for inclusion in a first content collection based on the generated relevancy values.
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