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1.发明申请公开(公告)号：US20160323490A1
公开(公告)日：2016-11-03
申请号：US14698632
申请日：2015-04-28
Applicant: Microsoft Technology Licensing, LLC
Inventor： Naveen Thumpudi , Denis Demandolx , Sandeep Kanumuri , Suhib Alsisan , William Guyman , Yijie Wang
IPC: H04N5/232
CPC classification number: H04N5/232 , H04N5/23222
Abstract: A facility for generating at least one image is described. For each of multiple registered photography scenarios, the facility determines a suitable score for the scenario based upon state of a photography device. The facility selects a scenario having a suitability score that is no lower than any other determined suitability score. The facility then captures a sequence of one or more frames in a manner specified for the selected scenario, and processes that captured sequence of frames in a manner specified for the selected scenario to obtain at least one image.
Abstract translation: 描述用于生成至少一个图像的设备。 对于多个注册摄影场景中的每一个，设备基于摄影设备的状态来确定场景的合适分数。 该设施选择具有不低于任何其他确定的适合性得分的适合性得分的场景。 然后，该设施以为所选场景指定的方式捕获一个或多个帧的序列，并且以为所选场景指定的方式处理捕获的帧序列以获得至少一个图像。
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公开(公告)号：US11514115B2
公开(公告)日：2022-11-29
申请号：US15844032
申请日：2017-12-15
Applicant: Microsoft Technology Licensing, LLC
Inventor： Souvik Ghosh , Timothy Paul Jurka , Sergei Tolmanov , Yijie Wang
IPC: G06F16/9535 , H04L67/306 , G06Q50/00 , G06N20/00 , H04L67/50
Abstract: In an example, a plurality of potential feed objects are obtained. An identification of a user performing a navigation command in a user interface is also obtained, the navigation command causing a feed to be displayed or updated. The identification of the user and the plurality of potential feed objects are fed to a machine learned feed object ranking model, the feed object ranking model having been trained via a machine learning algorithm to calculate a score for each of the potential feed objects, the score being based on a combination of a likelihood that the user will perform an interaction, via the user interface, on the potential feed object, likelihoods that the user's interaction will cause one or more downstream events by other users, and a value of the one or more downstream events to a social networking service. The plurality of feed objects are ranked by their scores.
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公开(公告)号：US10956524B2
公开(公告)日：2021-03-23
申请号：US16144848
申请日：2018-09-27
Applicant: Microsoft Technology Licensing, LLC
Inventor： Shaunak Chatterjee , Ajith Muralidharan , Viral Gupta , Yijie Wang , Deepak Agarwal
IPC: G06F7/02 , G06F16/00 , G06F16/9535 , G06F17/18 , G06N20/00 , G06F16/9038 , G06F16/2453 , G06Q50/00
Abstract: In an example embodiment, a machine learned model is used to determine whether to send a notification for a feed object to a user. This machine learned model is optimized not just based on the likelihood that the notification will cause the user to interact with the feed object, but also the likely short-term and long-term impacts of the user interacting with the feed object. This machine learned model factors in not only the viewer's probability of immediate action, such as clicking on a feed object, but also the probability of long-term impact, such as the display causing the viewer to contribute content to the network or the viewer's response encouraging more people to contribute content to the network. As such, the machine learned model is optimized not just on notification interactivity but also on feed objects interactivity.
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公开(公告)号：US20200218770A1
公开(公告)日：2020-07-09
申请号：US16241649
申请日：2019-01-07
Applicant: Microsoft Technology Licensing, LLC
Inventor： Ye Tu , Yiping Yuan , Chun Lo , Shaunak Chatterjee , Yijie Wang
IPC: G06F16/954 , G06F16/953 , H04L29/08 , G06N20/00 , G06Q50/00
Abstract: A machine for content-feedback-based machine learning to incent online content creation. The machine accesses a relevance value that identifies a level of relevance of a content item to a user. The content item is created by a content creator. The machine generates, using a machine learning model, a feedback sensitivity score associated with the content creator. The machine generates, based on the relevance value and a product between the feedback sensitivity score and a likelihood of the user providing a feedback signal in relation to the content item, a ranking score for the content item. The machine causes display of the content item, based on the ranking score, in a user interface of a client device associated with the user. An input pertaining to the content item received via the user interface causes improvement of the machine learning model based on updating the one or more feedback features.
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公开(公告)号：US20190188323A1
公开(公告)日：2019-06-20
申请号：US15844032
申请日：2017-12-15
Applicant: Microsoft Technology Licensing, LLC
Inventor： Souvik Ghosh , Timothy Paul Jurka , Sergei Tolmanov , Yijie Wang
CPC classification number: G06F16/9535 , G06N20/00 , G06Q50/01 , H04L67/306
Abstract: In an example, a plurality of potential feed objects are obtained. An identification of a user performing a navigation command in a user interface is also obtained, the navigation command causing a feed to be displayed or updated. The identification of the user and the plurality of potential feed objects are fed to a machine learned feed object ranking model, the feed object ranking model having been trained via a machine learning algorithm to calculate a score for each of the potential feed objects, the score being based on a combination of a likelihood that the user will perform an interaction, via the user interface, on the potential feed object, likelihoods that the user's interaction will cause one or more downstream events by other users, and a value of the one or more downstream events to a social networking service. The plurality of feed objects are ranked by their scores.
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Patent Agency Ranking
公开(公告)号：US10949480B2
公开(公告)日：2021-03-16
申请号：US15900219
申请日：2018-02-20
Applicant: Microsoft Technology Licensing, LLC
Inventor： Boyi Chen , Yijie Wang , Timothy Paul Jurka , Ying Xuan
IPC: G06F16/9535 , G06Q50/00 , G06N20/00 , G06F16/2457
Abstract: In an example embodiment, a GLMix model is utilized that models viewers and actors of feed items. This allows for random effects of individual viewers and actors to be taken into account without introducing biases. Additionally, in an example embodiment, predictions/recommendations are made more accurate by using three models, which are then combined, instead of a single GLMix model. Each of these models has different granularities and dimensions. A global model may model the similarity between user attributes (e.g., from the member profile or activity history) and item attributes. A per-viewer model may model user attributes and activity history of actors on feed items. A per-actor model may model user attributes and activity history of the viewers of feed items. The per-actor model may therefore, rely on information regarding how and what type of viewers interacted with items acted on by the particular actor.
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公开(公告)号：US20190258741A1
公开(公告)日：2019-08-22
申请号：US15900219
申请日：2018-02-20
Applicant: Microsoft Technology Licensing, LLC
Inventor： Boyi Chen , Yijie Wang , Timothy Paul Jurka , Ying Xuan
Abstract: In an example embodiment, a GLMix model is utilized that models viewers and actors of feed items. This allows for random effects of individual viewers and actors to be taken into account without introducing biases. Additionally, in an example embodiment, predictions/recommendations are made more accurate by using three models, which are then combined, instead of a single GLMix model. Each of these models has different granularities and dimensions. A global model may model the similarity between user attributes (e.g., from the member profile or activity history) and item attributes. A per-viewer model may model user attributes and activity history of actors on feed items. A per-actor model may model user attributes and activity history of the viewers of feed items. The per-actor model may therefore, rely on information regarding how and what type of viewers interacted with items acted on by the particular actor.
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公开(公告)号：US11151661B2
公开(公告)日：2021-10-19
申请号：US15966583
申请日：2018-04-30
Applicant: Microsoft Technology Licensing, LLC
Inventor： Yijie Wang , Souvik Ghosh , Timothy Paul Jurka , Shaunak Chatterjee , Wei Xue , Bonnie Barrilleaux
IPC: G06Q50/00 , G06F17/18 , G06N20/00 , G06F16/435 , G06F3/0482
Abstract: A plurality of potential feed objects and corresponding identifications of actors who performed a user interface action that caused a corresponding potential feed object to be generated are obtained. The plurality of potential feed objects and corresponding actor identifications are then fed into a machine learned feed object ranking model, with the machine learned feed object ranking model having been trained via a machine learning algorithm to calculate a score for each of the potential feed objects. The score is based on a combination of a likelihood that the user will perform an interaction, via the user interface, on the potential feed object, likelihood that the user's interaction will cause one or more downstream events by other users, and likelihood that a response from a viewer will cause the actor corresponding to the potential feed object to perform an additional user interface action to generate another potential feed object.
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公开(公告)号：US11042226B2
公开(公告)日：2021-06-22
申请号：US16059849
申请日：2018-08-09
Applicant: Microsoft Technology Licensing, LLC
Inventor： Erik Alan Holverson , Yijie Wang , Alexandru Octavian Balan
Abstract: Techniques that modify traditional input devices (e.g., traditional computer mouse form factor) to interact with the virtual reality (VR) devices are described. Such modification allows the VR devices the ability to track the position and orientation of the mouse in the 3 dimensional (3D) VR without requiring extensive complex hardware typically included in VR motion controllers. Specifically, the traditional mouse form factor may be merged with 3D constellation based tracking elements (e.g., LEDs) with minimal form-factor modifications. The constellation tracking elements may include a plurality of fiducial markers on the mouse that may be detected by an imaging sensor, and thus allow tracking with a single imaging sensor (e.g., either head-mounted or fixed position).
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公开(公告)号：US10769227B2
公开(公告)日：2020-09-08
申请号：US16241649
申请日：2019-01-07
Applicant: Microsoft Technology Licensing, LLC
Inventor： Ye Tu , Yiping Yuan , Chun Lo , Shaunak Chatterjee , Yijie Wang
IPC: G06F15/16 , G06F16/954 , G06F16/953 , G06Q50/00 , G06N20/00 , H04L29/08
Abstract: A machine for content-feedback-based machine learning to incent online content creation. The machine accesses a relevance value that identifies a level of relevance of a content item to a user. The content item is created by a content creator. The machine generates, using a machine learning model, a feedback sensitivity score associated with the content creator. The machine generates, based on the relevance value and a product between the feedback sensitivity score and a likelihood of the user providing a feedback signal in relation to the content item, a ranking score for the content item. The machine causes display of the content item, based on the ranking score, in a user interface of a client device associated with the user. An input pertaining to the content item received via the user interface causes improvement of the machine learning model based on updating the one or more feedback features.
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