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Patent Agency Ranking
公开(公告)号：US11514372B2
公开(公告)日：2022-11-29
申请号：US16557823
申请日：2019-08-30
Applicant: Microsoft Technology Licensing, LLC
Inventor： Zhiyuan Xu , Jinyun Yan , Kinjal Basu , Revant Kumar , Onkar A. Dalal
Abstract: Techniques are provided for automatically tuning a parameter in a layered model framework. One or more machine learning techniques are used to train multiple versions of a first model that includes a first version and a second version. A second model is stored that includes a parameter and accepts, as input, output from the first model. Multiple parameter values of the parameter are tested when processing content requests using the first and second versions of the first model. A strict subset of the plurality of parameter values are selected for the parameter of the second model, such that processing a first subset of the content requests using the first version of the first model results in a first value of a particular metric that matches a second value of the particular metric resulting from processing a second subset of the content requests using the second version of the first model.
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公开(公告)号：US20210342740A1
公开(公告)日：2021-11-04
申请号：US16866059
申请日：2020-05-04
Applicant: Microsoft Technology Licensing, LLC
Inventor： Zhiyuan Xu , Jinyun Yan , Ajith Muralidharan , Wensheng Sun , Jiaqi Ge , Shaunak Chatterjee
IPC: G06N20/00 , G06F16/9535 , G06N7/00
Abstract: Techniques for selectively transmitting electronic notifications using machine learning techniques based on entity selection history are provided. In one technique, a candidate notification is identified for a target entity. An entity selection rate of the candidate notification by the target entity is determined. Based on the candidate notification, determining a probability of the target entity visiting a target online system. Based on online history of the target entity, a measure of downstream interaction by the target entity relative to one or more online systems is determined. Based on the entity selection rate, the probability, and the measure of downstream interaction by the target entity, a score for the candidate notification is generated. Based on the score, it is determined whether data about the candidate notification is to be transmitted over a computer network to a computing device of the target entity.
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公开(公告)号：US20210233119A1
公开(公告)日：2021-07-29
申请号：US16774090
申请日：2020-01-28
Applicant: Microsoft Technology Licensing, LLC
Inventor： Zhiyuan Xu , Jinyun Yan , Shaunak Chatterjee
Abstract: Techniques for using a machine-learned model to personalize content item density. In one technique, an entity that is associated with a content request is identified. Multiple sets of content items are identified that includes content items of different types. A first position of a first slot is determined in a content item feed that comprises multiple slots. A second position of a previous content item is determined, in the content item feed, that is of a first type. A difference between the first position and the second position is determined. Based on the difference, a gap sensitivity value that is associated with the entity and is different than the difference is determined. Based on the gap sensitivity value, a content item from the multiple sets of content items is selected and inserted into the first slot. The content item feed is transmitted to a computing device to be presented thereon.
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公开(公告)号：US11321741B2
公开(公告)日：2022-05-03
申请号：US16774090
申请日：2020-01-28
Applicant: Microsoft Technology Licensing, LLC
Inventor： Zhiyuan Xu , Jinyun Yan , Shaunak Chatterjee
Abstract: Techniques for using a machine-learned model to personalize content item density. In one technique, an entity that is associated with a content request is identified. Multiple sets of content items are identified that includes content items of different types. A first position of a first slot is determined in a content item feed that comprises multiple slots. A second position of a previous content item is determined, in the content item feed, that is of a first type. A difference between the first position and the second position is determined. Based on the difference, a gap sensitivity value that is associated with the entity and is different than the difference is determined. Based on the gap sensitivity value, a content item from the multiple sets of content items is selected and inserted into the first slot. The content item feed is transmitted to a computing device to be presented thereon.
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公开(公告)号：US20210065064A1
公开(公告)日：2021-03-04
申请号：US16557823
申请日：2019-08-30
Applicant: Microsoft Technology Licensing, LLC
Inventor： Zhiyuan Xu , Jinyun Yan , Kinjal Basu , Revant Kumar , Onkar A. Dalal
Abstract: Techniques are provided for automatically tuning a parameter in a layered model framework. One or more machine learning techniques are used to train multiple versions of a first model that includes a first version and a second version. A second model is stored that includes a parameter and accepts, as input, output from the first model. Multiple parameter values of the parameter are tested when processing content requests using the first and second versions of the first model. A strict subset of the plurality of parameter values are selected for the parameter of the second model, such that processing a first subset of the content requests using the first version of the first model results in a first value of a particular metric that matches a second value of the particular metric resulting from processing a second subset of the content requests using the second version of the first model.
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