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公开(公告)号：US11792167B2
公开(公告)日：2023-10-17
申请号：US17219482
申请日：2021-03-31
Applicant: Microsoft Technology Licensing, LLC
Inventor： Haifeng Zhao , Yang Chen , Jiashuo Wang , Xiaojing Chen , Chencheng Wu , Souvik Ghosh , Ankit Gupta , Jing Wang , John Patrick Moore , Henry Heyburn Pistell , Mira Thambireddy , Haowen Cao , Keyi Yu
CPC classification number: H04L63/0428 , G06N20/00
Abstract: Techniques for a flexible data security and machine learning system for merging third-party data are provided. In one technique, the system receives a data set from a third-party entity and receives selection data that indicates that the third-party entity selected a set of data security policies that includes an encryption option and a data mixing option from among multiple data mixing options. In response to receiving the selection data, the system stores data that associates the set of data security policies with the data set, encrypts the data set according to the encryption option, and persistently stores the encrypted data set. Later, the system decrypts the encrypted data set in volatile memory, generates, based on the data mixing option, training data based on the decrypted version of the data set, trains a machine-learned model based on the training data, and stores the machine-learned model in association with the data set.
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公开(公告)号：US20200380407A1
公开(公告)日：2020-12-03
申请号：US16430243
申请日：2019-06-03
Applicant: Microsoft Technology Licensing, LLC
Inventor： Chengming Jiang , Kinjal Basu , Wei Lu , Souvik Ghosh , Mansi Gupta
Abstract: In an example embodiment, training data is obtained, the training data comprising values for a plurality of different features. Then a global machine learned model is trained using a first machine learning algorithm by feeding the training data into the first machine learning algorithm during a fixed effect training process. A non-linear first random effects machine learned model is trained by feeding a subset of the training data into a second machine learning algorithm, the subset of the training data being limited to training data corresponding to a particular value of one of the plurality of different features.
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公开(公告)号：US20240378425A1
公开(公告)日：2024-11-14
申请号：US18214939
申请日：2023-06-27
Applicant: Microsoft Technology Licensing, LLC
Inventor： Praveen Kumar Bodigutla , Suman Sundaresh , Souvik Ghosh , Saurabh Gupta , Sai Krishna Bollam , Arya Ghatak Choudhury , Weiheng Qian , Jiarui Wang
IPC: G06N3/0455 , G06N3/09
Abstract: Embodiments of the disclosed technologies include receiving first message attribute data and inputting the first message attribute data to a first machine learning model. The first machine learning model is configured to generate and output suggested message content based on first correlations between message content and message acceptance data. The first machine learning model generates a first set of message content suggestions based on the first message attribute data, and selects at least one message content suggestion from the first set of message content suggestions based on message evaluation data. Feedback data related to the selected at least one message content suggestion is received. The first machine learning model is tuned based on the feedback data. The tuned first machine learning model generates a second set of message content suggestions based on the first message attribute data.
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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：US10678997B2
公开(公告)日：2020-06-09
申请号：US15825657
申请日：2017-11-29
Applicant: Microsoft Technology Licensing, LLC
Inventor： Karan Ashok Ahuja , Befekadu Ayenew Ejigou , Ningfeng Liang , Lokesh P. Bajaj , Wei Wang , Paul Fletcher , Wei Lu , Shaunak Chatterjee , Souvik Ghosh , Yang Li , Wei Deng , Qiang Wu
IPC: G06F17/00 , G06F40/174 , G06Q50/00 , G06N20/00 , H04L29/08
Abstract: In an example, first and second machine learned models corresponding to a particular context of a social networking service are obtained, the first machine learned model trained via a first machine learning algorithm to output an indication of importance of a social networking profile field to obtaining results in the particular context, and the second machine learned model trained via a second machine learning algorithm to output a propensity of the user to edit a social networking profile field if requested. One or more missing fields in a social networking profile for the user are identified. For each of one or more of the one or more missing fields, the field and an identification of the user are passed through the first and second machine learned models, and outputs of the first and second machine learned models are combined to identify one or more top missing profile fields.
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Patent Agency Ranking
公开(公告)号：US20230351247A1
公开(公告)日：2023-11-02
申请号：US17735020
申请日：2022-05-02
Applicant: Microsoft Technology Licensing, LLC
Inventor： Boyi Chen , Tong Zhou , Siyao Sun , Lijun Peng , Xinruo Jing , Vakwadi Thejaswini Holla , Yi Wu , Pankhuri Goyal , Souvik Ghosh , Zheng Li , Yi Zhang , Onkar A. Dalal , Jing Wang , Aarthi Jayaram
IPC: G06N20/00
CPC classification number: G06N20/00
Abstract: Embodiments of the disclosed technologies receive a first-party trained model and a first-party data set from a first-party system into a protected environment, receive a first third-party data set into the protected environment, and, in a data clean room, joining the first-party data set and the first third-party data set to create a joint data set for the particular segment, tuning a first-party trained model with the joint data set to create a third-party tuned model, sending model parameter data learned in the data clean room as a result of the tuning to an aggregator node, receiving a globally tuned version of the first-party trained model from the aggregator node, applying the globally tuned version of the first-party trained model to a second third-party data set to produce a scored third-party data set, and providing the scored third-party data set to a content distribution service of the first-party system.
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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：US10728313B2
公开(公告)日：2020-07-28
申请号：US15488159
申请日：2017-04-14
Applicant: Microsoft Technology Licensing, LLC
Inventor： Aastha Jain , Shilpa Gupta , Myunghwan Kim , Shaunak Chatterjee , Hema Raghavan , Souvik Ghosh
Abstract: A system, a machine-readable storage medium storing instructions, and a computer-implemented method are described herein are directed to Future Connection Engine that generates a select pairing of member accounts for a potential social network connection. The Future Connection Engine predicts, according to the prediction model, a first number of subsequent social network connections for a first member account in the select pairing that will occur after establishing the potential social network connection and a second number of subsequent social network connections for a second member account in the select pairing that will occur after establishing the potential social network connection. The Future Connection Engine generates connection recommendations for display to the select pairing based on whether the first and/or the second number of subsequent social network connections satisfies a threshold.
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公开(公告)号：US20200005354A1
公开(公告)日：2020-01-02
申请号：US16024753
申请日：2018-06-30
Applicant: Microsoft Technology Licensing, LLC
Inventor： Rupesh Gupta , Guangde Chen , Curtis Chung-Yen Wang , Deepak K. Agarwal , Souvik Ghosh , Shipeng Yu
Abstract: Machine learning techniques for multi-objective content item selection are provided. In one technique, resource allocation data is stored that indicates, for each campaign of multiple campaigns, a resource allocation amount that is assigned by a central authority. In response to receiving the content request, a subset of the campaigns is identified based on targeting criteria. Multiple scores are generated, each score reflecting a likelihood that a content item of the corresponding campaign will be selected. Based on the scores, a particular campaign from the subset is selected and the corresponding content item transmitted over a computer network to be displayed on a computing device. A resource allocation amount that is associated with the particular campaign is identified. A resource reduction amount associated with displaying the content item of the particular campaign is determined. The particular resource allocation is reduced based on the resource reduction amount.
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公开(公告)号：US20190333162A1
公开(公告)日：2019-10-31
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 , G06F17/30 , G06F3/0482 , G06F15/18 , G06F17/18
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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