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公开(公告)号：US20230196070A1
公开(公告)日：2023-06-22
申请号：US17556218
申请日：2021-12-20
Applicant: Microsoft Technology Licensing, LLC
Inventor： Yuan Sun , Ye Tu , Ying Han , Chun Lo , Shaunak Chatterjee , Vrishti Gulati
CPC classification number: G06N3/0454 , G06N20/20 , G06N3/0472
Abstract: In an example embodiment, a separate mimicry machine-learned model is trained for each of a plurality of different item types. Each of these models is trained to estimate an effect of mimicry for a user (i.e., a user whose user profile or other information is passed to the corresponding mimicry machine-learned model at prediction-time). The output of these models may be either used on its own to perform various actions, such as modifying a location of a user interface element of a user interface, or may be passed as input to an interaction machine-learned model that is trained to determine a likelihood of a user (i.e., a user whose user profile or other information is passed to the interaction machine-learned model at prediction-time) interacting with a particular item, such as a potential feed item.
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公开(公告)号：US11657371B2
公开(公告)日：2023-05-23
申请号：US17341687
申请日：2021-06-08
Applicant: Microsoft Technology Licensing, LLC
Inventor： Hongche Liu , Divya Venugopalan , Shaunak Chatterjee
IPC: G06Q10/10 , G06Q10/1053 , G06N20/00 , G06F16/901
CPC classification number: G06Q10/1053 , G06F16/9024 , G06N20/00
Abstract: A machine for improving content delivery generates a graph representing a personalized conversational flow for sequenced delivery of digital content. The graph includes nodes representing interactive dialogues between a machine and a user, and edges that connect the nodes. The machine causes display of a user interface including a prompt related to job-seeking guidance. The machine, based on a first action in response to the prompt, dynamically adjusts the graph, the dynamic adjusting including selecting a first node. The machine generates and causes display of a first incentive content item, and a first call-to-action content item. The machine, in response to a second action received in response to the first call-to action content item, dynamically selects an edge connecting the first node and a further node. The dynamic selecting of the edge results in display of a further incentive content item, and a further call-to-action content item.
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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：US20200311747A1
公开(公告)日：2020-10-01
申请号：US16370127
申请日：2019-03-29
Applicant: Microsoft Technology Licensing, LLC
Inventor： Yunbo Ouyang , Kinjal Basu , Viral Gupta , Shaunak Chatterjee
Abstract: Techniques for automatically identifying a primary objective for a multi-objective optimization problem are provided. In one technique, an experiment is conduct and results of the experiment involving different values of a model parameter are tracked and stored. Multiple metrics are generated based on the results. For each metric, a maximum or minimum value of the metric given a particular value of the model parameter is determined and a variance associated with the metric is determined based on the maximum or minimum value. A metric that is associated with the lowest variance among the multiple metrics is identified. The identified metric is used as a primary metric in a multi-objective optimization problem.
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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：US10460402B2
公开(公告)日：2019-10-29
申请号：US15488137
申请日：2017-04-14
Applicant: Microsoft Technology Licensing, LLC
Inventor： Ankan Saha , Shaunak Chatterjee , Kinjal Basu
IPC: G06Q50/00 , G06F16/9535 , G06N5/00 , G06N20/00
Abstract: A system, a machine-readable storage medium storing instructions, and a computer-implemented method are described herein are directed to Large Scale Optimizing Engine. The Large Scale Optimizing Engine determines a probability, for each content item in a set of content items, of the respective member account performing a content item action. The Large Scale Optimizing Engine identifies a select content item from the set of content items based on determining display of the select content item will meet a first and second target. The Large Scale Optimizing Engine causes display of the select content item in a content slot in the respective member account's social network feed based on satisfaction of the first and second targets.
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公开(公告)号：US10380624B2
公开(公告)日：2019-08-13
申请号：US14585863
申请日：2014-12-30
Applicant: Microsoft Technology Licensing, LLC
Inventor： Deepak Agarwal , Shaunak Chatterjee , Liang Zhang , Bee-Chung Chen , Yang Yang
Abstract: This disclosure relates to systems and methods that include a member activity database including data indicative of interactions with content items on a social network by a population of users of the social network. A processor is configured to obtain an optimization criterion based on at least two constraints related to a performance of the social network, obtain, for a subset of the population of users, at least some of the data indicative of interactions with content items from the member activity database, determine, based on the at least some of the data as obtained, an operating condition for the social network that is estimated to meet the optimization criterion, and provide, to at least some of the user devices via the network interface, the social network based, at least in part, on the operating condition.
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公开(公告)号：US10341445B2
公开(公告)日：2019-07-02
申请号：US15223207
申请日：2016-07-29
Applicant: Microsoft Technology Licensing, LLC.
Inventor： Aastha Jain , Shaunak Chatterjee
Abstract: This disclosure relates to systems and methods for identifying members that increase engagement at an online social network. In one example, a method includes retrieving network connectivity and member interaction data for members of an online social networking service that includes a plurality of explicit social networks, building statistical correlations between properties of the respective explicit social networks and interactions between members of the respective explicit social networks, and ranking a set of potential new members for one of the explicit social networks according to the statistical correlations and a statistical likelihood that the new members will increase member interactions with the explicit social network.
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公开(公告)号：US20190130296A1
公开(公告)日：2019-05-02
申请号：US15794872
申请日：2017-10-26
Applicant: Microsoft Technology Licensing, LLC
Inventor： Kinjal Basu , Shaunak Chatterjee , Ankan Saha
Abstract: A method may include determining a decision space representing a set of content items to be presented on a user interface of a social networking site, the decision space accounting for competing quadratic constraints and interaction effects, estimating the decision space to linearize the competing quadratic constraints, determining, in the estimated decision space and using an objective function, a display probability for each content item in the set of content items, each respective display probability corresponding to a given content item's probability of display in a specific content slot of a plurality of content slots on the user interface; and causing display of the content items with the highest display probabilities.
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公开(公告)号：US10275716B2
公开(公告)日：2019-04-30
申请号：US14814104
申请日：2015-07-30
Applicant: Microsoft Technology Licensing, LLC
Inventor： Guanfeng Liang , Shaunak Chatterjee , Romer Rosales
Abstract: A method and apparatus for populating content items into a feed is provided. The feed comprises a sequence of content item ordered in such a way as to maximize a number of content items displayed to a user by virtue of the user scrolling down through the feed. The content items are each associated with a click-through rate, an indication of a number of times the content has been displayed to users, an indication of a number of times that the users have scrolled to a next item in the feed after the item was displayed, and a height of the content item. These values are used to train a behavioral model and then used by the behavioral model to layout the content items in a feed rendered at a user device.
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