Patents by Inventor How Jing
How Jing has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Patent number: 11604990Abstract: In an example embodiment, a framework to infer a user's value for a particular attribute based upon a multi-task machine learning process with uncertainty weighting that incorporates signals from multiple contexts is provided. In an example embodiment, the framework aims to measure a level of a user attribute under a certain context. Rather than attempting to devise a universal, one-size-fits-all value for the attribute, the framework acknowledges that the user's value for that attribute can vary depending on context and factors in the context under which the user's attribute levels are measured. Multiple contexts are defined depending on different situations where users and entities such as companies and organizations need to evaluate user attribute levels. Signals for attribute levels are then collected for each context. Machine learning models are utilized to estimate attribute values for different contexts. Multi-task deep learning is used to level attributes from different contexts.Type: GrantFiled: June 16, 2020Date of Patent: March 14, 2023Assignee: Microsoft Technology Licensing, LLCInventors: Xiao Yan, Wenjia Ma, Jaewon Yang, Jacob Bollinger, Qi He, Lin Zhu, How Jing
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Publication number: 20210390390Abstract: In an example embodiment, a framework to infer a user's value for a particular attribute based upon a multi-task machine learning process with uncertainty weighting that incorporates signals from multiple contexts is provided. In an example embodiment, the framework aims to measure a level of a user attribute under a certain context. Rather than attempting to devise a universal, one-size-fits-all value for the attribute, the framework acknowledges that the user's value for that attribute can vary depending on context and factors in the context under which the user's attribute levels are measured. Multiple contexts are defined depending on different situations where users and entities such as companies and organizations need to evaluate user attribute levels. Signals for attribute levels are then collected for each context. Machine learning models are utilized to estimate attribute values for different contexts. Multi-task deep learning is used to level attributes from different contexts.Type: ApplicationFiled: June 16, 2020Publication date: December 16, 2021Inventors: Xiao Yan, Wenijia Ma, Jaewon Yang, Jacob Bollinger, Qi He, Lin Zhu, How Jing
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Patent number: 10586157Abstract: In an example embodiment, for each of a plurality of different titles in a social network structure, the title is mapped into a first vector having n coordinates, while kills are mapped into a second vector having n coordinates. The first and second vectors are stored in a deep representation data structure. One or more objective functions are applied to at least one combination of two or more of the vectors in the deep representation data structure. Then, an optimization test on each of the at least one combination is performed using a corresponding objective function output for each of the at least one combination of two or more of the vectors, and, for any combination that did not pass the optimization test, one or more coordinates for the vectors in the combination are altered so that the vectors in the combination become closer together within an n-dimensional space.Type: GrantFiled: November 23, 2016Date of Patent: March 10, 2020Assignee: Microsoft Technology Licensing, LLCInventors: Uri Merhav, How Jing, Jaewon Yang, Dan Shacham
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Patent number: 10459901Abstract: In an example embodiment, for each of a plurality of different entities in a social network structure, the entity is mapped into a vector having n coordinates. The vector for each of the plurality of different entities is stored in a deep representation data structure. One or more objective functions are applied to at least one combination of two or more of the vectors in the deep representation data structure. Then, an optimization test on each of the at least one combination of two or more of the vectors is performed using a corresponding objective function output for each of the at least one combination of two or more of the vectors, and, for any combination that did not pass the optimization test, one or more coordinates for the vectors in the combination are altered so that the vectors in the combination become closer together within an n-dimensional space.Type: GrantFiled: November 23, 2016Date of Patent: October 29, 2019Assignee: Microsoft Technology Licensing, LLCInventors: Uri Merhav, How Jing, Jaewon Yang, Dan Shacham
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Publication number: 20190130281Abstract: Techniques for predicting a next company and next title of a user are disclosed herein. In some embodiments, an encoder is used for encoding a representation of the user's profile. The encoding includes accessing discrete entities comprising context information included in the user's profile, constructing a plurality of embedding vectors from the context information, and generating a context vector from the plurality of embedding vectors. The plurality of embedding vectors including a skill embedding vector, a school embedding vector, and a location embedding vector. A decoder is for decoding a career path from the context vector. The decoding includes applying a long short-term memory (LSTM) model to the context vector to generate perform the prediction of the user's next company and next title for presentation in a user interface.Type: ApplicationFiled: October 31, 2017Publication date: May 2, 2019Inventors: Jaewon Yang, Qi He, How Jing, Bee-Chung Chen, Liangyue Li
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Publication number: 20180144253Abstract: In an example embodiment, for each of a plurality of different titles in a social network structure, the title is mapped into a first vector having n coordinates, while kills are mapped into a second vector having n coordinates. The first and second vectors are stored in a deep representation data structure. One or more objective functions are applied to at least one combination of two or more of the vectors in the deep representation data structure. Then, an optimization test on each of the at least one combination is performed using a corresponding objective function output for each of the at least one combination of two or more of the vectors, and, for any combination that did not pass the optimization test, one or more coordinates for the vectors in the combination are altered so that the vectors in the combination become closer together within an n-dimensional space.Type: ApplicationFiled: November 23, 2016Publication date: May 24, 2018Inventors: Uri Merhav, How Jing, Jaewon Yang, Dan Shacham
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Publication number: 20180144008Abstract: In an example embodiment, for each of a plurality of different entities in a social network structure, the entity is mapped into a vector having n coordinates. The vector for each of the plurality of different entities is stored in a deep representation data structure. One or more objective functions are applied to at least one combination of two or more of the vectors in the deep representation data structure. Then, an optimization test on each of the at least one combination of two or more of the vectors is performed using a corresponding objective function output for each of the at least one combination of two or more of the vectors, and, for any combination that did not pass the optimization test, one or more coordinates for the vectors in the combination are altered so that the vectors in the combination become closer together within an n-dimensional space.Type: ApplicationFiled: November 23, 2016Publication date: May 24, 2018Inventors: Uri Merhav, How Jing, Jaewon Yang, Dan Shacham
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Publication number: 20180089734Abstract: Techniques for suggesting to a first member an endorsement of a skill possessed by a second member are described. In an example, disclosed is a system that accesses a plurality of skills associated with members from a member database, and accesses a reputation score for each skill of each member from a skill reputation database. Moreover, a subset of skills can be selected based on the reputation scores of the first member. Furthermore, a specific skill in which to endorse the second member can be determined from the subset of skills based on the reputation score of the second member for the specific skill. A quality score for the specific skill is calculated. Subsequently, a user interface can present, on a display of a device of the first member, an endorsement suggestion for the specific skill of the second member when the quality score transgresses a predetermined threshold.Type: ApplicationFiled: December 9, 2016Publication date: March 29, 2018Inventors: Albert C. Chen, Yo-Tzu Yeh, Victor Louis Kabdebon, Jaewon Yang, How Jing