Patents by Inventor Junrui XU

Junrui XU 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).

  • Patent number: 11797619
    Abstract: In an example embodiment, a first machine learned model is trained to produce output, and a second machine learned model is then trained using training data that has been labeled, at least partially, using the output of the first machine learned model. The first machine learned model is trained to output a measure of how strong a positive signal in the training data really is. Specifically, this measure indicates the level of intention of a user who has engaged in a first user interface action with respect to a piece of content to engage in a subsequent second user interface action with the same piece of content.
    Type: Grant
    Filed: April 2, 2020
    Date of Patent: October 24, 2023
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Qing Duan, Junrui Xu, Huichao Xue, Jianqiang Shen
  • Patent number: 11556841
    Abstract: Technologies for generating a graph containing clusters of feature attribute values for training a machine learning model for content item selection and delivery are provided. The disclosed techniques include, for each entity, of a plurality of entities, a system identifies transitions from one geographic location to another geographic location. A graph is generated based on the transitions associated with each entity. The graph comprises nodes representing geographic locations and edges connecting the nodes. Each of the edges connects two nodes, represents a transition from one geographic location to another geographic location, and each edge represents an edge weight value that is based on frequencies of transitions between geographic locations represented by the two connected nodes. The system generates a plurality of clusters from the nodes based upon the edge weight value of each edge. The system includes the plurality of clusters as features in a machine learning model.
    Type: Grant
    Filed: June 20, 2019
    Date of Patent: January 17, 2023
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Qing Duan, Xiaowen Zhang, Xiaoqing Wang, Junrui Xu
  • Publication number: 20220275199
    Abstract: A polyester resin composition is obtained by compounding at least: (A) 100 parts by mass of a polybutylene terephthalate resin, (B) 15 to 100 parts by mass of an amorphous resin and (C) 0.01 to 5 parts by mass of an epoxy resin with a special structure, and the melting point of the polyester resin composition is greater than 210° C. but less than 221° C. The polyester resin composition and the molded product thereof have excellent laser transmittance and can be used for various electrical mounting components for automobiles, connectors, switch components, relay components.
    Type: Application
    Filed: July 20, 2020
    Publication date: September 1, 2022
    Inventors: Xianwen Tang, Junrui Xu, Lezhen Zhang, Koya Kato, Kenji Ota, Akitoshi Omayu, Wenbo Zhu
  • Patent number: 11397899
    Abstract: In some embodiments, a computer system selects a first subset of candidate content items based on their filter scores that are generated based on a partial generalized linear mixed model comprising a baseline model and a user-based model, with the baseline model being a generalized linear model, and the user-based model being a random effects model based on user actions by the target user directed towards reference content items related to the candidate content items. In some embodiments, the computer system then selects a second subset from the first subset based on recommendation scores that are generated based on a full generalized linear mixed model comprising the baseline model, the user-based model, and an item-based model, with the item-based model being a random effects model based on user actions directed towards the candidate online content item by reference users related to the target user.
    Type: Grant
    Filed: March 26, 2019
    Date of Patent: July 26, 2022
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Huichao Xue, Girish Kathalagiri Somashekariah, Ye Yuan, Varun Mithal, Junrui Xu, Ada Cheuk Ying Yu
  • Patent number: 11194877
    Abstract: In an example the output of a machine learned model is a score is then compared to a threshold, and if the score transgresses the threshold, the corresponding item is available to be recommended to the user via the graphical user interface. In an example embodiment, rather than a fixed (static) threshold, a dynamic threshold is utilized. This dynamic threshold is based on a harmonic mean of probabilities utilized in the GLMix model. Specifically, the GLMix model may calculate and utilize the probability that a user will engage with a particular item via a graphical user interface, and also a probability that a user will dismiss a particular item via a graphical user interface.
    Type: Grant
    Filed: October 30, 2019
    Date of Patent: December 7, 2021
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Xiaowen Zhang, Qing Duan, Xiaoqing Wang, Junrui Xu
  • Publication number: 20210312237
    Abstract: In an example embodiment, a first machine learned model is trained to produce output, and a second machine learned model is then trained using training data that has been labeled, at least partially, using the output of the first machine learned model. The first machine learned model is trained to output a measure of how strong a positive signal in the training data really is. Specifically, this measure indicates the level of intention of a user who has engaged in a first user interface action with respect to a piece of content to engage in a subsequent second user interface action with the same piece of content.
    Type: Application
    Filed: April 2, 2020
    Publication date: October 7, 2021
    Inventors: Qing Duan, Junrui Xu, Huichao Xue, Jianqiang Shen
  • Publication number: 20210192460
    Abstract: Technologies for leveraging machine learning techniques to present content items to an entity based upon prior interaction history of the entity are provided. The disclosed techniques include identifying a first plurality of content items with which the entity has interacted during prior entity sessions. Interactions include selecting, viewing, or dismissing content items during prior entity sessions. For each content item in the first plurality, a learned embedding is identified, where each of the embeddings represent a vector of content item features mapped in a vector space. An aggregated embedding is generated based on the identified embeddings. A comparison is performed between the aggregated embedding and embeddings corresponding to a second plurality of content items. Based on the comparison, a subset of content items from the second plurality of content items is identified. The subset of content items is then presented on a computing device of the entity.
    Type: Application
    Filed: December 24, 2019
    Publication date: June 24, 2021
    Inventors: Junrui Xu, Qing Duan, Xiaowen Zhang, Xiaoqing Wang, Benjamin Le, Aman Grover
  • Publication number: 20210133266
    Abstract: In an example the output of a machine learned model is a score is then compared to a threshold, and if the score transgresses the threshold, the corresponding item is available to be recommended to the user via the graphical user interface. In an example embodiment, rather than a fixed (static) threshold, a dynamic threshold is utilized. This dynamic threshold is based on a harmonic mean of probabilities utilized in the GLMix model. Specifically, the GLMix model may calculate and utilize the probability that a user will engage with a particular item via a graphical user interface, and also a probability that a user will dismiss a particular item via a graphical user interface.
    Type: Application
    Filed: October 30, 2019
    Publication date: May 6, 2021
    Inventors: Xiaowen Zhang, Qing Duan, Xiaoqing Wang, Junrui Xu
  • Publication number: 20200401931
    Abstract: Technologies for generating a graph containing clusters of feature attribute values for training a machine learning model for content item selection and delivery are provided. The disclosed techniques include, for each entity, of a plurality of entities, a system identifies transitions from one geographic location to another geographic location. A graph is generated based on the transitions associated with each entity. The graph comprises nodes representing geographic locations and edges connecting the nodes. Each of the edges connects two nodes, represents a transition from one geographic location to another geographic location, and each edge represents an edge weight value that is based on frequencies of transitions between geographic locations represented by the two connected nodes. The system generates a plurality of clusters from the nodes based upon the edge weight value of each edge. The system includes the plurality of clusters as features in a machine learning model.
    Type: Application
    Filed: June 20, 2019
    Publication date: December 24, 2020
    Inventors: Qing Duan, Xiaowen Zhang, Xiaoqing Wang, Junrui Xu
  • Publication number: 20200311568
    Abstract: In some embodiments, a computer system selects a first subset of candidate content items based on their filter scores that are generated based on a partial generalized linear mixed model comprising a baseline model and a user-based model, with the baseline model being a generalized linear model, and the user-based model being a random effects model based on user actions by the target user directed towards reference content items related to the candidate content items. In some embodiments, the computer system then selects a second subset from the first subset based on recommendation scores that are generated based on a full generalized linear mixed model comprising the baseline model, the user-based model, and an item-based model, with the item-based model being a random effects model based on user actions directed towards the candidate online content item by reference users related to the target user.
    Type: Application
    Filed: March 26, 2019
    Publication date: October 1, 2020
    Inventors: Huichao Xue, Girish Kathalagiri Somashekariah, Ye Yuan, Varun Mithal, Junrui Xu, Ada Cheuk Ying Yu
  • Publication number: 20200311162
    Abstract: The disclosed embodiments provide a system for selecting recommendations based on title transition embeddings. During operation, the system obtains a word embedding model of a set of job histories. Next, the system calculates similarities between pairs of the embeddings produced by the word embedding model from attributes associated with titles in the set of job histories. The system then identifies, based on the similarities, job titles with high similarity to a current title of the candidate. Finally, the system outputs the job titles for use in selecting job recommendations for the candidate.
    Type: Application
    Filed: March 28, 2019
    Publication date: October 1, 2020
    Applicant: Microsoft Technology Licensing, LLC
    Inventors: Junrui Xu, Meng Meng, Girish Kathalagiri Somashekariah, Huichao Xue, Varun Mithal, Ada Cheuk Ying Yu
  • Publication number: 20200311157
    Abstract: In some embodiments, a computer system determines that online postings belong to a cohort based on the postings having an attribute of the cohort, identifies skills from the postings, determines that a user belongs to the cohort based on a determination that a profile of the user includes the attribute(s) of the cohort, determines that one or more of the skills is stored in association with the profile, determines a user confidence score that indicates a relevance level of the skill to the user for each one of the one or more of the skills, determines a cohort confidence score for each one of the one or more of the skills based on how many of the postings include the skill, and displays a recommendation associated based on a combination of the user confidence score and the cohort confidence score for at least a portion of the skills.
    Type: Application
    Filed: March 28, 2019
    Publication date: October 1, 2020
    Inventors: Ye Yuan, Girish Kathalagiri Somashekariah, Huichao Xue, Varun Mithal, Ada Cheuk Ying Yu, Junrui Xu
  • Patent number: 10454300
    Abstract: An Uninterrupted Power Supply (UPS) power supply system is provided, which includes: a filter module connected to a mains supply and configured to filter an input from the mains supply; a BOOST module, where a first terminal of the BOOST module is connected to the filter module via a first switch group and connected to a battery via a second switch group, and a second terminal of the BOOST module is connected to a positive bus and a negative bus; and a bi-directional Direct Current/Direct Current (DC/DC) module, where a first terminal of the bi-directional DC/DC module is connected to the battery, and a second terminal of the bi-directional DC/DC module is connected to the positive bus and the negative bus.
    Type: Grant
    Filed: May 23, 2017
    Date of Patent: October 22, 2019
    Assignee: Vertiv Tech Co., Ltd.
    Inventors: Junrui Xu, Yunhe Mao, Fubin Xu, Yang Bing
  • Publication number: 20170373531
    Abstract: An Uninterrupted Power Supply (UPS) power supply system is provided, which includes: a filter module connected to a mains supply and configured to filter an input from the mains supply; a BOOST module, where a first terminal of the BOOST module is connected to the filter module via a first switch group and connected to a battery via a second switch group, and a second terminal of the BOOST module is connected to a positive bus and a negative bus; and a bi-directional Direct Current/Direct Current (DC/DC) module, where a first terminal of the bi-directional DC/DC module is connected to the battery, and a second terminal of the bi-directional DC/DC module is connected to the positive bus and the negative bus.
    Type: Application
    Filed: May 23, 2017
    Publication date: December 28, 2017
    Applicant: Emerson Network Power Co., Ltd.
    Inventors: Junrui XU, Yunhe MAO, Fubin XU, Yang BING