Patents by Inventor Xu Miao

Xu Miao 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).

  • Publication number: 20200184272
    Abstract: One embodiment of the present invention sets forth a technique for managing machine learning. The technique includes organizing a set of reusable components for performing machine learning under a framework. The technique also includes representing, within the framework, a machine learning model as a graph-based structure that includes nodes representing a subset of the reusable components and edges representing input-output relationships between pairs of the nodes. The technique further includes validating the machine learning model based on inputs and outputs associated with the nodes and the input-output relationships represented by the edges in the graph-based structure. Finally, the technique includes generating the machine learning model according to the graph-based structure and configurations for the subset of the reusable components.
    Type: Application
    Filed: December 7, 2018
    Publication date: June 11, 2020
    Inventors: Zhenjie ZHANG, Karan SAMEL, Xu MIAO, Maram NAGENDRAPRASAD, Ankit ARYA, Adil MOHAMMED, Baiji HE, Masayo IIDA
  • Patent number: 10643558
    Abstract: A driving method of display panel, a display panel and a display device are disclosed. The driving method includes: in a single-frame display time, sequentially applying signals to a plurality of first sub-pixels connected to first data lines in a scanning direction so that: a signal polarity applied to each of a plurality of first white sub-pixels connected to first data lines is opposite to a signal polarity applied to a first sub-pixel which is located at an upstream of the first white sub-pixel along the scanning direction and is adjacent to the first white sub-pixel, and a signal polarity applied to each of a plurality of first colored sub-pixels is identical with a signal polarity applied to a first sub-pixel which is located at an upstream of the first colored sub-pixel along the scanning direction and is adjacent to the first colored sub-pixel.
    Type: Grant
    Filed: February 24, 2017
    Date of Patent: May 5, 2020
    Assignees: BOE TECHNOLOGY GROUP CO., LTD., BEIJING BOE DISPLAY TECHNOLOGY CO., LTD.
    Inventors: Guohuo Su, Zhihua Sun, Yujie Gao, Shulin Yao, Baoyu Liu, Xu Zhang, Weichao Ma, Zhihao Zhang, Wenkai Mu, Yingmeng Miao, Guangquan He
  • Publication number: 20200135887
    Abstract: A method includes forming a dummy gate structure over a substrate, forming a plurality of gate spacers respectively on opposite sidewalls of the dummy gate structure and having a first dielectric constant, removing the dummy gate structure to form a gate trench between the gate spacers, forming a dopant source layer to line the gate trench, annealing the dopant source layer to diffuse k-value reduction impurities from the dopant source layer into the gate spacers to lower the first dielectric constant of the gate spacers to a second dielectric constant, and forming a replacement gate stack in the gate trench.
    Type: Application
    Filed: October 3, 2019
    Publication date: April 30, 2020
    Applicant: TAIWAN SEMICONDUCTOR MANUFACTURING CO., LTD.
    Inventors: Xu-Sheng WU, Chang-Miao LIU, Hui-Ling SHANG
  • Patent number: 10586169
    Abstract: The disclosed embodiments provide a system for processing data. During operation, the system obtains a hierarchical representation containing a set of namespaces of a set of features shared by a set of statistical models. Next, the system uses the hierarchical representation to obtain, from one or more execution environments, a subset of the features for use in calculating the derived feature. The system then applies a formula from the hierarchical representation to the subset of the features to produce the derived feature. Finally, the system provides the derived feature for use by one or more of the statistical models.
    Type: Grant
    Filed: February 17, 2016
    Date of Patent: March 10, 2020
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: David J. Stein, Xu Miao, Lance M. Wall, Joel D. Young, Eric Huang, Songxiang Gu, Da Teng, Chang-Ming Tsai, Sumit Rangwala
  • Patent number: 10586167
    Abstract: The disclosed embodiments provide a method and system for performing regularized model adaptation for in-session recommendations. During operation, the system obtains, from a server, a first global version of a statistical model. During a first user session with a user, the system improves a performance of the statistical model by using the first global version to output one or more recommendations to the user and using the first global version and user feedback from the user to create a first personalized version of the statistical model. At an end of the first user session, the system transmits an update containing a difference between the first personalized version and the first global version to the server for use in producing a second global version of the statistical model by the server.
    Type: Grant
    Filed: September 24, 2015
    Date of Patent: March 10, 2020
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Xu Miao, Yitong Zhou, Joel D. Young, Lijun Tang, Anmol Bhasin
  • Publication number: 20190354810
    Abstract: One embodiment of the present invention sets forth a technique for processing training data for a machine learning model. The technique includes training the machine learning model using training data comprising a set of features and a set of original labels associated with the set of features. The technique also includes generating multiple groupings of the training data based on internal representations of the training data in the machine learning model. The technique further includes replacing, in a first subset of groupings of the training data, a first subset of the original labels with updated labels based at least on occurrences of values for the original labels in the first subset of groupings.
    Type: Application
    Filed: May 21, 2019
    Publication date: November 21, 2019
    Inventors: Karan SAMEL, Xu MIAO, Zhenjie Zhang, Masayo IIDA, Maran NAGENDRAPRASAD
  • Publication number: 20190342800
    Abstract: Embodiments of a Next Generation Node-B (gNB) and methods of communication are generally described herein. The gNB may be configurable to operate as a source gNB. The gNB may be configured with logical nodes including a gNB central unit (gNB-CU) and a gNB distributed unit (gNB-DU). The gNB-CU may comprise a gNB-CU control plane (gNB-CU-CP) for control-plane functionality, and a gNB-CU user plane (gNB-CU-UP) for user-plane functionality. When a handover of a User Equipment (UE) from the source gNB to a target gNB is performed, the gNB may transfer, from the gNB-DU to the gNB-CU-UP, a downlink data delivery status (DDDS) message to indicate that the gNB-CU-UP is to stop transfer, to the gNB-DU, of downlink data intended for the UE.
    Type: Application
    Filed: May 22, 2019
    Publication date: November 7, 2019
    Inventors: Alexander Sirotkin, Xu Zhang, Jaemin Han, Feng Yang, Honglei Miao, Jerome Parron, Markus Dominik Mueck, Jing Zhu, Ellen Liao, Jeongho Jeon, Youn Hyoung Heo, Anthony Lee, Seau S. Lim, Marta Martinez Tarradelll, Meghashree Dattatri Kedalagudde, Puneet Jain, Bharat Shrestha
  • Patent number: 10380500
    Abstract: A system and method for managing asynchronously receiving updates and merging updates into global versions of a statistical model using version control are disclosed. During operation, the system transmits a first global version of a statistical model to a set of client computer systems. Next, the system obtains, from a first subset of the client computer systems, a first set of updates to the first global version. The system then merges the first set of updates into a second global version of the statistical model. Finally, the system transmits the second global version to the client computer systems asynchronously from receiving a second set of updates to the first and/or second global versions from a second subset of the client computer systems.
    Type: Grant
    Filed: September 24, 2015
    Date of Patent: August 13, 2019
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Xu Miao, Yitong Zhou, Joel D. Young, Lijun Tang, Anmol Bhasin
  • Publication number: 20180285759
    Abstract: The disclosed embodiments provide a system for performing online hyperparameter tuning in distributed machine learning. During operation, the system uses input data for a first set of versions of a statistical model for a set of entities to calculate a batch of performance metrics for the first set of versions. Next, the system applies an optimization technique to the batch to produce updates to a set of hyperparameters for the statistical model. The system then uses the updates to modulate the execution of a second set of versions of the statistical model for the set of entities. When a new entity is added to the set of entities, the system updates the set of hyperparameters with a new dimension for the new entity.
    Type: Application
    Filed: April 3, 2017
    Publication date: October 4, 2018
    Applicant: LinkedIn Corporation
    Inventors: Ian B. Wood, Xu Miao, Chang-Ming Tsai, Joel D. Young
  • Patent number: 10048749
    Abstract: Examples are disclosed herein that relate to gaze tracking. One example provides a computing device including an eye-tracking system including an image sensor, a logic device, and a storage device comprising instructions executable by the logic device to track an eye gaze direction by acquiring an image of the eye via the eye-tracking system, and determining a determined location of a center of a lens of the eye from the image of the eye. The instructions are further executable to adjust the determined location of the center of the lens on a sub-pixel scale by applying a predetermined sub-pixel offset to the determined location of the center of the lens to produce an adjusted location of the center of the lens, to determine a gaze direction from the adjusted location of the center of the lens, and perform an action on a computing device based on the gaze direction.
    Type: Grant
    Filed: January 9, 2015
    Date of Patent: August 14, 2018
    Assignee: MICROSOFT TECHNOLOGY LICENSING, LLC
    Inventors: Xu Miao, Michael J. Conrad, Dijia Wu
  • Patent number: 9864430
    Abstract: Examples are disclosed herein that are related to gaze tracking via image data. One example provides, on a gaze tracking system comprising an image sensor, a method of determining a gaze direction, the method comprising acquiring image data via the image sensor, detecting in the image data facial features of a human subject, determining an eye rotation center based upon the facial features using a calibrated face model, determining an estimated position of a center of a lens of an eye from the image data, determining an optical axis based upon the eye rotation center and the estimated position of the center of the lens, determining a visual axis by applying an adjustment to the optical axis, determining the gaze direction based upon the visual axis, and providing an output based upon the gaze direction.
    Type: Grant
    Filed: January 9, 2015
    Date of Patent: January 9, 2018
    Assignee: MICROSOFT TECHNOLOGY LICENSING, LLC
    Inventors: Dijia Wu, Michael J. Conrad, Tim Burrell, Xu Miao, Zicheng Liu, Qin Cai, Zhengyou Zhang
  • Publication number: 20170221007
    Abstract: Learning to rank modeling in the context of an on-line social network is described. A learning to rank model can learn from pairwise preference (e.g., job posting A is more relevant than job posting B for a particular member profile) thus directly optimizing for the rank order of job postings for each member profile. With ranking position taken into consideration during training, top-ranked job postings may be treated by a recommendation system as being of more importance than lower-ranked job postings. In addition, a learning to rank approach may also result in an equal optimization across all member profiles and help minimize bias towards those member profiles that have been paired with a larger number of job postings.
    Type: Application
    Filed: April 13, 2017
    Publication date: August 3, 2017
    Inventors: Lijun Tang, Eric Huang, Xu Miao, Yitong Zhou, David Hardtke, Joel Daniel Young
  • Publication number: 20170109652
    Abstract: The disclosed embodiments provide a system for processing data. During operation, the system obtains a hierarchical representation containing a set of namespaces of a set of features shared by a set of statistical models. Next, the system uses the hierarchical representation to obtain, from one or more execution environments, a subset of the features for use in calculating the derived feature. The system then applies a formula from the hierarchical representation to the subset of the features to produce the derived feature. Finally, the system provides the derived feature for use by one or more of the statistical models.
    Type: Application
    Filed: February 17, 2016
    Publication date: April 20, 2017
    Applicant: LinkedIn Corporation
    Inventors: David J. Stein, Xu Miao, Lance M. Wall, Joel D. Young, Eric Huang, Songxiang Gu, Da Teng, Chang-Ming Tsai, Sumit Rangwala
  • Patent number: 9626654
    Abstract: Learning to rank modeling in the context of an on-line social network is described. A learning to rank model can learn from pairwise preference (e.g., job posting A is more relevant than job posting B for a particular member profile) thus directly optimizing for the rank order of job postings for each member profile. With ranking position taken into consideration during training, top-ranked job postings may be treated by a recommendation system as being of more importance than lower-ranked job postings. In addition, a learning to rank approach may also result in an equal optimization across all member profiles and help minimize bias towards those member profiles that have been paired with a larger number of job postings.
    Type: Grant
    Filed: June 30, 2015
    Date of Patent: April 18, 2017
    Assignee: LinkedIn Corporation
    Inventors: Lijun Tang, Eric Huang, Xu Miao, Yitong Zhou, David Hardtke, Joel Daniel Young
  • Publication number: 20170091651
    Abstract: The disclosed embodiments provide a system and method for performing version control for asynchronous distributed machine learning. During operation, the system transmits a first global version of a statistical model to a set of client computer systems. Next, the system obtains, from a first subset of the client computer systems, a first set of updates to the first global version. The system then merges the first set of updates into a second global version of the statistical model. Finally, the system transmits the second global version to the client computer systems asynchronously from receiving a second set of updates to the first and/or second global versions from a second subset of the client computer systems.
    Type: Application
    Filed: September 24, 2015
    Publication date: March 30, 2017
    Applicant: LinkedIn Corporation
    Inventors: Xu Miao, Yitong Zhou, Joel D. Young, Lijun Tang, Anmol Bhasin
  • Publication number: 20170091652
    Abstract: The disclosed embodiments provide a method and system for performing regularized model adaptation for in-session recommendations. During operation, the system obtains, from a server, a first global version of a statistical model. During a first user session with a user, the system improves a performance of the statistical model by using the first global version to output one or more recommendations to the user and using the first global version and user feedback from the user to create a first personalized version of the statistical model. At an end of the first user session, the system transmits an update containing a difference between the first personalized version and the first global version to the server for use in producing a second global version of the statistical model by the server.
    Type: Application
    Filed: September 24, 2015
    Publication date: March 30, 2017
    Applicant: LINKEDIN CORPORATION
    Inventors: Xu Miao, Yitong Zhou, Joel D. Young, Lijun Tang, Anmol Bhasin
  • Publication number: 20170004455
    Abstract: Nonlinear featurization of decision trees for linear regression modeling in the context of an on-line social network is described. A computer-implemented converter is provided that is capable of reading a decision tree structure that is included in the learning to rank algorithm and convert each path from root to a leaf into an s-expression. The s-expressions are used as additional features to train a logistic regression model.
    Type: Application
    Filed: June 30, 2015
    Publication date: January 5, 2017
    Inventors: Lijun Tang, Eric Huang, Xu Miao, Yitong Zhou, David Hardtke, Jeol Daniel Young
  • Publication number: 20170004454
    Abstract: Learning to rank modeling in the context of an on-line social network is described. A learning to rank model can learn from pairwise preference (e.g., job posting A is more relevant than job posting B for a particular member profile) thus directly optimizing for the rank order of job postings for each member profile. With ranking position taken into consideration during training, top-ranked job postings may be treated by a recommendation system as being of more importance than lower-ranked job postings. In addition, a learning to rank approach may also result in an equal optimization across all member profiles and help minimize bias towards those member profiles that have been paired with a larger number of job postings.
    Type: Application
    Filed: June 30, 2015
    Publication date: January 5, 2017
    Inventors: Lijun Tang, Eric Huang, Xu Miao, Yitong Zhou, David Hardtke, Joel Daniel Young
  • Patent number: 9452989
    Abstract: Methods, compositions, and systems for detecting gamma radiation is disclosed and described. A compound for detecting gamma radiation can comprise a conjugated imidazole having the following structure: [Formula I] where at least one of R1, R2, and R3 are conjugated organic groups. Additionally, the conjugated imidazole can be capable of reacting with a radical or ion formed by the reaction of gamma radiation with a radical generating component such as a halogen solvent to decrease a molar extinction coefficient of the conjugated imidazole in the visible light region or to quench fluorescence of the conjugated imidazole. As a sensor (100), a radiation detection indicator (108) can indicate the change in molar extinction coefficient or fluorescence of the conjugated imidazole material (120) upon exposure to gamma radiation.
    Type: Grant
    Filed: May 24, 2013
    Date of Patent: September 27, 2016
    Assignee: University of Utah Research Foundation
    Inventors: Ling Zang, Jimin Han, Xu Miao
  • Publication number: 20160202757
    Abstract: Examples are disclosed herein that relate to gaze tracking. One example provides a computing device including an eye-tracking system including an image sensor, a logic device, and a storage device comprising instructions executable by the logic device to track an eye gaze direction by acquiring an image of the eye via the eye-tracking system, and determining a determined location of a center of a lens of the eye from the image of the eye. The instructions are further executable to adjust the determined location of the center of the lens on a sub-pixel scale by applying a predetermined sub-pixel offset to the determined location of the center of the lens to produce an adjusted location of the center of the lens, to determine a gaze direction from the adjusted location of the center of the lens, and perform an action on a computing device based on the gaze direction.
    Type: Application
    Filed: January 9, 2015
    Publication date: July 14, 2016
    Inventors: Xu Miao, Michael J. Conrad, Dijia Wu