Patents by Inventor Shulei Wang

Shulei Wang 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: 20260092169
    Abstract: The present application provides an all-steel snow tire tread, consisting of the following raw materials in parts by weight: 50-80 parts of natural rubber, 20-50 parts of solution polymerized styrene butadiene rubber with dual glass transition temperatures, 40-60 parts of carbon black, 10-20 parts of white carbon black with a high specific surface area, 1-2.5 parts of silane coupling agent, 5-10 parts of modified anti-wet skid resin, 2-5 parts of zinc oxide, 1-3 parts of stearic acid, 1-3 parts of anti-aging agent 4020, 1-3 parts of anti-aging agent RD, 1-3 parts of protective wax, 1-1.8 parts of sulfur, and 1-1.5 parts of promoter NS.
    Type: Application
    Filed: September 28, 2025
    Publication date: April 2, 2026
    Applicant: SHANDONG LINGLONG TIRE CO., LTD.
    Inventors: FENG WANG, SHULEI WANG, TAO SUN, JUNYING WANG, ZHENLING WANG, TAO XING
  • Patent number: 10176246
    Abstract: In some examples, a time-series data set can be analyzed and grouped in a fast and efficient manner. For instance, fast grouping of multiple time-series into clusters can be implemented through data reduction, determining cluster population, and fast matching by locality sensitive hashing. In some situations, a user can select a level of granularity for grouping time-series into clusters, which can involve trade-offs between the number of clusters and the maximum distance between two time-series in a cluster.
    Type: Grant
    Filed: June 14, 2013
    Date of Patent: January 8, 2019
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Yingnong Dang, Qiang Wang, Qianchuan Zhao, Shulei Wang, Rui Ding, Qiang Fu, Dongmei Zhang
  • Publication number: 20160140208
    Abstract: In some examples, a time-series data set can be analyzed and grouped in a fast and efficient manner. For instance, fast grouping of multiple time-series into clusters can be implemented through data reduction, determining cluster population, and fast matching by locality sensitive hashing. In some situations, a user can select a level of granularity for grouping time-series into clusters, which can involve trade-offs between the number of clusters and the maximum distance between two time-series in a cluster.
    Type: Application
    Filed: June 14, 2013
    Publication date: May 19, 2016
    Inventors: Yingnong Dang, Qiang Wang, Qianchuan Zhao, Shulei Wang, Rui Ding, Qiang Fu, Dongmei Zhang