Patents by Inventor Jacob Sean LARIVIERE

Jacob Sean LARIVIERE 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).

  • Machine-learning for combinatorial decisioning
    Patent number: 11232501
    Abstract: According to examples, an apparatus may include a processor and a memory on which is stored machine readable instructions that may cause the processor to generate a matrix based on features of reference entities and a respective allocation of value provided to each reference entity, apply first-stage machine-learning on the matrix to identify relevant features of the reference entities that correlate with the respective allocation of value provided to each reference entity, and access an identity of a target entity and target features of the target entity. The instructions may further cause the processor to apply second-stage machine-learning to generate a cluster comprising the target entity and a set of the reference entities based on the relevant features and determine a distribution of values allocated to the set. The distribution of values may be used to generate or assess a target allocation of value for the target entity.
    Type: Grant
    Filed: February 19, 2020
    Date of Patent: January 25, 2022
    Assignee: MICROSOFT TECHNOLOGY LICENSING, LLC
    Inventors: Jacob Sean Lariviere, Jason Thorpe, Stephen Bruce Broschart, Shujuan Huang, Xin Yang, Alperen Kok, Shiqi Wen
  • MACHINE-LEARNING FOR COMBINATORIAL DECISIONING
    Publication number: 20210182927
    Abstract: According to examples, an apparatus may include a processor and a memory on which is stored machine readable instructions that may cause the processor to generate a matrix based on features of reference entities and a respective allocation of value provided to each reference entity, apply first-stage machine-learning on the matrix to identify relevant features of the reference entities that correlate with the respective allocation of value provided to each reference entity, and access an identity of a target entity and target features of the target entity. The instructions may further cause the processor to apply second-stage machine-learning to generate a cluster comprising the target entity and a set of the reference entities based on the relevant features and determine a distribution of values allocated to the set. The distribution of values may be used to generate or assess a target allocation of value for the target entity.
    Type: Application
    Filed: February 19, 2020
    Publication date: June 17, 2021
    Applicant: Microsoft Technology Licensing, LLC
    Inventors: Jacob Sean LARIVIERE, Jason THORPE, Stephen Bruce BROSCHART, Shujuan HUANG, Xin Yang, Alperen KOK, Shiqi WEN