Patents by Inventor Shahdad Irajpour

Shahdad Irajpour 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).

  • Hashing query and job posting features for improved machine learning model performance
    Patent number: 10565562
    Abstract: In an example, a first hash function is performed on job posting features extracted from a job posting to obtain hashed job posting features. The hashed job posting features are stored in a forward-index corresponding to the job posting in the database. When a job search query is received from a first member of a social networking service, job search query features are extracted from the job search query and a second hash function is performed on the job search query features. The hashed job posting features and the hashed job search query features are fed to a job posting result ranking model trained via a machine learning algorithm to compare the hashed job posting features to the hashed job search query features to generate an application likelihood score indicating a likelihood that the first member will apply for a job corresponding to the job posting.
    Type: Grant
    Filed: July 25, 2017
    Date of Patent: February 18, 2020
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Ankan Saha, Dhruv Arya, Shahdad Irajpour
  • HASHING QUERY AND JOB POSTING FEATURES FOR IMPROVED MACHINE LEARNING MODEL PERFORMANCE
    Publication number: 20190034882
    Abstract: In an example, a first hash function is performed on job posting features extracted from a job posting to obtain hashed job posting features. The hashed job posting features are stored in a forward-index corresponding to the job posting in the database. When a job search query is received from a first member of a social networking service, job search query features are extracted from the job search query and a second hash function is performed on the job search query features. The hashed job posting features and the hashed job search query features are fed to a job posting result ranking model trained via a machine learning algorithm to compare the hashed job posting features to the hashed job search query features to generate an application likelihood score indicating a likelihood that the first member will apply for a job corresponding to the job posting.
    Type: Application
    Filed: July 25, 2017
    Publication date: January 31, 2019
    Inventors: Ankan Saha, Dhruv Arya, Shahdad Irajpour