Patents by Inventor Pavel ROIT

Pavel ROIT 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: 11238521
    Abstract: The disclosure herein describes a recommendation system utilizing a specialized domain-specific language model for generating cold-start recommendations in an absence of user-specific data based on a user-selection of a seed item. A generalized language model is trained using a domain-specific corpus of training data, including title and description pairs associated with candidate items in a domain-specific catalog. The language model is trained to distinguish between real title-description pairs and fake title-description pairs. The trained language model analyzes the title and description of the seed item with the title and description of each candidate item in the catalog to create a hybrid set of scores. The set of scores includes similarity scores and classification scores for the seed item title with each candidate item description and title. The scores are utilized by the model to identify candidate items maximizing similarity with the seed item for cold-start recommendation to a user.
    Type: Grant
    Filed: February 12, 2020
    Date of Patent: February 1, 2022
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Itzik Malkiel, Pavel Roit, Noam Koenigstein, Oren Barkan, Nir Nice
  • Publication number: 20210182935
    Abstract: The disclosure herein describes a recommendation system utilizing a specialized domain-specific language model for generating cold-start recommendations in an absence of user-specific data based on a user-selection of a seed item. A generalized language model is trained using a domain-specific corpus of training data, including title and description pairs associated with candidate items in a domain-specific catalog. The language model is trained to distinguish between real title-description pairs and fake title-description pairs. The trained language model analyzes the title and description of the seed item with the title and description of each candidate item in the catalog to create a hybrid set of scores. The set of scores includes similarity scores and classification scores for the seed item title with each candidate item description and title. The scores are utilized by the model to identify candidate items maximizing similarity with the seed item for cold-start recommendation to a user.
    Type: Application
    Filed: February 12, 2020
    Publication date: June 17, 2021
    Inventors: Itzik MALKIEL, Pavel ROIT, Noam KOENIGSTEIN, Oren BARKAN, Nir NICE