Patents by Inventor Nir Levine

Nir Levine 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).

  • ROBUST REINFORCEMENT LEARNING FOR CONTINUOUS CONTROL WITH MODEL MISSPECIFICATION
    Publication number: 20220343157
    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a policy neural network having policy parameters. One of the methods includes sampling a mini-batch comprising one or more observation-action-reward tuples generated as a result of interactions of a first agent with a first environment; determining an update to current values of the Q network parameters by minimizing a robust entropy-regularized temporal difference (TD) error that accounts for possible perturbations of the states of the first environment represented by the observations in the observation-action-reward tuples; and determining, using the Q-value neural network, an update to the policy network parameters using the sampled mini-batch of observation-action-reward tuples.
    Type: Application
    Filed: June 17, 2020
    Publication date: October 27, 2022
    Inventors: Daniel J. Mankowitz, Nir Levine, Rae Chan Jeong, Abbas Abdolmaleki, Jost Tobias Springenberg, Todd Andrew Hester, Timothy Arthur Mann, Martin Riedmiller
  • Relevance model for session search
    Patent number: 10956409
    Abstract: A session search relevance model identifies a user's dynamic information need based on a feedback model and a session relevance model. The feedback model is based on query changes in the session search and user interest in particular documents presented throughout the session search. The relevance model modifies a user's current query to retrieve documents most relevant to a user's information need.
    Type: Grant
    Filed: May 10, 2017
    Date of Patent: March 23, 2021
    Assignee: International Business Machines Corporation
    Inventors: Haggai Roitman, Doron Cohen, Nir Levine
  • RELEVANCE MODEL FOR SESSION SEARCH
    Publication number: 20180329947
    Abstract: A session search relevance model identifies a user's dynamic information need based on a feedback model and a session relevance model. The feedback model is based on query changes in the session search and user interest in particular documents presented throughout the session search. The relevance model modifies a user's current query to retrieve documents most relevant to a user's information need.
    Type: Application
    Filed: May 10, 2017
    Publication date: November 15, 2018
    Inventors: Haggai Roitman, Doron Cohen, Nir Levine
  • Relevance model for session search
    Patent number: 10078661
    Abstract: A session search relevance model identifies a user's dynamic information need based on a feedback model and a session relevance model. The feedback model is based on query changes in the session search and user interest in particular documents presented throughout the session search. The relevance model modifies a user's current query to retrieve documents most relevant to a user's information need.
    Type: Grant
    Filed: December 20, 2017
    Date of Patent: September 18, 2018
    Assignee: International Business Machines Corporation
    Inventors: Haggai Roitman, Doron Cohen, Nir Levine