Patents by Inventor Hani ALMOUSLI

Hani ALMOUSLI 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).

  • System and method for deep learning recommender
    Patent number: 12711529
    Abstract: Recommendations are generated for users by identifying items held by the users defined by a shallow representation and attributes; defining the items based on a deep representation derived from attributes; generating a deep holding matrix identifying the items held by the users based on deep representations; generating a shallow holding matrix identifying the items held by the users based on shallow representations; generating a similarity score matrix between the deep representations; decomposing the shallow holding matrix into a user latent representation and a product feature latent representation; concatenating the product feature latent representation and product information and pass to a first neural network; concatenating the user latent representation and user information and pass to a second neural network; performing a dot product matrix multiplication on the output of the first neural and the output of the second neural network to generate, for every user and every product, a probability.
    Type: Grant
    Filed: October 30, 2020
    Date of Patent: August 18, 2026
    Assignee: ROYAL BANK OF CANADA
    Inventors: Omar Nada, Hani Almousli, Sean Singh
  • SYSTEM AND METHOD FOR DEEP LEARNING RECOMMENDER
    Publication number: 20210133853
    Abstract: Recommendations are generated for users by identifying items held by the users defined by a shallow representation and attributes; defining the items based on a deep representation derived from attributes; generating a deep holding matrix identifying the items held by the users based on deep representations; generating a shallow holding matrix identifying the items held by the users based on shallow representations; generating a similarity score matrix between the deep representations; decomposing the shallow holding matrix into a user latent representation and a product feature latent representation; concatenating the product feature latent representation and product information and pass to a first neural network; concatenating the user latent representation and user information and pass to a second neural network; performing a dot product matrix multiplication on the output of the first neural and the output of the second neural network to generate, for every user and every product, a probability.
    Type: Application
    Filed: October 30, 2020
    Publication date: May 6, 2021
    Inventors: Omar NADA, Hani ALMOUSLI, Sean SINGH