Patents by Inventor Sajad MODARESI

Sajad MODARESI 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).

  • Quick learning recommendation method, non-transitory computer readable medium and system for baskets of goods
    Patent number: 11151631
    Abstract: Embodiments provide a recommendation for an additional item in response to receiving a basket of goods determine a type for the basket of goods from a set of basket types, receive a set of additional targeted items as target recommendations and receive a history of received types of baskets of goods. Embodiments iteratively perform a clustering into a plurality of clusters of each of the basket types based on the history of received types of baskets of goods, and preference updating for each of the targeted items into each of the plurality of clusters. The iteratively performing, after a plurality of iterations, outputs a sequence of mappings and a sequence of preference parameters. Embodiments generate a frequency of tabulation of mappings from the sequence of mappings and then generate the recommendation based on the sequence of mappings, the sequence of preference parameters and the frequency of tabulation of mappings.
    Type: Grant
    Filed: April 17, 2018
    Date of Patent: October 19, 2021
    Assignee: ORACLE INTERNATIONAL CORPORATION
    Inventors: Sajad Modaresi, Fernando Bernstein, Denis Saure, Setareh Borjian Boroujeni, Su-Ming Wu, Robert Corr, Nikos Nikolakis
  • Quick Learning Recommendation Systems for Baskets of Goods
    Publication number: 20190318410
    Abstract: Embodiments provide a recommendation for an additional item in response to receiving a basket of goods determine a type for the basket of goods from a set of basket types, receive a set of additional targeted items as target recommendations and receive a history of received types of baskets of goods. Embodiments iteratively perform a clustering into a plurality of clusters of each of the basket types based on the history of received types of baskets of goods, and preference updating for each of the targeted items into each of the plurality of clusters. The iteratively performing, after a plurality of iterations, outputs a sequence of mappings and a sequence of preference parameters. Embodiments generate a frequency of tabulation of mappings from the sequence of mappings and then generate the recommendation based on the sequence of mappings, the sequence of preference parameters and the frequency of tabulation of mappings.
    Type: Application
    Filed: April 17, 2018
    Publication date: October 17, 2019
    Inventors: Sajad MODARESI, Fernando BERNSTEIN, Denis SAURE, Setareh Borjian BOROUJENI, Su-Ming WU, Robert CORR, Nikos NIKOLAKIS