Patents by Inventor Rindranirina RAMAMONJISON

Rindranirina RAMAMONJISON 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).

  • Publication number: 20240347168
    Abstract: Methods and systems for generating a personalized target combination of items for a user are disclosed. The method includes accessing an item database, accessing a target score database, receiving information about item election values of the user, determining, for each item of the item database, an item election value based on the first input, receiving information about a user profile of the user, determining a set of user target feature scores for the user, determining a list of candidate item combinations from the item database, determining, for each candidate item combination, a global feature score based on the feature scores of the sub-items of the items of the candidate item combination, receiving an indication of a preferred candidate item combination and adjusting a respective quantity of the sub-items of the items of the preferred item combination to obtain the personalized target item combination by minimizing an objective function.
    Type: Application
    Filed: April 14, 2023
    Publication date: October 17, 2024
    Inventors: Shiqi HE, Rindranirina RAMAMONJISON, Yong ZHANG
  • Publication number: 20240311548
    Abstract: The present disclosure provides a computer implemented method and system for generating an algebraic modelling language (AML) formulation of natural language text description of an optimization problem. The computer implemented method includes generating, based on the natural language text description, a text markup language intermediate representation (IR) of the optimization problem, the text markup language IR including an IR objective declaration that defines an objective for the optimization problem and a first IR constraint declaration that indicates a first constraint for the optimization problem. The computer implemented also includes generating, based on the text markup language IR, the AML formulation of the optimization problem, the AML formulation including an AML objective declaration that defines the objective for the optimization problem and a first AML constraint declaration that indicates the first constraint for the optimization problem.
    Type: Application
    Filed: May 22, 2024
    Publication date: September 19, 2024
    Inventors: Rindranirina RAMAMONJISON, Amin BANITALEBI DEHKORDI, Vishnu Gokul RENGAN, Zirui ZHOU, Yong ZHANG
  • Patent number: 12001779
    Abstract: The present disclosure provides a computer implemented method and system for generating an algebraic modelling language (AML) formulation of natural language text description of an optimization problem. The computer implemented method includes generating, based on the natural language text description, a text markup language intermediate representation (IR) of the optimization problem, the text markup language IR including an IR objective declaration that defines an objective for the optimization problem and a first IR constraint declaration that indicates a first constraint for the optimization problem. The computer implemented also includes generating, based on the text markup language IR, the AML formulation of the optimization problem, the AML formulation including an AML objective declaration that defines the objective for the optimization problem and a first AML constraint declaration that indicates the first constraint for the optimization problem.
    Type: Grant
    Filed: January 14, 2022
    Date of Patent: June 4, 2024
    Assignee: HUAWEI CLOUD COMPUTING TECHNOLOGIES CO., LTD.
    Inventors: Rindranirina Ramamonjison, Amin Banitalebi Dehkordi, Vishnu Gokul Rengan, Zirui Zhou, Yong Zhang
  • Publication number: 20230281974
    Abstract: The present disclosure provides a method and system for adapting a machine learning model, such as an object detection model, to account for domain shift. The method includes receiving a labeled data elements and target image samples and performing a plurality of model adaptation epochs. Each adaptation epoch includes: predicting for each of the target image samples, using the machine learning model configured by a current set of configuration parameters, a corresponding target class label for the respective target data object included in the target image sample; generating a plurality of labeled mixed data elements that each include: (i) a mixed image sample including a source data object from one of the source image samples and a target data object from one of the target image samples, and (ii) the corresponding source class label for the source data object and the corresponding target class label for the target data object.
    Type: Application
    Filed: May 15, 2023
    Publication date: September 7, 2023
    Inventors: Rindranirina RAMAMONJISON, Amin BANITALEBI DEHKORDI, Xinyu KANG, Yong ZHANG
  • Publication number: 20230229849
    Abstract: The present disclosure provides a computer implemented method and system for generating an algebraic modelling language (AML) formulation of natural language text description of an optimization problem. The computer implemented method includes generating, based on the natural language text description, a text markup language intermediate representation (IR) of the optimization problem, the text markup language IR including an IR objective declaration that defines an objective for the optimization problem and a first IR constraint declaration that indicates a first constraint for the optimization problem. The computer implemented also includes generating, based on the text markup language IR, the AML formulation of the optimization problem, the AML formulation including an AML objective declaration that defines the objective for the optimization problem and a first AML constraint declaration that indicates the first constraint for the optimization problem.
    Type: Application
    Filed: January 14, 2022
    Publication date: July 20, 2023
    Inventors: Rindranirina RAMAMONJISON, Amin BANITALEBI DEHKORDI, Vishnu Gokul RENGAN, Zirui ZHOU, Yong ZHANG