Patents by Inventor Jacquelyn Zaydel

Jacquelyn Zaydel 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).

  • Machine learning for nutrient quantity estimation in score-based diets and methods of use thereof
    Patent number: 11803781
    Abstract: Systems and methods of the present disclosure use one or more processor(s) to receive a consumable preference and a daily score intake value associated with a user and to obtain a content data regarding consumable item including an amount of a first nutrient found in the consumable item. The processor(s) utilizes, in real-time, a nutrient prediction machine learning model to ingest the content data regarding the consumable item and predict an amount of a second nutrient in the consumable item based on the content data and a decision tree library of thousand nutrient decision trees. The processor(s) determines zero-scored consumable item based on the daily score intake value, the amount of the first nutrient, the amount of the second nutrient, and the consumable preference. The processor(s) instructs a computing device to utilize a graphical user interface element to identify the zero-scored consumable item on a screen of the computing device.
    Type: Grant
    Filed: September 8, 2022
    Date of Patent: October 31, 2023
    Assignee: Weight Watchers International, Inc.
    Inventors: Gary Foster, Ute Gerwig, Laura Smith, Reka Daniel-Weiner, Michael Skarlinski, Judith Bünker, Jacquelyn Zaydel
  • MACHINE LEARNING FOR NUTRIENT QUANTITY ESTIMATION IN SCORE-BASED DIETS AND METHODS OF USE THEREOF
    Publication number: 20230079862
    Abstract: Systems and methods of the present disclosure use one or more processor(s) to receive a consumable preference and a daily score intake value associated with a user and to obtain a content data regarding consumable item including an amount of a first nutrient found in the consumable item. The processor(s) utilizes, in real-time, a nutrient prediction machine learning model to ingest the content data regarding the consumable item and predict an amount of a second nutrient in the consumable item based on the content data and a decision tree library of thousand nutrient decision trees. The processor(s) determines zero-scored consumable item based on the daily score intake value, the amount of the first nutrient, the amount of the second nutrient, and the consumable preference. The processor(s) instructs a computing device to utilize a graphical user interface element to identify the zero-scored consumable item on a screen of the computing device.
    Type: Application
    Filed: September 8, 2022
    Publication date: March 16, 2023
    Inventors: Gary Foster, Ute Gerwig, Laura Smith, Reka Daniel-Weiner, Michael Skarlinski, Judith Bünker, Jacquelyn Zaydel