Patents by Inventor Susan Andrea Macisaac

Susan Andrea Macisaac 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).

  • USING MACHINE LEARNING-BASED SEED HARVEST MOISTURE PREDICTIONS TO IMPROVE A COMPUTER-ASSISTED AGRICULTURAL FARM OPERATION
    Publication number: 20210342719
    Abstract: Embodiments generate digital plans for agricultural fields. In an embodiment, a model receives digital inputs including stress risk data, product maturity data, field location data, planting date data, and/or harvest date data. The model mathematically correlates sets of digital inputs with threshold data associated with the stress risk data. The model is used to generate stress risk prediction data for a set of product maturity and field location combinations. In a digital plan, product maturity data or planting date data or harvest date data or field location data can be adjusted based on the stress risk prediction data. A digital plan can be transmitted to a field manager computing device. An agricultural apparatus can be moved in response to a digital plan.
    Type: Application
    Filed: May 6, 2021
    Publication date: November 4, 2021
    Inventors: Shilpa Sood, Matthew Sorge, Nikisha Shah, Timothy Reich, Herbert Ssegane, Jason Kendrick Bull, Tonya S. Ehlmann, Morrison Jacobs, Susan Andrea Macisaac, Bruce J. Schnicker, Yao Xie, Allan Trapp, Xiao Yang
  • Using machine learning-based seed harvest moisture predictions to improve a computer-assisted agricultural farm operation
    Patent number: 11017306
    Abstract: Embodiments generate digital plans for agricultural fields. In an embodiment, a model receives digital inputs including stress risk data, product maturity data, field location data, planting date data, and/or harvest date data. The model mathematically correlates sets of digital inputs with threshold data associated with the stress risk data. The model is used to generate stress risk prediction data for a set of product maturity and field location combinations. In a digital plan, product maturity data or planting date data or harvest date data or field location data can be adjusted based on the stress risk prediction data. A digital plan can be transmitted to a field manager computing device. An agricultural apparatus can be moved in response to a digital plan.
    Type: Grant
    Filed: October 23, 2019
    Date of Patent: May 25, 2021
    Assignee: THE CLIMATE CORPORATION
    Inventors: Shilpa Sood, Matthew Sorge, Nikisha Shah, Timothy Reich, Herbert Ssegane, Jason Kendrick Bull, Tonya S. Ehlmann, Morrison Jacobs, Susan Andrea Macisaac, Bruce J. Schnicker, Yao Xie, Allan Trapp, Xiao Yang
  • USING MACHINE LEARNING-BASED SEED HARVEST MOISTURE PREDICTIONS TO IMPROVE A COMPUTER-ASSISTED AGRICULTURAL FARM OPERATION
    Publication number: 20200134485
    Abstract: Embodiments generate digital plans for agricultural fields. In an embodiment, a model receives digital inputs including stress risk data, product maturity data, field location data, planting date data, and/or harvest date data. The model mathematically correlates sets of digital inputs with threshold data associated with the stress risk data. The model is used to generate stress risk prediction data for a set of product maturity and field location combinations. In a digital plan, product maturity data or planting date data or harvest date data or field location data can be adjusted based on the stress risk prediction data. A digital plan can be transmitted to a field manager computing device. An agricultural apparatus can be moved in response to a digital plan.
    Type: Application
    Filed: October 23, 2019
    Publication date: April 30, 2020
    Inventors: Shilpa Sood, Matthew Sorge, Nikisha Shah, Timothy Reich, Herbert Ssegane, Jason Kendrick Bull, Tonya S. Ehlmann, Morrison Jacobs, Susan Andrea Macisaac, Bruce J. Schnicker, Yao Xie, Allan Trapp, Xiao Yang