Patents by Inventor Andrew Collins Bessey

Andrew Collins Bessey 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 LEARNED RESOURCE ALLOCATION MODELS FOR CENTRALIZED DATABASE PREDICTIONS
    Publication number: 20250094859
    Abstract: A central database system trains and applies machine-learned models based on characteristics of one or more entities associated with the central database system. For instance, the central database system trains a machine-learned model configured to identify issues a target entity is likely to encounter based on training data identifying characteristics of historical entities and issues faced by the historical entities. Likewise, the central database system trains machine-learned models configured to predict actions that entities are likely to take in the future, and resources required to take those actions. The central database system can then perform one or more proactive actions or make one or more recommendations based on the predicted issues, the predicted future actions, and the predicted required resources.
    Type: Application
    Filed: September 15, 2023
    Publication date: March 20, 2025
    Inventors: Christian Franklin Hillson, Jacques Robert Caspi, Andrew Collins Bessey, Lilly Anne Pieper, Ryan David Kappedal, Jasmine Walker Motupalli, Addison Woodford Bohannon, Rebecca Alice Carter
  • MACHINE LEARNED ENTITY ISSUE MODELS FOR CENTRALIZED DATABASE PREDICTIONS
    Publication number: 20250094899
    Abstract: A central database system trains and applies machine-learned models based on characteristics of one or more entities associated with the central database system. For instance, the central database system trains a machine-learned model configured to identify issues a target entity is likely to encounter based on training data identifying characteristics of historical entities and issues faced by the historical entities. Likewise, the central database system trains machine-learned models configured to predict actions that entities are likely to take in the future, and resources required to take those actions. The central database system can then perform one or more proactive actions or make one or more recommendations based on the predicted issues, the predicted future actions, and the predicted required resources.
    Type: Application
    Filed: September 15, 2023
    Publication date: March 20, 2025
    Inventors: Christian Franklin Hillson, Jacques Robert Caspi, Andrew Collins Bessey, Lilly Anne Pieper, Ryan David Kappedal, Jasmine Walker Motupalli, Addison Woodford Bohannon, Rebecca Alice Carter
  • MACHINE LEARNED ENTITY ACTION MODELS FOR CENTRALIZED DATABASE PREDICTIONS
    Publication number: 20250094452
    Abstract: A central database system trains and applies machine-learned models based on characteristics of one or more entities associated with the central database system. For instance, the central database system trains a machine-learned model configured to identify issues a target entity is likely to encounter based on training data identifying characteristics of historical entities and issues faced by the historical entities. Likewise, the central database system trains machine-learned models configured to predict actions that entities are likely to take in the future, and resources required to take those actions. The central database system can then perform one or more proactive actions or make one or more recommendations based on the predicted issues, the predicted future actions, and the predicted required resources.
    Type: Application
    Filed: September 15, 2023
    Publication date: March 20, 2025
    Inventors: Christian Franklin Hillson, Jacques Robert Caspi, Andrew Collins Bessey, Lilly Anne Pieper, Ryan David Kappedal, Jasmine Walker Motupalli, Addison Woodford Bohannon, Rebecca Alice Carter