Patents by Inventor Alyssa Hester

Alyssa Hester 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: 20230359937
    Abstract: A central database system trains a machine-learned model based on training data identifying characteristics of account holder entities, characteristics of account provider entities, and relationships between the account holder entities and account provider entities. For a target entity, the central database system then identifies a target set of account provider entities, and applies the trained machine-learned model to identify a subset of the target set of account provider entities. The identified subset of account provider entities are entities that, if recommended to the target entity, are most likely to result in an established relationship with the target entity. A recommendation is then generated for display to the target entity, the recommendation identifying the subset of account provider entities and including interface elements that, if selected by the target entity, cause a notification identifying the target entity to be sent to a corresponding account provider entity.
    Type: Application
    Filed: July 5, 2023
    Publication date: November 9, 2023
    Inventors: Victoria Huang, Alyssa Hester, Rebecca Carter
  • Patent number: 11734613
    Abstract: A central database system trains a machine-learned model based on training data identifying characteristics of account holder entities, characteristics of account provider entities, and relationships between the account holder entities and account provider entities. For a target entity, the central database system then identifies a target set of account provider entities, and applies the trained machine-learned model to identify a subset of the target set of account provider entities. The identified subset of account provider entities are entities that, if recommended to the target entity, are most likely to result in an established relationship with the target entity. A recommendation is then generated for display to the target entity, the recommendation identifying the subset of account provider entities and including interface elements that, if selected by the target entity, cause a notification identifying the target entity to be sent to a corresponding account provider entity.
    Type: Grant
    Filed: August 15, 2022
    Date of Patent: August 22, 2023
    Assignee: ZENPAYROLL, INC.
    Inventors: Victoria Huang, Alyssa Hester, Rebecca Carter
  • Publication number: 20220391770
    Abstract: A central database system trains a machine-learned model based on training data identifying characteristics of account holder entities, characteristics of account provider entities, and relationships between the account holder entities and account provider entities. For a target entity, the central database system then identifies a target set of account provider entities, and applies the trained machine-learned model to identify a subset of the target set of account provider entities. The identified subset of account provider entities are entities that, if recommended to the target entity, are most likely to result in an established relationship with the target entity. A recommendation is then generated for display to the target entity, the recommendation identifying the subset of account provider entities and including interface elements that, if selected by the target entity, cause a notification identifying the target entity to be sent to a corresponding account provider entity.
    Type: Application
    Filed: August 15, 2022
    Publication date: December 8, 2022
    Inventors: Victoria Huang, Alyssa Hester, Rebecca Carter
  • Patent number: 11449804
    Abstract: A central database system trains a machine-learned model based on training data identifying characteristics of account holder entities, characteristics of account provider entities, and relationships between the account holder entities and account provider entities. For a target entity, the central database system then identifies a target set of account provider entities, and applies the trained machine-learned model to identify a subset of the target set of account provider entities. The identified subset of account provider entities are entities that, if recommended to the target entity, are most likely to result in an established relationship with the target entity. A recommendation is then generated for display to the target entity, the recommendation identifying the subset of account provider entities and including interface elements that, if selected by the target entity, cause a notification identifying the target entity to be sent to a corresponding account provider entity.
    Type: Grant
    Filed: September 4, 2020
    Date of Patent: September 20, 2022
    Assignee: ZENPAYROLL, INC.
    Inventors: Victoria Huang, Alyssa Hester, Rebecca Carter
  • Publication number: 20220076156
    Abstract: A central database system trains a machine-learned model based on training data identifying characteristics of account holder entities, characteristics of account provider entities, and relationships between the account holder entities and account provider entities. For a target entity, the central database system then identifies a target set of account provider entities, and applies the trained machine-learned model to identify a subset of the target set of account provider entities. The identified subset of account provider entities are entities that, if recommended to the target entity, are most likely to result in an established relationship with the target entity. A recommendation is then generated for display to the target entity, the recommendation identifying the subset of account provider entities and including interface elements that, if selected by the target entity, cause a notification identifying the target entity to be sent to a corresponding account provider entity.
    Type: Application
    Filed: September 4, 2020
    Publication date: March 10, 2022
    Inventors: Victoria Huang, Alyssa Hester, Rebecca Carter