Patents by Inventor Ryan H. Lewis

Ryan H. Lewis 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).

  • Scalable topological data analysis
    Patent number: 10417262
    Abstract: An example method comprises receiving first data associated with data points, receiving a lens function selection, a metric function selection, and a resolution function, the metric function identified by the metric function selection being capable of performing functions on data as matrix functions, mapping second data based on the first data to a reference space by utilizing matrix vector multiplication for application of selected lens function on second data based on the first data to map the second data to the reference space, generating cover of reference space including the second data, clustering second data in cover based on the selected metric function to determine each node of a plurality of nodes, each of the nodes of the plurality of nodes comprising members representative of at least one subset of the data points, and generating a visualization comprising the plurality of nodes and a plurality of edges wherein each of the edges of the plurality of edges connects nodes with shared members.
    Type: Grant
    Filed: June 30, 2017
    Date of Patent: September 17, 2019
    Assignee: Ayasdi AI LLC
    Inventor: Ryan H. Lewis
  • SCALABLE TOPOLOGICAL DATA ANALYSIS
    Publication number: 20180004836
    Abstract: An example method comprises receiving first data associated with data points, receiving a lens function selection, a metric function selection, and a resolution function, the metric function identified by the metric function selection being capable of performing functions on data as matrix functions, mapping second data based on the first data to a reference space by utilizing matrix vector multiplication for application of selected lens function on second data based on the first data to map the second data to the reference space, generating cover of reference space including the second data, clustering second data in cover based on the selected metric function to determine each node of a plurality of nodes, each of the nodes of the plurality of nodes comprising members representative of at least one subset of the data points, and generating a visualization comprising the plurality of nodes and a plurality of edges wherein each of the edges of the plurality of edges connects nodes with shared members.
    Type: Application
    Filed: June 30, 2017
    Publication date: January 4, 2018
    Inventor: Ryan H. Lewis