Granted Patent B2

US 12,339,904 · App. 17/015,065 · Granted Jun 24, 2025

Inventor: Edward Hunter (Gaithersburg, MD)

Assignee: Digital Asset Capital, Inc

G06F16/9024G06F16/245G06F16/289G06F16/951H04L9/3263H04L67/133

Abstract

Claims (62)

1. A tangible, non-transitory, machine-readable medium storing instructions that, when executed by a computing system, effectuate operations comprising:

determining, with a computer system, a set of features associated in memory of the computer system with a set of vertices of a first directed graph, wherein a feature of the set of features is associated in memory of the computer system with a category type comprising a set of mutually exclusive categories;

obtaining, with the computer system, a set of feature values associated with the set of vertices, wherein each respective vertex of set of vertices is associated with a respective subset of feature values, wherein:

each feature value is associated with a feature of the set of features, and

the respective subset of feature values comprise a respective category of the set of mutually exclusive categories;

selecting, with the computer system, a first subset of features based on the set of feature values, wherein the selecting comprises:

determining a plurality of candidate subsets of features;

determining a plurality of feature subset scores associated with the plurality of candidate subsets of features based on a category label selected from the set of mutually exclusive categories and the set of feature values; and

selecting the first subset of features based on the plurality of feature subset scores;

performing, with the computer system, a first operation to determine a set of extracted feature values, the first operation comprising:

determining a set of input values by increasing a set of feature values associated with the first subset of features with a set of weights; and

determining the set of extracted feature values based on the set of input values, wherein the set of extracted feature values comprises a first multidimensional vector associated with the first directed graph;

determining, with the computer system, a metric based on a distance between the first multidimensional vector and a second multidimensional vector of a second directed graph;

determining, with the computer system, whether the metric satisfies a first threshold; and

storing, with the computer system, the metric in persistent storage.

2. The medium of claim 1 , wherein determining the plurality of feature subset scores associated with the plurality of candidate subsets comprises:

determining a first candidate subset of features, wherein the plurality of candidate subsets comprises the first candidate subset of features;

determining a first feature subset score based on the first candidate subset of features using a neural network or decision tree; and

selecting the first candidate subset of features as the first subset of features based on the first feature subset score being a maximum or minimum of the plurality of feature subset scores.

3. The medium of claim 1 , the operations further comprising:

obtaining a set of eigenvectors; and

computing the first multidimensional vector based on a first set of feature values for a first vertex and the set of eigenvectors, wherein a sum of the set of eigenvectors when weighted by the first multidimensional vector satisfies a second threshold associated with the first set of feature values.

4. The medium of claim 1 , wherein determining an extracted feature score comprises using a neural network that comprises a set of input layers and a set of output layers, wherein a count of the set of input layers is equal to a count of the set of output layers.

5. The medium of claim 1 , wherein determining the metric comprises determining a Minkowski distance between the first multidimensional vector and a second multidimensional vector.

6. The medium of claim 1 , the operations further comprising determining a first subset of vertices of the set of vertices based on the first subset of features and the set of extracted feature values satisfying a third threshold.

7. The medium of claim 6 , wherein determining the first subset of vertices comprises obtaining a set of prioritization parameters comprising the third threshold via a user interface element.

8. The medium of claim 1 , the operations further comprising:

determining whether a graph portion of the first directed graph matches a graph portion template, the graph portion template indicating a first vertex template, a second vertex template, and a directed edge template;

generate an indicator associated with the graph portion; and

visually indicating the graph portion associated with the graph portion based on the indicator.

9. The medium of claim 1 , the operations further comprising increasing a feature value associated with a first feature in response to a determination that a first vertex is associated with a first category label and that a first conditional statement associated with the first vertex is satisfied.

10. The medium of claim 1 , the operations further comprising visually indicating a natural language text section associated with a vertex of the first directed graph.

11. The medium of claim 10 , wherein determining the set of feature values comprises:

determining a set of embedding values based on the natural language text section using a neural network; and

determining a topic score based on the set of embedding scores, wherein the set of feature values comprises the topic score.

12. The medium of claim 1 , the operations further comprising visually indicating a first vertex of the first directed graph with a shape, color, pattern, or animation that is different from a shape, color, pattern, or animation of a second vertex of the first directed graph in a visual display of the first directed graph.

13. The medium of claim 1 , the operations further comprising providing a user interface, the user interface comprising a set of shapes representing vertices and a set of lines connecting the set of shapes, wherein each line is associated with an edge.

14. The medium of claim 13 , the operations further comprising providing a user interface (UI), the UI indicating a first subset of vertices in a different color than a second subset of vertices of the first directed graph.

15. The medium of claim 1 , wherein determining the set of extracted feature values comprises:

determining a set of adjacency values associated with a first vertex; and

determining a matrix multiplication product based on the set of adjacency values and one or more feature values of the set of feature values.

16. The medium of claim 1 , the operations further comprising providing a user interface (UI), the UI comprising:

a set of identifiers associated with the first subset of features; and

a set of UI elements that, after manipulation, causes an update to a feature value of a vertex of the first directed graph.

17. The medium of claim 16 , the operations further comprising determining a limit associated with a first feature of the first subset of features, wherein the UI causes a display of the limit.

18. The medium of claim 1 , the operations further comprising steps for determining the metric between the first directed graph and the second directed graph.

19. The medium of claim 1 , the operations further comprising steps for determining the first subset of features.

20. A method comprising:

determining, with a computer system, a set of features associated in memory of the computer system with a set of vertices of a first directed graph, wherein a feature of the set of features is associated in memory of the computer system with a category type comprising a set of mutually exclusive categories;

obtaining, with the computer system, a set of feature values associated with the set of vertices, wherein each respective vertex of set of vertices is associated with a respective subset of feature values, wherein:

each feature value is associated with a feature of the set of features, and

the respective subset of feature values comprise a respective category of the set of mutually exclusive categories;

selecting, with the computer system, a first subset of features based on the set of feature values, wherein the selecting comprises:

determining a plurality of candidate subsets of features;

determining a plurality of feature subset scores associated with the plurality of candidate subsets of features based on a category label selected from the set of mutually exclusive categories and the set of feature values; and

selecting the first subset of features based on the plurality of feature subset scores;

performing, with the computer system, a first operation to determine a set of extracted feature values, the first operation comprising:

determining a set of input values by increasing a set of feature values associated with the first subset of features with a set of weights; and

determining the set of extracted feature values based on the set of input values, wherein the set of extracted feature values comprises a first multidimensional vector associated with the first directed graph;

determining, with the computer system, a metric based on a distance between the first multidimensional vector and a second multidimensional vector of a second directed graph;

determining, with the computer system, whether the metric satisfies a first threshold; and

storing, with the computer system, the metric in persistent storage.

Assignments (1)

Continuity (16)

References Cited (201)

Cited By (2)

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