Applied Artificial Intelligence Technology for Natural Language Generation Using a Graph Data Structure and Configurable Chooser Code

Patent metadata

Field	Value
Publication number	US-2024135110-A1
Application number	US-202217749532-A
Country	US
Kind code	A1
Filing date	May 19, 2022
Priority date	May 24, 2021
Publication date	Apr 25, 2024
Grant date	—

Abstract

Natural language generation technology is disclosed that applies artificial intelligence to structured data to determine content for expression in natural language narratives that describe the structured data. A graph data structure is employed, where the graph data structure comprises a plurality of nodes. Each of a plurality of the nodes (1) represents a corresponding intent so that a plurality of different nodes represent different corresponding intents and (2) is associated with one or more links to one or more of the nodes to define relationships among the intents. A processor executes chooser code based on a plurality of operating rules and/or parameters that control how the chooser code traverses the graph data structure to determine which of the nodes to use for content to be expressed in the natural language narratives, wherein the operating rules and/or parameters are configurable to change strategies for choosing which nodes are used for the content to be expressed in the natural language narratives.

First claim

What is claimed is:

1 . A natural language generation (NLG) system that applies artificial intelligence to structured data to determine content to be expressed in natural language narratives that describe the structured data, the system comprising:
a processor; and a memory; wherein the memory is configured to store a graph data structure, wherein the graph data structure comprises a plurality of nodes, wherein each of a plurality of the nodes (1) represents a corresponding intent so that a plurality of different nodes represent different corresponding intents and (2) is associated with one or more links to one or more of the nodes to define relationships among the intents; and wherein the processor is configured to execute chooser code based on a plurality of operating rules and/or parameters that control how the chooser code traverses the graph data structure to determine which of the nodes to use for content to be expressed in the natural language narratives, wherein the operating rules and/or parameters are configurable to change strategies for choosing which nodes are used for the content to be expressed in the natural language narratives.

2 . The system of claim 1 wherein the operating rules and/or parameters are configurable to adjust a size for the natural language narratives.

3 . The system of claim 1 wherein the operating rules and/or parameters are configurable to adjust which of the nodes are used for the content based on importance values assigned to results from the nodes.

4 . The system of claim 1 wherein the operating rules and/or parameters are configurable to adjust which of the nodes are used for the content based on interestingness values assigned to results from the nodes.

5 . The system of claim 1 wherein the operating rules and/or parameters are configurable to adjust which of the nodes are used for the content based on characterizations assigned to results from the nodes.

6 . The system of claim 5 wherein the characterizations comprise positive characterizations and/or negative characterizations.

7 . The system of claim 1 wherein the operating rules and/or parameters are configurable to define which of the links are to be used when traversing the knowledge graph.

8 . The system of claim 1 wherein the operating rules and/or parameters are configurable based on a user profile and/or training data associated with a user to whom a natural language narrative is to be presented.

9 . The system of claim 8 wherein the user profile and/or training data defines a size for the natural language narratives.

10 . The system of claim 8 wherein the user profile and/or training data defines a weighting to be applied to results produced from a plurality of nodes of the traversed graph data structure.

11 . The system of claim 1 wherein the graph data structure is adjustable to add one or more additional nodes to the graph data structure.

12 . The system of claim 1 wherein the graph data structure is adjustable modify one or more of the nodes.

13 . The system of claim 1 wherein the graph data structure is parameterized based on the structured data.

14 . The system of claim 1 wherein the graph data structure comprises an authoring graph.

15 . The system of claim 14 wherein the authoring graph is parameterized based on the structured data to define a knowledge graph, wherein the chooser code operates on the knowledge graph to determine content for expression in the natural language narratives.

16 . The system of claim 1 wherein the processor is configured to generate a plurality of the natural language narratives in an interactive mode based on conversational inputs from users.

17 . The system of claim 1 wherein the processor comprises a plurality of processors.

18 . A natural language generation (NLG) method that applies artificial intelligence to structured data to determine content to be expressed in natural language narratives that describe the structured data, the method comprising:
a processor accessing a graph data structure in memory, wherein the graph data structure comprises a plurality of nodes, wherein each of a plurality of the nodes (1) represents a corresponding intent so that a plurality of different nodes represent different corresponding intents and (2) is associated with one or more links to one or more of the nodes to define relationships among the intents; and the processor executing chooser code based on a plurality of operating rules and/or parameters that control how the chooser code traverses the graph data structure to determine which of the nodes to use for content to be expressed in the natural language narratives, wherein the operating rules and/or parameters are configurable to change strategies for choosing which nodes are used for the content to be expressed in the natural language narratives.

19 . An article of manufacture for natural language generation (NLG) that applies artificial intelligence to structured data to determine content to be expressed in natural language narratives that describe the structured data, the article of manufacture comprising:
machine-readable code that is resident on a non-transitory computer-readable storage medium, wherein the code is executable by a processor to cause the processor to:
access a graph data structure in memory, wherein the graph data structure comprises a plurality of nodes, wherein each of a plurality of the nodes (1) represents a corresponding intent so that a plurality of different nodes represent different corresponding intents and (2) is associated with one or more links to one or more of the nodes to define relationships among the intents; and
execute chooser code based on a plurality of operating rules and/or parameters that control how the chooser code traverses the graph data structure to determine which of the nodes to use for content to be expressed in the natural language narratives, wherein the operating rules and/or parameters are configurable to change strategies for choosing which nodes are used for the content to be expressed in the natural language narratives.

20 . The article of manufacture of claim 19 wherein the operating rules and/or parameters are configurable to adjust which of the nodes are used for the content based on (1) importance values assigned to results from the nodes and/or (2) interestingness values assigned to results from the nodes.

Assignees

• Narrative Science Inc

Inventors

• Mujica-Parodi Iii Mauro Eduardo Ignacio
• Nichols Nathan Drew
• Krapf Nathan William
• Gimby Brendan Robert

Classifications

• G06F16/242

  Query formulation · CPC title

• G06F40/40 Primary

  Processing or translation of natural language (natural language analysis G06F40/20; semantic analysis G06F40/30) · CPC title

• G06F40/35

  Discourse or dialogue representation · CPC title

• G06F16/9024 Primary

  Graphs; Linked lists (G06F16/9027 takes precedence) · CPC title

• G06F16/243 Primary

  Natural language query formulation · CPC title