US 12,001,786 B2
Applied artificial intelligence technology for natural language generation using a graph data structure with follow-up capabilities
Mauro Eduardo Ignacio Mujica-Parodi, III, Chicago, IL (US); Nathan Drew Nichols, Chicago, IL (US); Nathan William Krapf, Chicago, IL (US); and Brendan Robert Gimby, Park City, UT (US)
Assigned to Salesforce, Inc., San Francisco, CA (US)
Filed by Salesforce, Inc., San Francisco, CA (US)
Filed on May 20, 2022, as Appl. No. 17/749,561.
Claims priority of provisional application 63/192,396, filed on May 24, 2021.
Prior Publication US 2024/0143914 A1, May 2, 2024
Int. Cl. G06F 40/20 (2020.01); G06F 16/242 (2019.01); G06F 16/901 (2019.01); G06N 5/02 (2023.01)
CPC G06F 40/20 (2020.01) [G06F 16/2428 (2019.01); G06F 16/243 (2019.01); G06F 16/9024 (2019.01); G06N 5/02 (2013.01)] 20 Claims
OG exemplary drawing
 
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 (1) traverse the graph data structure to determine which of the nodes to use for content to be expressed in the natural language narratives and (2) determine which of the nodes to use for providing follow-up information about the natural language narratives based on the links of the graph data structure so that the follow-up information is derived from nodes that are linked to the nodes used for the content to be expressed in the natural language narrative.