Narrative Science Inc.

Salesforce, Inc.

Narrative Scienc Inc.

Nathan Drew Nichols

Kristian J Hammond

Andrew R Paley

Lawrence A Birnbaum

Maia Lewis Meza

Michael Justin Smathers

Daniel Joseph Platt

Matthew Lloyd Trahan

Jared Lorince

Lawrence E Adams

Clayton Nicholas Norris

Michael Tien Thinh Pham

Charlie M Truong

Nathan William Krapf

Santiago Santana

Pia Opulencia

Shingo Murata

Brendan Robert Gimby

Mauro Eduardo Ignacio Mujica-Parodi, Iii

Systems and methods are disclosed for integrating NLG-based natural language narrative story generation with story sharing. A processor can (1) generate a plurality of natural language narrative stories based on a plurality of semantic source models for the natural language narrative stories, (2) analyze the semantic source models to determine a plurality of users to whom the natural language narrative stories that are generated from the analyzed semantic source models are to be shared, and (3) share the generated natural language narrative stories with their determined users. In this fashion, stories can be posted to user-customized newsfeeds in a manner that can more reliably capture stories that are of interest to the users.

Applied artificial intelligence technology for integrating natural language narrative generation with newsfeeds

Artificial intelligence (AI) technology can be used in combination with composable communication goal statements to facilitate a user's ability to quickly structure story outlines using 'explanation' communication goals in a manner usable by an NLG narrative generation system without any need for the user to directly author computer code. This AI technology permits NLG systems to determine the appropriate content for inclusion in a narrative story about a data set in a manner that will satisfy a desired explanation communication goal such that the narratives will express various ideas that are deemed relevant to a given explanation communication goal.

Applied artificial intelligence technology for narrative generation based on explanation communication goals

Artificial intelligence (AI) technology can be used in combination with composable communication goal statements to facilitate a user's ability to quickly structure story outlines in a manner usable by an NLG narrative generation system without any need for the user to directly author computer code. Narrative analytics that are linked to communication goal statements can employ a conditional outcome framework that allows the content and structure of resulting narratives to intelligently adapt as a function of the nature of the data under consideration. This AI technology permits NLG systems to determine the appropriate content for inclusion in a narrative story about a data set in a manner that will satisfy a desired communication goal.

Applied artificial intelligence technology for narrative generation based on a conditional outcome framework

Disclosed herein is computer technology that provides adaptive mechanisms for learning concepts that are expressed by natural language sentences, and then applies this learning to appropriately classify new natural language sentences with the relevant concept that they express.

Applied artificial intelligence technology for adaptively classifying sentences based on the concepts they express to improve natural language understanding

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.

Applied artificial intelligence technology for natural language generation using a graph data structure and configurable chooser code

Artificial intelligence (AI) technology can be used process natural language statements to facilitate the automated generation of narratives about data sets that achieve a desired communication goal without any need for a user to directly author computer code. This AI technology permits NLG systems to determine the appropriate content for inclusion in the narrative in a manner that will satisfy the desired communication goal.

Applied artificial intelligence technology for performing natural language generation (NLG) using composable communication goals and ontologies to generate narrative stories

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 story graph is employed, where the story graph comprises a plurality of items of content to be expressed in a natural language narrative. A processor executes code corresponding to any of a plurality of structurers that organize the items of content into different story outlines from which the natural language narratives are generated, wherein the different structurers comprise different operating rules and/or parameters that implement different strategies for organizing the items of content into the story outlines.

Applied artificial intelligence technology for natural language generation using a story graph and different structurers

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 (1) traverses the graph data structure to determine which of the nodes to use for content to be expressed in the natural language narratives and (2) determines 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.

Applied artificial intelligence technology for natural language generation using a graph data structure with follow-up capabilities

Disclosed herein is computer technology that applies natural language processing (NLP) techniques to training data to generate information used to train a natural language generation (NLG) system to produce output that stylistically resembles the training data. In this fashion, the NLG system can be readily trained with training data supplied by a user so that the NLG system is adapted to produce output that stylistically resembles such training data. In an example, an NLP system detects a plurality of linguistic features in the training data. These detected linguistic features are then aggregated into a specification data structure that is arranged for training the NLG system to produce natural language output that stylistically resembles the training data. Parameters in the specification data structure can be linked to objects in an ontology used by the NLG system to facilitate the training of the NLG system based on the detected linguistic features.

Applied artificial intelligence technology for using natural language processing and concept expression templates to train a natural language generation system

Artificial intelligence methods and systems for triggering the generation of narratives are disclosed. Specific embodiments relate to real-time evaluation and automated generation of narrative stories based on received data. For example, data can be tested against data representative of a plurality of story angles to determine whether a narrative story incorporating one or more such story angles is to be automatically generated.

Method and apparatus for triggering the automatic generation of narratives

The exemplary embodiments described herein are related to techniques for automatically generating narratives about data based on communication goal data structures that are associated with configurable content blocks. The use of such communication goal data structures facilitates modes of operation whereby narratives can be generated in real-time and/or interactive manners.

Automatic generation of narratives from data using communication goals and narrative analytics

The exemplary embodiments describe, inter alia, an apparatus comprising: a processor configured to (1) generate a plurality of graphical user interfaces (GUIs) for interaction with a user to support configuration of a narrative story generator to automatically generate a narrative story based on input data, wherein at least one of the GUIs presents content blocks comprising a story outline in a hierarchical structure, (2) evaluate configuration elements of the narrative story generated using imported sample data, and (3) generate narrative stories based on the configuration of the narrative story generator and the input data.

Method and system for configuring automatic generation of narratives from data

Disclosed herein is an NLP system that is able to extract meaning from a natural language message using improved parsing techniques. Such an NLP system can be used in concert with an NLG system to interactively interpret messages and generate response messages in an interactive conversational stream. The parsing can include (1) named entity recognition that contextualizes the meanings of words in a message with reference to a knowledge base of named entities understood by the NLP and NLG systems, (2) syntactically parsing the message to determine a grammatical hierarchy for the named entities within the message, (3) reduction of recognized named entities into aggregations of named entities using the determined grammatical hierarchy and reduction rules to further clarify the message's meaning, and (4) mapping the reduced aggregation of named entities to an intent or meaning, wherein this intent/meaning can be used as control instructions for an NLG process.

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Chicago, IL, United States of America

Nathan Drew Nichols