Abstract: 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.

Abstract: 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.

Abstract: Artificial intelligence (AI) technology can be used in combination with composable communication goal statements and an ontology 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. 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.

Abstract: Artificial intelligence (AI) technology can be used in combination with composable communication goal statements and an ontology 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. 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. The AI technology permits multiple communication goal statements to be arranged into a story outline that defines a narrative structure for the narrative story.

Abstract: Artificial intelligence (AI) technology can be used in combination with composable communication goal statements and an ontology 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. 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. The AI NLG computer system can also build and update the ontology concurrently with a user's composition of communication goal statements.

Abstract: Artificial intelligence (AI) technology can be used in combination with composable communication goal statements and an ontology 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. 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. The AI technology also permits story edits to cause corresponding updates to an ontology and/or story outline used to support narrative generation.

Abstract: Features are disclosed for selecting preferred content request modes on a client computing device when initiating content requests. The request modes may correspond to direct requests (e.g., requests made from a client device directly to a content sever hosting requested content) or to indirect requests (e.g., requests made from the client device to the content server via an intermediary system). The preferred request modes made be based on a statistical analysis of performance data (e.g., prior content load times) observed or recorded by the client computing device in connection with prior content requests. Randomly selected request modes may be used to provide additional data for performance analysis.

Abstract: Artificial intelligence (AI) technology can be used in combination with composable communication goal statements and an ontology 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. 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.

Abstract: Artificial intelligence (AI) technology can be used in combination with composable communication goal statements and an ontology 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. 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. The determined content can be arranged in a computed story outline that is created at runtime, and NLG can be performed on the computed story outline to generate the narrative story.

Abstract: Features are disclosed for generating request decision models for use by client computing devices to determine request paths or modes for content requests. The request modes may correspond to direct requests (e.g., requests made from a client device directly to a content server hosting requested content) or to indirect requests (e.g., requests made from the client device to the content server via an intermediary system). The request decision models may be trained by a machine learning algorithm using performance data (e.g., prior content load times), contextual information (e.g., state information associated with devices at times content requests are executed), and the like.

Abstract: Features are disclosed for determining preferred content request modes for client computing devices when initiating content requests. The request modes may correspond to direct requests (e.g., requests made from a client device directly to a content sever hosting requested content) or to indirect requests (e.g., requests made from the client device to the content server via an intermediary system). The preferred request modes made be based on a statistical analysis of performance data (e.g., prior content load times) obtained from one or more client computing devices for a given content item, group of content items (e.g., domain), and the like.

Abstract: Approaches to enable a computing device, such as a phone or tablet computer, to determine when a user viewing the content is being distracted or is generally viewing the content with a sufficient level of irregularity, and present an audible representation of the content during the times when the user is deemed distracted. The determination of when the user is distracted or is otherwise viewing the content with irregularity can be performed using sensor data captured by one or more sensors of the computing device. For example, the computing device may analyze the image data captured by one or more cameras, such as by tracking the movement/location of eye pupils of the user and/or tracking the head movement of the user to detect when the user is distracted.

Abstract: Features are disclosed for enabling users to efficiently store and share browsing sessions or portions thereof with other users or the general public. Browsing session requests and other activities may be sent to an intermediary system, which can retrieve requested content and store a representation of the requested content or data regarding the requested content. The stored data may be organized as a saved browsing session such that users may access the shared browsing session at a subsequent time and view the browsing session substantially in its entirety. Users may search for shared browsing sessions and access data regarding the requests made during a browsing session. In addition, data regarding client devices used during shared browsing sessions may be tracked and associated with the shared browsing sessions such that subsequent users can search for shared browsing sessions based partly on such device characteristics.

Abstract: Approaches to enable a computing device, such as a phone or tablet computer, to determine when a user viewing the content is being distracted or is generally viewing the content with a sufficient level of irregularity, and present an audible representation of the content during the times when the user is deemed distracted. The determination of when the user is distracted or is otherwise viewing the content with irregularity can be performed using sensor data captured by one or more sensors of the computing device. For example, the computing device may analyze the image data captured by one or more cameras, such as by tracking the movement/location of eye pupils of the user and/or tracking the head movement of the user to detect when the user is distracted.

Abstract: Approaches to enable a computing device, such as a phone or tablet computer, to determine when a user viewing the content is being distracted or is generally viewing the content with a sufficient level of irregularity, and present an audible representation of the content during the times when the user is deemed distracted. The determination of when the user is distracted or is otherwise viewing the content with irregularity can be performed using sensor data captured by one or more sensors of the computing device. For example, the computing device may analyze the image data captured by one or more cameras, such as by tracking the movement/location of eye pupils of the user and/or tracking the head movement of the user to detect when the user is distracted.

Abstract: Features are disclosed for determining preferred content request modes for client computing devices when initiating content requests. The request modes may correspond to direct requests (e.g., requests made from a client device directly to a content sever hosting requested content) or to indirect requests (e.g., requests made from the client device to the content server via an intermediary system). The preferred request modes made be based on a statistical analysis of performance data (e.g., prior content load times) obtained from one or more client computing devices for a given content item, group of content items (e.g., domain), and the like.

Abstract: Features are disclosed for generating request decision models for use by client computing devices to determine request paths or modes for content requests. The request modes may correspond to direct requests (e.g., requests made from a client device directly to a content server hosting requested content) or to indirect requests (e.g., requests made from the client device to the content server via an intermediary system). The request decision models may be trained by a machine learning algorithm using performance data (e.g., prior content load times), contextual information (e.g., state information associated with devices at times content requests are executed), and the like.

Abstract: Features are disclosed for selecting preferred content request modes on a client computing device when initiating content requests. The request modes may correspond to direct requests (e.g., requests made from a client device directly to a content sever hosting requested content) or to indirect requests (e.g., requests made from the client device to the content server via an intermediary system). The preferred request modes made be based on a statistical analysis of performance data (e.g., prior content load times) observed or recorded by the client computing device in connection with prior content requests. Randomly selected request modes may be used to provide additional data for performance analysis.