Abstract: Self-learning systems process incoming data from sources such broadcast, cable, or IP-driven television and can discover topics that broadly describe the incoming data in real-time. These topics can be used to gather and store metadata from various metadata sources such as social networks. Using the metadata, content delivery systems working in parallel with the self-learning systems can deliver highly contextualized supplementary content to client applications, such as mobile devices used as “second screen” devices.

Abstract: Self-learning systems process incoming data from sources such broadcast, cable, or IP-driven television and can discover topics that broadly describe the incoming data in real-time. These topics can be used to gather and store metadata from various metadata sources such as social networks. Using the metadata, content delivery systems working in parallel with the self-learning systems can deliver highly contextualized supplementary content to client applications, such as mobile devices used as “second screen” devices.

Abstract: A virtual reality device can implement varying interactive modes for document viewing and editing by displaying, at an application container level, a current mode view in a view frame of the virtual reality device; and in response to receiving an overview command trigger, determining context, including that the current mode view is at the application container level; expanding to a next level view of, e.g., a task level or an overview level; and displaying, at a next level, the next level view in the view frame of the virtual reality device. The current mode view of the application container level includes a container space of an application and an application container level rule for the container space. Conversely, the virtual reality device can adjust the next level view back to the application container level in response to a focused command trigger and identified region of interest.

Abstract: A virtual reality device can implement varying interactive modes for document viewing and editing by displaying, at an application container level, a current mode view in a view frame of the virtual reality device; and in response to receiving an overview command trigger, determining context, including that the current mode view is at the application container level; expanding to a next level view of, e.g., a task level or an overview level; and displaying, at a next level, the next level view in the view frame of the virtual reality device. The current mode view of the application container level includes a container space of an application and an application container level rule for the container space. Conversely, the virtual reality device can adjust the next level view back to the application container level in response to a focused command trigger and identified region of interest.

Abstract: Self-learning systems process incoming data from sources such broadcast, cable, or IP-driven television and can discover topics that broadly describe the incoming data in real-time. These topics can be used to gather and store metadata from various metadata sources such as social networks. Using the metadata, content delivery systems working in parallel with the self-learning systems can deliver highly contextualized supplementary content to client applications, such as mobile devices used as “second screen” devices.

Abstract: Self-learning systems process incoming data from sources such broadcast, cable, or IP-driven television and can discover topics that broadly describe the incoming data in real-time. These topics can be used to gather and store metadata from various metadata sources such as social networks. Using the metadata, content delivery systems working in parallel with the self-learning systems can deliver highly contextualized supplementary content to client applications, such as mobile devices used as “second screen” devices.

Abstract: Self-learning systems process data in real-time and output the processed data to client applications in an effective manner. They comprise a capture platform that captures data and generates a stream of text, a text decoding server that extracts individual words from the stream of text, an entity extractor that identifies entities, a trending engine that outputs trending results, and a live queue broker that filters the trending results. The self-learning systems provide more efficient realization of Boxfish technologies, and provide or work in conjunction with real-time processing, storage, indexing, and delivery of segmented video. Furthermore, the self-learning systems efficiently perform entity relationing by creating entity network graphs, and are operable to identify advertisements from the data.