Abstract: A modular open system architecture for common intelligence picture generation is disclosed. The system receives intelligence requirements through a multimodal artificial intelligence system and calculates collection feasibility across multiple intelligence sources based on physical and temporal conditions. The system develops integrated collection plans through the containerized analytics workbench using containerized analytics modules and processes intelligence through GPU-accelerated deep learning models for automated target recognition. Satellite collection is orchestrated through satellite data acquisition optimization platform by evaluating weather conditions, orbital parameters, and sensor capabilities, while space domain awareness is maintained through space domain awareness system for real-time collection asset management. Multi-source intelligence data is fused through multi-source intelligence fusion system to populate a common intelligence picture.

Abstract: Users collect digital media items such as songs, images, and videos into media libraries. Over time, the user can collect a very large number of media items making organization and use of the media library difficult and time-consuming. The systems and methods described herein alleviate this task by collecting metadata about the media items from multiple sources, determining a similarity between the media items, and clustering the media items with like media items. The systems and methods described herein can position the media items relative to one another in a layout based on their respective similarity. Feedback from the user and from other users can be added to the metadata and used to update the layout of the media items.