Abstract: The techniques disclosed herein provide a system for automatically organizing content captures of user activity into collections based on a shared topic. Due to the significant portion of daily life that occurs via personal computing devices, there is increasing need for helpful user experiences to enhance productivity and engagement. Namely, those directed to assisting a user in managing and recalling their past activity. As such, the presented system retrieves content captures depicting a moment of interest. Accordingly, the system performs a visual analysis of each content capture to determine a semantic context and assign one or more topics. This is accomplished by utilizing a pair of machine learning models to identify and extract relevant text information. The extracted text information is then analyzed by a topic classification model to assign the one or more topics. The system further includes graphical user interfaces for viewing and managing collections of content captures.

Abstract: The techniques disclosed herein provide a system for automatically organizing content captures of user activity into collections based on a shared topic. Due to the significant portion of daily life that occurs via personal computing devices, there is increasing need for helpful user experiences to enhance productivity and engagement. Namely, those directed to assisting a user in managing and recalling their past activity. As such, the presented system retrieves content captures depicting a moment of interest. Accordingly, the system performs a visual analysis of each content capture to determine a semantic context and assign one or more topics. This is accomplished by utilizing a pair of machine learning models to identify and extract relevant text information. The extracted text information is then analyzed by a topic classification model to assign the one or more topics. The system further includes graphical user interfaces for viewing and managing collections of content captures.