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.

Abstract: Disclosed herein is a system that automatically correlates related instances of written feedback associated with a computer product to relevant telemetry data so that a performance and/or reliability issue associated with the computer product can be identified and fixed in a more efficient manner. The system applies a natural language processing model to instances of written feedback that have been received for the computer product to identify a cluster of instances of written feedback which describe the same issue. For each instance of written feedback in the cluster, the system uses an identification and a timestamp to retrieve a telemetry event log. The system analyzes the telemetry event logs to identify a common telemetry event. This effectively correlates meaningful written descriptions with common telemetry behavior(s). Information regarding this correlation is provided to a computing device of a user (e.g., a developer) tasked with examining the performance and/or reliability issue.