Dropbox had 18 patents in artificial intelligence during Q2 2024. Dropbox Inc’s patents filed in Q2 2024 focus on systems and methods for generating content clusters, storing desktop scene layouts, determining chronotypes for users, detecting scene types in media content, displaying differences between versions of content items, and providing file organization scores for folders. These technologies aim to enhance user experience and organization within the Dropbox platform. GlobalData’s report on Dropbox gives a 360-degree view of the company including its patenting strategy. Buy the report here.
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Dropbox had no grants in artificial intelligence as a theme in Q2 2024.
Recent Patents
Application: Generating digital content clusters based on web activity (Patent ID: US20240202250A1)
Dropbox Inc. has filed a patent for systems and methods that involve generating content clusters and collections based on topic and focus data, storing and restoring desktop scene layouts, and arranging video call scenes. The systems can create content clusters by grouping digital content items according to topic and focus data, generate content collections for user accounts, and store and restore desktop scene layouts for application windows. Additionally, the systems can arrange specific desktop scene layouts for video call scenes. The patent also includes claims detailing methods for identifying topic data, determining focus data, generating content clusters using machine learning models, calculating return visit likelihood scores, and providing visual indicators for content items within clusters.
The patent claims outline a method for identifying topic data and focus data associated with digital content items, generating content clusters based on this data, determining return visit likelihood scores for items within clusters, and providing visual indicators for easy access to content items. The claims also specify steps for identifying topic data through titles, keywords, and objects within content items, determining focus data by analyzing user activity patterns, and utilizing machine learning models to group content items. Furthermore, the claims detail methods for calculating return visit likelihood scores based on topic and focus data, restoring content items based on user interactions, and displaying content clusters through visual representations in a cluster graph.
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