Abstract: Content objects may be selected for sharing and shared across devices using a sharing routine and suggestion engine to provide suggestions of target applications to share the content objects and recipients with whom the content objects may be shared. A sharing routine may receive and analyze a sharing request and invoke a suggestion engine configured to generate application suggestions and/or recipient suggestions. The sharing routine may receive sets of application suggestions and/or recipient suggestions from a suggestion engine, modify and/or select subsets of the suggestions to be provided to user via a sharing user interface. In response to a user selection of applications and/or recipient via the user interface, the sharing routine may invoke the selected application for sharing, and provide the content object to the selected application.

Abstract: Systems and methods can suggest applications and/or recipients for a user of a computing device to perform communication. The suggestions can be provided on a user interface for a user to select. A suggestion engine can use historical user interactions that include a recipient, a communication application used to communicate with the recipient, and contextual data to determine which application and/or recipients to suggest. The user interactions may occur in a variety of ways, e.g., after a content object has been selected within a host application, where a communication application is selected thereafter. Multiple models may be used to provide the suggestions, such as a pattern model or a cluster model that uses recent user interactions. As another example, a heuristics model may also be used.

Abstract: Systems and methods can suggest applications for use by a user of a computing device. The suggestions can be provided on a user interface for the user to select. A suggestion engine can use historical user interactions and contextual data to derive features for a machine learning mode. The machine learning model can determine which application to suggest according to the current context. Multiple models, such as a pattern model and a heuristics model, may be user to generate features for the machine learning model based in user interactions.

Abstract: The subject technology provides systems and methods for context-based user status indicator selection. In an example, a method includes obtaining, by a first electronic device associated with a first user, status indicators, each of which indicates a respective status of a second user of a second electronic device. Furthermore, the method includes determining, by the first electronic device, a respective relevance priority of each of the status indicators. Based on the determined respective priorities, a subset of the status indicators is selected by the first electronic device and is displayed in a graphical element on the first electronic device.

Abstract: Systems and methods can suggest applications for use by a user of a computing device. The suggestions can be provided on a user interface for the user to select. A suggestion engine can use historical user interactions and contextual data to derive features for a machine learning mode. The machine learning model can determine which application to suggest according to the current context. Multiple models, such as a pattern model and a heuristics model, may be user to generate features for the machine learning model based in user interactions.

Abstract: The subject disclosure provides a machine learning engine trained to recommend, from contacts on a user's device, potential group members to be included in a group with the user. The potential group members can be identified in a privacy preserving manner in which the identification is performed locally at the user's device, using data that is locally stored at the user device. In one or more implementations, a remote server may provide an initial indication to the user's device that potential group members may exist, thereby triggering the local identification of the potential group members for suggestion to the user.

Abstract: Systems and methods can suggest applications and/or recipients for a user of a computing device to perform communication. The suggestions can be provided on a user interface for a user to select. A suggestion engine can use historical user interactions that include a recipient, a communication application used to communicate with the recipient, and contextual data to determine which application and/or recipients to suggest. The user interactions may occur in a variety of ways, e.g., after a content object has been selected within a host application, where a communication application is selected thereafter. Multiple models may be used to provide the suggestions, such as a pattern model or a cluster model that uses recent user interactions. As another example, a heuristics model may also be used.

Abstract: Content objects may be selected for sharing and shared across devices using a sharing routine and suggestion engine to provide suggestions of target applications to share the content objects and recipients with whom the content objects may be shared. A sharing routine may receive and analyze a sharing request and invoke a suggestion engine configured to generate application suggestions and/or recipient suggestions. The sharing routine may receive sets of application suggestions and/or recipient suggestions from a suggestion engine, modify and/or select subsets of the suggestions to be provided to user via a sharing user interface. In response to a user selection of applications and/or recipient via the user interface, the sharing routine may invoke the selected application for sharing, and provide the content object to the selected application.