Apple Inc.

Chiraag Sumanth

Joao Pedro Lacerda

Joseph E Meyer

Marc Jason Krochmal

John Joseph Iarocci

Gokcen Cilingir

Jacob S Klapper

Saeed Abbasi Moghaddam

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.

Sharing routine for suggesting applications to share content from host application

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.

People suggester using historical interactions on a device

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.

Feature determination for machine learning to suggest applications/recipients

Growing community of inventors

Mountain View, CA, United States of America

Chiraag Sumanth