Apple Inc.

Joao Pedro Lacerda

Joseph E Meyer

Chiraag Sumanth

Gaurav Kapoor

Jahshan A Bhatti

Adam A Sonnanstine

John L Blatz

Alexander D Palmer

Marc Jason Krochmal

John Joseph Iarocci

Gokcen Cilingir

Brian E Korver

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

The embodiments set forth techniques for implementing various 'prediction engines' that can be configured to provide different kinds of predictions within a mobile computing device. According to some embodiments, each prediction engine can assign itself as an 'expert' on one or more &#x201c;prediction categories&#x201d; within the mobile computing device. When a software application issues a request for a prediction for a particular category, and two or more prediction engines respond with their respective prediction(s), a &#x201c;prediction center&#x201d; can be configured to receive and process the predictions prior to responding to the request. Processing the predictions can involve removing duplicate information that exists across the predictions, sorting the predictions in accordance with confidence levels advertised by the prediction engines, and the like. In this manner, the prediction center can distill multiple predictions down into an optimized prediction and provide the optimized prediction to the software application.

Systems and methods for providing predictions to applications executing on a computing device

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

Techniques are disclosed for predictive media streaming using microlocation. Microlocations of a mobile device can be determined by measuring one or more sensor values at one or more times, the one or more sensor values are determined from one or more signals emitted by a corresponding one or more signal sources. Streaming events can be stored at the mobile device. Each streaming event may include a destination device for playing media and a cluster location, the cluster location corresponding to sensor values that are spatially near each other. A selection of a media item is detected and one or more current sensor values are measured. A current cluster location can be identified using the one or more current sensor value. The current cluster location and the streaming events can identify a particular destination device for playing the selected media item.

Predictive routing based on microlocation

A device implementing adaptive, cross-device event data synchronization includes at least one processor to maintain a database on the device, the database storing event data items derived from user data items corresponding to user activity on the device or a second device. The at least one processor periodically transmits, to a server, first insertion records corresponding to new event data items generated at the device, the transmission periodicity based on a frequency at which the new event data items are generated. The at least one processor, upon detecting deletion of one user data item, deletes, from the database, one corresponding event data item, and immediately transmits, to the server for propagation to the second device, a first deletion record for deletion of one corresponding event data item at the second device, the first deletion record being transmitted along with any of the first insertion records awaiting transmission.

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Santa Cruz, CA, United States of America

Joao Pedro Lacerda