Apple Inc.

Alexander D Palmer

Jahshan A Bhatti

Adam A Sonnanstine

John L Blatz

Joseph E Meyer

Joao Pedro Lacerda

Aaron Cotter

Kartik R Venkatraman

Archana Venkatesh

Jingran Zhou

Gina B Lu

Michael J Zhu

Michael G Abood

Sumit Wattal

Some techniques are described herein for tracking items across different devices. Such techniques synchronize a portion of each item while locally storing another portion. In some examples, the synchronization is performed via a conflict-free replicated data type (CRDT) and includes exchanging state vectors and/or hashes corresponding to portions of items that are being synchronized. Other techniques incrementally update local views of data as new data is received. Such techniques use storage plans that maintain operations for different local views. Other techniques manage the order of updates of different local views of data using various sets of criteria described herein. For example, some views can intentionally be left out of date while other views can be updated more frequently to satisfy requests for such views.

Techniques for managing data

Reducing power consumption in an electronic device can include analyzing device usage data associated with the device to predict an extended period of user inactivity, the usage data including at least one of historical usage data and present usage signals and entering an enhanced reduced power state by implementing one or more power saving optimizations for at least a portion the extended period of extended user inactivity, the one or more power saving optimizations slowing, delaying, or interrupting one or more normal activities normally performed by the device. The method can further include exiting the enhanced reduced power state by suspending the one or more power saving optimizations. Exiting the enhanced reduced power state can be performed in response to at least one of: user activity; a specified user routine; or a time predicted by the analyzing device usage data.

Energy saving for battery powered devices

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

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San Jose, CA, United States of America

Alexander D Palmer