Abstract: 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 For example, some views can intentionally be left out of date while other views can be updated more frequently to satisfy requests for such views.

Abstract: 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.

Abstract: An electronic device can include a power system including a battery and a processor programmed to: receive synchronized context data from one or more other devices associated with a user of the device, determine, at least in part based on the synchronized context data, one or more battery charging intervals, and operate the power system to charge the battery from the external power source during the identified one or more battery charging intervals. The processor can be programmed to determine the one or more battery charging intervals using a machine learning model. The synchronized context data can provide indication of the user's location. If the synchronized context data indicates that the user is at a different location than the device, the one or more battery charging intervals determined based at least in part on an expected time for the user to return to the location of the device.

Abstract: Synchronizing context information between a plurality of electronic devices associated with a user, each device including one or more processors, communication interfaces, and memory or storage, can be performed by at least one of the devices and can include subscribing to one or more contexts, each context corresponding one or more properties, statuses, or other information corresponding to another of the plurality of electronic devices; and receiving periodic updates of the one or more subscribed contexts from a data store shared or distributed among the plurality of devices, wherein receiving periodic updates comprises pulling the periodic updates from the data store or receiving pushed updates from the data store. The subscribed contexts can be filtered based on at least one of relevance or importance, wherein the relevance or importance of each context controls the frequency, scheduling, and prioritization of updates for that context.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.