Abstract:
An example computer-implemented method includes determining, by an electronic device, that the electronic device has not received a user activity for an interval of time. The method also includes determining, by the electronic device, a contextual state of the electronic device, and adapting, by the electronic device, a sleep delay value based on the determined contextual state of the electronic device. The method also includes determining that the interval of time has exceeded the sleep delay value, and responsive to determining that the interval of time has exceeded the sleep delay value, transitioning, by the electronic device, from a first power state to a second power state, where the first power state is higher or lower than the second power state.
Abstract:
In some implementations, a mobile device can be configured to monitor environmental, system and user events associated with the mobile device and/or a peer device. The occurrence of one or more events can trigger adjustments to system settings. The mobile device can be configured to keep frequently invoked applications up to date based on a forecast of predicted invocations by the user. In some implementations, the mobile device can receive push notifications associated with applications that indicate that new content is available for the applications to download. The mobile device can launch the applications associated with the push notifications in the background and download the new content. In some implementations, before running an application or communicating with a peer device, the mobile device can be configured to check energy and data budgets and environmental conditions of the mobile device and/or a peer device to ensure a high quality user experience.
Abstract:
Pacer activity data of a user may be managed. For example, historical activity data of a user corresponding to a particular time of a day prior to a current day may be received. Additionally, a user interface configured to display an activity goal of the user may be generated and the user interface may be provided for presentation. In some aspects, the user interface may be configured to display a first indicator that identifies cumulative progress towards the activity goal and a second indicator that identifies predicted cumulative progress towards the activity goal. The cumulative progress may be calculated based on monitored activity from a start of the current day to the particular time of the current day and the predicted cumulative progress may be calculated based on the received historical activity data corresponding to the particular time of the day prior to the current day.
Abstract:
In some implementations, a mobile device can be configured to monitor environmental, system and user events. The occurrence of one or more events can trigger adjustments to system settings. In some implementations, the mobile device can be configured to keep frequently invoked applications up to date based on a forecast of predicted invocations by the user. In some implementations, the mobile device can receive push notifications associated with applications that indicate that new content is available for the applications to download. The mobile device can launch the applications associated with the push notifications in the background and download the new content. In some implementations, before running an application or accessing a network interface, the mobile device can be configured to check energy and data budgets and environmental conditions of the mobile device to preserve a high quality user experience.
Abstract:
Systems and methods are disclosed for improving search results returned to a user from one or more domains, utilizing query features learned locally on the user's device. One or more domains can inform a computing device of one or more features related to a search query upon which to the computing device can apply local learning. A local search system can include a local database, a local search history and feedback history database, and a local learning system to identify features about query terms. The features can be learned from the user's interaction with both local search results and remote search results, without sending the user interaction information or other user identification information to a remote search engine. A locally learned feature can be used to extend a query, bias a query term, or filter query results.
Abstract:
Techniques for power management of a portable device are described herein. According to one embodiment, a user agent of an operating system executed within a portable device is configured to monitor activities of programs running within the portable device and to predict user intent at a given point in time and possible subsequent user interaction with the portable device based on the activities of the program. Power management logic is configured to adjust power consumption of the portable device based on the predicted user intent and subsequent user interaction of the portable device, such that remaining power capacity of a battery of the portable device satisfies intended usage of the portable device.
Abstract:
The subject technology transforms a machine learning model into a transformed machine learning model in accordance with a particular model specification when the machine learning model does not conform to the particular model specification, the particular model specification being compatible with an integrated development environment (IDE). The subject technology generates a code interface and code for the transformed machine learning model, the code interface including code statements in the object oriented programming language, the code statements corresponding to an object representing the transformed machine learning model. Further, the subject technology provides the generated code interface and the code for display in the IDE, the IDE enabling modifying of the generated code interface and the code.
Abstract:
Embodiments described herein provide for a non-transitory machine-readable medium storing instructions to cause one or more processors to receive, at a client device, a machine learning model from a server, detect a usage pattern for a content item, store an association between the content item and the detected usage pattern in local data, train the machine learning model using local data for the content item with the detected usage pattern to generate a trained machine learning model, generate an update for the machine learning model, privatize the update for the machine learning model, and transmit the privatized update for the machine learning model to the server.
Abstract:
Systems and methods are disclosed for advising a user when an energy storage device in a computing system needs charging. State of charge data of the energy storage device can be measured and stored at regular intervals. The historic state of charge data can be queried over a plurality of intervals and a state of charge curve generated that is representative of a user's charging habits over time. The state of charge curve can be used to generate a rate of charge histogram and an acceleration of charge histogram. These can be used to predict when a user will charge next, and whether the energy storage device will have an amount of energy below a predetermined threshold amount before the next predicted charging time. A first device can determine when a second device typically charges and whether the energy storage device in the second device will have an amount of energy below the predetermined threshold amount before the next predicted charge time for the second device. The first device can generate an advice to charge notification to the user on either, or both, devices.
Abstract:
The subject technology provides for generating machine learning (ML) model code from a ML document file, the ML document file being in a first data format, the ML document file being converted to code in an object oriented programming language different than the first data format. The subject technology further provides for receiving additional code that calls a function provided by the ML model code. The subject technology compiles the ML model code and the additional code, the compiled ML model code including object code corresponding to the compiled ML model code and the compiled additional code including object code corresponding to the additional code. The subject technology generates a package including the compiled ML model code and the compiled additional code. Further, the subject technology sends the package to a runtime environment on a target device for execution.