Abstract: Ad hoc data backup for mobile devices is disclosed. When the user of a mobile device has poor or no data connectivity with a network-based storage system and friends are identified that are in the vicinity of the user, backup data is transferred from the user's mobile device to one or more of the friend devices using peer-to-peer connections.

Abstract: Techniques for mobile devices to subscribe and share raw sensor data are provided. The raw sensor data associated with sensors (e.g., accelerometers, gyroscopes, compasses, pedometers, pressure sensors, audio sensors, light sensors, barometers) of a mobile device can be used to determine the movement or activity of a user. By sharing the raw or compressed sensor data with other computing devices, the other computing devices can determine a motion state based on the sensor data. Additionally, in some instances, the other computing devices can determine a functional state based on the sensor data and the motion state. For example, functional state classification can be associated with each motion state (e.g., driving, walking) by further describing each motion state (e.g., walking on rough terrain, driving while texting).

Abstract: Techniques for controlling motions using motion recognizers generated in advance by users are described. According to embodiment, the motion recognizers created by end users are utilized to control virtual objects displayed in a virtual environment. By manipulating one or more motion sensitive devices, end users could command what the objects to do in the virtual environment. Motion signals from each of the motion sensitive devices are recognized in accordance with the motion recognizers created in advance by the users. One or more of the motion signals are at the same time utilized to tune the motion recognizers or create additional motion recognizers. As a result, the motion recognizers are constantly updated to be more accommodating to the user(s).

Abstract: A humanized navigation system provides humanized instructions that mimic a real human navigator, focuses on comprehension rather than precision, and attempts to make the navigation session less stressful for the user. In some implementations, complex navigation situations are classified according to shared common navigation problems. Once a class is determined, humanized navigation instructions are generated and/or selected based on the class and the current location of the user. The humanized navigation instructions include information to aid the user in navigating a route.

Abstract: In some implementations, a mobile device can be configured to provide simplified audio navigation instructions. The simplified audio navigation instructions can provide a reduced set of audio navigation instructions so that the audio instructions are only presented to the user when the user wishes to or needs to hear the instructions. A user can enable the simplified audio navigation instructions. The simplified audio navigation instructions can be enabled automatically. The simplified audio navigation instructions can be configured with rules for when to present audio navigation instructions. For example, the rules can specify that audio navigation instructions are to be provided for complex road segments, a user defined portion of a route, or specified road types, among other criteria. The mobile device can be configured with exceptions to the rules such that audio navigation instructions can be presented when the user has, for example, deviated from a defined route.

Abstract: Methods and mobile devices determine an exit from a vehicle. Sensors of a mobile device can be used to determine when the user is in a vehicle that is driving. The same or different sensors can be used to identify a disturbance (e.g., loss of communication connection from mobile device to a car computer). After the disturbance, an exit confidence score can be determined at various times, and compared to a threshold. A determination of the exit of the user can be determined based on the comparison of the exit confidence score to the threshold. The mobile device can perform one or more functions in response to the exit confidence score exceeding the threshold, such as changing a user interface (e.g., of a navigation app) or obtaining a location to designate a parking location.

Abstract: Methods, program products, and systems of motion-based device operations are described. A mobile device can coordinate operations of a motion sensor and a proximity sensor. The mobile device can determine a gesture event using the motion sensor. The mobile device can determine a proximity event using the proximity sensor. The mobile device can use the gesture event and proximity event to confirm one another, and determine that the mobile device has moved in proximity to a target object following a specified gesture. Upon confirmation, the mobile device can perform a specified task.

Abstract: Motion sensors of a mobile device mounted to a vehicle are used to detect a mount angle of the mobile device. The motion sensors are used to determine whether the vehicle is accelerating or de-accelerating, whether the vehicle is turning and whether the mount angle of the mobile device is rotating. The mount angle of the mobile device is obtained from data output from the motion sensors and can be used to correct a compass heading. Data from the motion sensors that are obtained while the vehicle is turning or the mobile device is rotating are not used to obtain the mount angle.

Abstract: Implementations are disclosed for validating data retrieved from a calibration database. In some implementations, calibrated magnetometer data for a magnetometer of a mobile device is retrieved from a calibration database and validated by data from another positioning system, such as course or heading data provided by a satellite-based positioning system. In some implementations, one or more context keys are used to retrieve magnetometer calibration data from a calibration database that is valid for a particular context of the mobile device, such as when the mobile device is mounted in a vehicle. In some implementations, currently retrieved calibration data is compared with previously retrieved calibration data to determine if the currently retrieved calibration data is valid.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining a presumed activity associated with a mobile device. A plurality of sensor values detected by one or more sensors onboard the mobile device is received over a period of time. A plurality of derived values is calculated from the plurality of sensor values. The derived values are selectively combined to generate one or more abstract values. A presumed activity is identified from a plurality of possible activities based on a level of similarity between the one or more abstract values and expected values of each of the plurality of possible activities that correspond to the one or more abstract values.

Abstract: In some implementations, a mobile device can be configured to provide navigation instructions to a user of the mobile device. The navigation instructions can be graphical, textual or audio instructions. The presentation of the navigation instructions can be dynamically adjusted based the importance of individual instructions and/or environmental conditions. For example, each navigation instruction can be associated with an importance value indicating how important the instruction is. The volume of important audio instructions can be adjusted (e.g., increased) to compensate for ambient noise so that a user will be more likely to hear the navigation instruction. The timing and/or repetition of the presentation of important instructions can be adjusted based on weather conditions, traffic conditions, or road conditions and/or road features so that a user will be less likely to miss an important navigation instruction.

Abstract: A real-time calibration system and method for a mobile device having an onboard magnetometer uses an estimator to estimate magnetometer calibration parameters and a magnetic field external to the mobile device (e.g., the earth magnetic field). The calibration parameters can be used to calibrate uncalibrated magnetometer readings output from the onboard magnetometer. The external magnetic field can be modeled as a weighted combination of a past estimate of the external magnetic field and the asymptotic mean of that magnetic field, perturbed by a random noise (e.g., Gaussian random noise). The weight can be adjusted based on a measure of the statistical uncertainty of the estimated calibration parameters and the estimated external magnetic field. The asymptotic mean of the external magnetic field can be modeled as a time average of the estimated external magnetic field.

Abstract: In some implementations, a mobile device can be configured to provide navigation instructions to a user of the mobile device. The navigation instructions can be graphical, textual or audio instructions. The presentation of the navigation instructions can be dynamically adjusted based the importance of individual instructions and/or environmental conditions. For example, each navigation instruction can be associated with an importance value indicating how important the instruction is. The volume of important audio instructions can be adjusted (e.g., increased) to compensate for ambient noise so that a user will be more likely to hear the navigation instruction. The timing and/or repetition of the presentation of important instructions can be adjusted based on weather conditions, traffic conditions, or road conditions and/or road features so that a user will be less likely to miss an important navigation instruction.

Abstract: A real-time calibration system and method for a mobile device having an onboard magnetometer uses an estimator to estimate magnetometer calibration parameters and a magnetic field external to the mobile device (e.g., the earth magnetic field). The calibration parameters can be used to calibrate uncalibrated magnetometer readings output from the onboard magnetometer. The external magnetic field can be modeled as a weighted combination of a past estimate of the external magnetic field and the asymptotic mean of that magnetic field, perturbed by a random noise (e.g., Gaussian random noise). The weight can be adjusted based on a measure of the statistical uncertainty of the estimated calibration parameters and the estimated external magnetic field. The asymptotic mean of the external magnetic field can be modeled as a time average of the estimated external magnetic field. are within the scope of the following claims.

Abstract: A video gaming system includes a wireless controller that senses linear and angular acceleration to calculate paths of controller movement over a broad range of controller motion. The system also includes an electromagnetic alignment element, such as a set of LEDS. The controller includes an additional sensor to sense light from the LEDs over a relatively restricted range of controller motion, and use this sensed light to dynamically calibrate the controller when the controller passes through the restricted range of motion over which the sensor senses the light.

Abstract: In some implementations, a computer-implemented method includes receiving a reading from a magnetometer of a mobile device. A cluster from a plurality of clusters of bias offsets generated from previously-calibrated readings is selected. The selected cluster has a representative bias offset, a mean of magnitudes in the selected cluster, and a magnitude threshold. An external magnetic field is estimated based on the reading and the representative bias offset for the selected cluster. Whether a magnitude of the estimated external field is within a magnitude range defined by the mean magnitude and the mean magnitude plus the magnitude threshold is determined.

Abstract: Techniques for interpreting orientation invariant motion are disclosed. Unlike a prior art controller that has a specific physical design to induce or force a user to grip the controller in a consistent way, a disclosed controller does not have such a physical design and allows a user to grip the controller in any way that is comfortable to the user. One or more transformations or rotations are designed to transform or rotate readings from inertial sensors housed in the controller to readings independent from how the controller is being gripped by a user.

Abstract: Sensor measurements are used to detect when a device incorporating the sensor is stationary. While the device is stationary, sensor measurements at a current device temperature are used to estimate model parameters. The model parameters can be used in a state estimator to provide an estimated attitude that can be provided to other applications. In some implementations, the estimated attitude can be used to mitigate interference in other sensor measurements.

Abstract: In general, in one aspect, a method includes receiving data from one or more motion sensors of a mobile device and calculating, after the period of time, a statistical measurement of the motion sensor data. The method also includes comparing the calculated statistical measurement to one or more threshold values, and, based on the comparing, determining a dynamic state of the mobile device. The method also includes, based on the determined dynamic state, determining an orientation of the mobile device.

Abstract: The embodiments described relate to techniques and systems for utilizing a portable electronic device to monitor, process, present and manage data captured by a series of sensors and location awareness technologies to provide a context aware map and navigation application. The context aware map application offers a user interface including visual and audio input and output, and provides several map modes that can change based upon context determined by data captured by a series of sensors and location awareness technologies.