Abstract:
A mobile device enables its user to retroactively “check in,” on social media, to locations to which the device has previously been. The mobile device automatically tracks the locations to which it goes during some time interval. As the mobile device goes to each location, the mobile device stores data that specifies that location. Following the time interval, and potentially in response to a request by the device's user to view the locations previously visited, the mobile device presents a list of at least some of the locations on its display. The device's user can select one or more of the presented locations. The selection of a location causes the mobile device to post, to an Internet-based social media service, information pertaining to the selected location. For example, such information can indicate that the device's user had been at the selected location.
Abstract:
Techniques of delivering location data are described. A location server can receive, from a mobile device, a request for location data for determining a location of the mobile device at a venue. The request can include an estimated location of the mobile device. The location server can provide to the mobile device coarse location data for each venue that is located within a threshold distance to the estimated location of the mobile device. The coarse location data can include a list of coarse tiles at each venue, and parameters of a probability distribution function for determining in which tile of the venue the mobile device is located based on signals detected by the mobile device. The location server can the provide location fingerprint data associated with the tile and neighboring tiles to the mobile device. The mobile can use the location fingerprint data to determine a more detailed location.
Abstract:
Methods, program products, and systems for using a location fingerprint database to determine a location of a mobile device are described. A mobile device can use location fingerprint data and readings of a sensor to obtain a location observation. The mobile device can use the location observation in a particle filter for determining a location of the mobile device at a venue. Using state of movement of the mobile device and a map of the venue, the mobile device can determine one or more candidate locations of the device. The mobile device can then update the candidate locations using a next observation, and determine a probability density function based on the candidate locations. The mobile device can then present to a user a most probable location as a current location of the device in the venue.
Abstract:
Techniques for predictive user assistance are described. A mobile device can learn movement patterns of the mobile device. The mobile device can construct a state model that is an abstraction of locations where the mobile device stayed for sufficient amount of time. The state model can include states representing the locations, and transitions representing movement of the mobile device between the locations. The mobile device can use the state model, a current location of the mobile device, and a current time to determine a predicted future location of the mobile device at a given future time. Based on the predicted location and the given future time, the mobile device can predict what assistance a user of the mobile device may request. The mobile device can then provide the assistance to the user before the given future time.
Abstract:
Methods, program products, and systems of location estimation using a probability density function are disclosed. In general, in one aspect, a server can estimate an effective altitude of a wireless access gateway using harvested data. The server can harvest location data from multiple mobile devices. The harvested data can include a location of each mobile device and an identifier of a wireless access gateway that is located within a communication range of the mobile device. The server can calculate an effective altitude of the wireless access gateway using a probability density function of the harvested data. The probability density function can be a sufficient statistic of the received set of location coordinates for calculating an effective altitude of the wireless access gateway. The server can send the effective altitude of the wireless access gateway to other mobile devices for estimating altitudes of the other mobile devices.
Abstract:
A proximity fence can be a location-agnostic fence defined by signal sources having no geographic location information. The proximity fence can correspond to a group of signal sources instead of a point location fixed to latitude and longitude coordinates. A signal source can be a radio frequency (RF) transmitter broadcasting a beacon signal. The beacon signal can include a payload that includes an identifier indicating a category to which the signal source belongs, and one or more labels indicating one or more subcategories to which the signal source belongs. The proximity fence defined by the group of signal sources can trigger different functions of application programs associated with the proximity fence on a mobile device, when the mobile device moves within the proximity fence and enters and exits different parts of the proximity fence corresponding to the different subcategories.
Abstract:
A user interface enables a user to calibrate the position of a three dimensional model with a real-world environment represented by that model. Using a device's sensor suite, the device's location and orientation is determined. A video image of the device's environment is displayed on the device's display. The device overlays a representation of an object from a virtual reality model on the video image. The position of the overlaid representation is determined based on the device's location and orientation. In response to user input, the device adjusts a position of the overlaid representation relative to the video image.
Abstract:
Methods, program products, and systems of location estimation using a probability density function are disclosed. In general, in one aspect, a server can estimate an effective altitude of a wireless access gateway using harvested data. The server can harvest location data from multiple mobile devices. The harvested data can include a location of each mobile device and an identifier of a wireless access gateway that is located within a communication range of the mobile device. The server can calculate an effective altitude of the wireless access gateway using a probability density function of the harvested data. The probability density function can be a sufficient statistic of the received set of location coordinates for calculating an effective altitude of the wireless access gateway. The server can send the effective altitude of the wireless access gateway to other mobile devices for estimating altitudes of the other mobile devices.
Abstract:
Methods, program products, and systems for using a location fingerprint database to determine a location of a mobile device are described. A mobile device can use location fingerprint data received from a server to determine a location of the mobile device at the venue. The mobile device can obtain, from a sensor of the mobile device, a vector of sensor readings, each sensor reading can measure an environment variable, e.g., a signal received by the sensor from a signal source. The mobile device can perform a statistical match between the vector and the location fingerprint data. The mobile device can then estimate a current location of the mobile device based on the statistical match.
Abstract:
Methods, program products, and systems of location estimation using a probability density function are disclosed. In general, in one aspect, a server can estimate an effective altitude of a wireless access gateway using harvested data. The server can harvest location data from multiple mobile devices. The harvested data can include a location of each mobile device and an identifier of a wireless access gateway that is located within a communication range of the mobile device. The server can calculate an effective altitude of the wireless access gateway using a probability density function of the harvested data. The probability density function can be a sufficient statistic of the received set of location coordinates for calculating an effective altitude of the wireless access gateway. The server can send the effective altitude of the wireless access gateway to other mobile devices for estimating altitudes of the other mobile devices.