Apple Inc.

Joseph Ding-Jiu Huang

Robert William Mayor

David Benjamin Millman

Darin Tay

Vitali Lovich

Abhinav R Patel

Jessica Noel Tsoong

Ashish Agarwal

Brian Stephen Smith

Mithilesh Kumar

Ilya K Veygman

Vojislav Samsalovic

Coarse location estimation for mobile devices is disclosed for detecting mobile device presence at general locations of interest and switching operating modes and services for one or more location context aware applications. In some implementations, sensor data is received from a plurality of data sources at a location. For each data source, a first probability is estimated that the mobile device is at the location based on sensor data from the data sources. A second probability is estimated that the mobile device is not at the location based on sensor data from the data sources. The first and second estimated probabilities are statistically combined to generate a third estimated probability that the mobile device is at the location.

Coarse location estimation for mobile devices

Methods, systems, and computer program product for determining a building floor level are described. A mobile device can use wireless signal sources and location fingerprint data to determine a level of a building floor on which the mobile device is located. The location fingerprint data can include or be associated with a list and a count of wireless signal sources previously detected on each floor. The mobile device can compare the list and count with wireless signal sources detected by the mobile device, and use results of the comparison to configure a statistical filter that determines a location of the mobile device. The mobile device can then determine the location, including a building floor level, using the statistical filter.

Floor level determination

Methods, systems, and computer program products for correcting in-venue location estimation using structural information are described. A mobile device can use wireless location technologies and dead reckoning to determine an estimated location of the mobile device in a venue. The mobile device can compare the estimated location with a map of the venue. Upon determining that the estimated location conflicts with a structural constraint, the mobile device can adjust the location estimation using the structural information. Adjusting the location estimation can include adjusting a statistical filter that provides estimation of the location and changing a heading of the mobile device used in the dead reckoning.

Correcting in-venue location estimation using structural information

Methods, systems, and computer program product for prefetching location data based on predicted user behavior. A mobile device can request, from a user routine subsystem of the mobile device, a list of locations that a user of the mobile device routinely visits while the user carries the mobile device. The mobile device can determine a cluster of these locations that are within a specified distance between one another. The mobile device can request location data for these locations from a location server, even if the user is not at one of these locations. The location data can include a venue map and a venue location fingerprint. Upon detecting that the user entered a venue at one of these locations, the mobile device can determine a location of the user inside of the venue using the venue location fingerprint. The mobile device can then display the location on a venue map.

Venue data prefetch

Methods, systems, and computer program product for generating location fingerprint data for a venue are described. A sampling device surveying a venue can move inside the venue. While the sampling device moves, the sampling device can record environmental readings including, for example, strengths of signals from multiple radio signal sources. The sampling device can take the recording at fixed or various time intervals. Meanwhile, the sampling device can determine, based on a starting point and motion sensor readings, an estimated location of the mobile device for each time the sampling device takes the recordings. The sampling device can display a motion path of the estimated locations and a user interface item for receiving a user input for correcting the estimated locations. The sample device can tag the recorded environmental readings with the corrected locations, and submit the tagged readings to a server for determining a location fingerprint for the venue.

Indoor location survey assisted by a motion path on a venue map

A method of generating a user?s location using a mobile device. The method comprises, determining a signal snapshot on the mobile device, the signal snapshot describing characteristics of unregulated radio frequency (RF) transmissions detectable by the mobile device. Generating the user?s location on the mobile device using the signal snapshot and at least one additional input from the mobile device. The generating and determining are iteratively repeated. The unregulated RF transmission can comprise WiFi signals.

Method and apparatus for mobile location determination

Methods, systems, and computer program product for deduplicating location fingerprint data for a venue are described. A system including a location server, or a mobile device, or both, can deduplicate the location fingerprint data. Deduplicating the location fingerprint data can include identifying correlated signal sources the signals of which are mutually dependent such that measurements of one signal source can be used to predict measurements of another. The system can determine a mutual information entropy value for each pair of signal sources, and identify the correlated signal sources based on high mutual information entropy value. The system can adjust weights of the correlated signal sources in location determination.

Deduplicating location fingerprint data

Methods, systems, and computer program product for location determination using dual statistical filters are described. A mobile device can determine a location of the mobile device using a particle filter and a Kalman filter. The particle filter can filter candidate locations of the mobile device using measurements of environment variables in the venue. The Kalman filter can filter inputs from a sensor of the mobile device for measuring angular movement of the mobile device. The particle filter and the Kalman filter can be linked by heading of the mobile device. Output of the Kalman filter can be used to determine where to place particles, or candidate locations, in a next iteration of the particle filter. Output from the particle filter can be used to determine a center mode of the Kalman filter and to determine a bias of the sensor for measuring angular movement.

Location determination using dual statistical filters

Methods, systems, and computer program product for deduplicating location fingerprint data for a venue are described. A system including a location server, or a mobile device, or both, can deduplicate the location fingerprint data. Deduplicating the location fingerprint data can include identifying correlated signal sources the signals of which are mutually dependent such that measurements of one signal source can be used to predict measurements of another. The system can determine a mutual information entropy value for each pair of signal sources, and identify the correlated signal sources based on high mutual information entropy value. The system can adjust weights of the correlated signal sources in location determination

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Kingston, Canada

Joseph Ding-Jiu Huang