Apple Inc.

Benjamin A Werner

Brent Michael Ledvina

Merrick K McCracken

Robert W Brumley

Dennis Hilgenberg

Aarti Sathyanarayana

Isaac Thomas Miller

Changlin Ma

Saurabh Godha

Christina Selle

Tunc Ertan

Mark G Petovello

Merrick K Mccracken

Embodiments for a device locator application are described. In an embodiment, one or more inertial displacement measurement values may be received using the inertial sensor and received camera sensor data, a trajectory based on the one or more inertial displacement measurement values may be determined, a beacon signal from a target wireless device and determine at least one signal strength value from the beacon signal may be received, at least one proximity value to the target wireless device may be estimated based on the at least one signal strength value corresponding to at least one position along the trajectory, and an indicator of the at least one proximity value to the target wireless device may be presented along the trajectory in a user interface.

Device location finding

A mobile device may receive a plurality of timestamps, wherein the plurality of timestamps indicate sending and receiving time for ranging packets and response packets. The mobile device may calculate a responder turn-around time as a first difference between the second time and the first time. The mobile device may calculate a responding round trip time as a second difference between the second time and the third time. The mobile device may receive from the electronic device an initiator turn-around time and an initiator round trip time. The mobile device may calculate a frequency offset for the wireless protocol using the responder turn-around time, the responding round trip time, the initiator turn-around time, and the initiator round trip time. The mobile device may compare an observed frequency offset to the calculated frequency offset to determine a frequency offset difference and whether it exceeds a threshold, adjusting a ranging measurement.

Techniques for improving ranging between electronic devices

A device implementing a system for estimating device location includes at least one processor configured to receive an estimated position based on a positioning system comprising a Global Navigation Satellite System (GNSS) satellite, and receive a set of parameters associated with the estimated position. The processor is further configured to apply the set of parameters and the estimated position to a machine learning model, the machine learning model having been trained based at least on a position of a receiving device relative to the GNSS satellite. The processor is further configured to provide the estimated position and an output of the machine learning model to a Kalman filter, and provide an estimated device location based on an output of the Kalman filter.

Machine learning assisted satellite based positioning

A method for identifying a second mobile device in a vicinity of a first mobile device includes performing, by the first mobile device, determining distance information corresponding to a distance between the first mobile device and the second mobile device, determining angular information indicating an angle between a pointing direction of the first mobile device and the second mobile device, determining, based on the distance information and the angular information, that a location of the second mobile device lies within a first spatial region relative to the first mobile device, and providing a user interface that displays an icon corresponding to the second mobile device in a specified position on the user interface based on the location of the second mobile device being within the first spatial region.

Ranging measurements for spatially-aware user interface of a mobile device

A device implementing a system for estimating device location includes at least one processor configured to estimate a first position of a device based on a first set of parameters, the first set of parameters derived from first sensor data obtained on the device, the first set of parameters corresponding to device motion. The at least one processor is configured to estimate a second position of a user of the device based on a second set of parameters, the second set of parameters derived from second sensor data obtained on the device, the second set of parameters corresponding to user motion. Estimating the first and second positions is constrained by a predefined relationship between the device motion and the user motion. The at least one processor is configured to provide at least one of the first position of the device or the second position of the user.

User-specific motion model for location estimation

A method for identifying one or more other mobile devices in a vicinity of a first mobile device includes transmitting a first set of pulses to a second mobile device, receiving a second set of pulses from the second mobile device, and determining distance and angular information corresponding to transmission times of the first set of pulses and reception times of the second set of pulses received at the mobile device. The angular information indicates an angle between a pointing direction of the first mobile device and the second mobile device. A first probability that a position of the second mobile device lies within a first spatial region having a defined distance range and aligned with the pointing direction of the first mobile device is determined based on the distance information and the angular information. An importance metric for the second mobile device is determined based on the first probability.

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San Jose, CA, United States of America

Benjamin A Werner