Apple Inc.

Ailive, Inc.

Xiaoyuan Tu

Wei Yen

Ian Wright

Anil K Kandangath

Stuart Reynolds

Charles Musick, Jr

John Funge

Daniel Dobson

Alexander Singh Alvarado

William Robert Powers, Iii

Dana Wilkinson

Hung A Pham

Adam S Howell

Ronald Keryuan Huang

Christopher D Moore

Umamahesh Srinivas

Yoichiro Kawano

Curt Bererton

William Matthew Vieta

Brian Keith Cabral

Karthik Jayaraman Raghuram

Xing Tan

Nigel Duffy

Yann Jerome Julien Renard

Michael David McNally

Hengliang Zhang

Margaret H Tam

Sunny Kai Pang Chow

Sheena Sharma

Huayu Ding

Ron Musick

Harshavardhan Mylapilli

Geoffrey Louis Chi-Johnston

Vivek Garg

Justin N Wood

Patrick Steven Piemonte

Roberto Garcia

Parisa Dehleh Hossein Zadeh

Aditya Sarathy

Lukas Michael Marti

Brian Michael King

Arvindh Krishnaswamy

Gunes Dervisoglu

Leonardo A Soto Matamala

Maulik V Choksi

Libo C Meyers

Lawrence Yuan Yang

Srinivasan Nimmala

Christopher Blumenberg

Billy P Chen

Gregory B Novick

Emily Clark Schubert

Indranil S Sen

Seejo K Pylappan

Venkateswara Rao Manepalli

Glenn Donald MacGougan

Woo-Ram Lee

Christopher Tenzin Mullens

Bharath Narasimha Rao

Purvi J Desai

Richard B Warren

Vijay Kumar Ramamurthi

Andrew M Wadycki

Digvijay Arjunrao Jadhav

Andre M Boule

Kyle S Macomber

Jonathan M Beard

Sunny K Chow

Jonathan C King

Anh N Phan

Hui Lin

Xiao Xiao

Suresh B Malakar

Halil Ibrahim Basturk

John D Blackwell

Martin M Menzel

Charles Musick

Óscar Morales Vivó

Tusi Chowdhury

Seyyedeh Mahsa Mirzargar

Keyu Chen

Evan G Kriminger

Johahn Leung

Simon Woollard

Mehmet Akgul

Parisa Deleh Hossein Zadeh

Bor-rong Chen

Embodiments are disclosed for user posture transition detection and classification. In an embodiment, a method comprises: obtaining, using one or more processors, motion data from a headset worn by a user; determining, using the one or more processors, one or more windows of motion data that indicate biomechanics of one or more phases of a user's postural transition; and classifying, using the one or more processors, as the user's postural transition based on the one or more windows of data.

User posture transition detection and classification

Embodiments are disclosed for adaptive audio centering for head tracking in spatial audio applications. In an embodiment, a method comprises: obtaining first motion data from an auxiliary device communicatively coupled to a source device, the source device configured to provide spatial audio content and the auxiliary device configured to playback the spatial audio content; obtaining second motion data from one or more motion sensors of the source device; determining whether the source device and auxiliary device are in a period of mutual quiescence based on the first and second motion data; in accordance with determining that the source device and the auxiliary device are in a period of mutual quiescence, re-centering the spatial audio in a three-dimensional virtual auditory space; and rendering the 3D virtual auditory space for playback on the auxiliary device.

Adaptive audio centering for head tracking in spatial audio applications

In an example method, a mobile device obtains a signal indicating an acceleration measured by a sensor over a time period. The mobile device determines an impact experienced by the user based on the signal. The mobile device also determines, based on the signal, one or more first motion characteristics of the user during a time prior to the impact, and one or more second motion characteristics of the user during a time after the impact. The mobile device determines that the user has fallen based on the impact, the one or more first motion characteristics of the user, and the one or more second motion characteristics of the user, and in response, generates a notification indicating that the user has fallen.

Detecting falls using a mobile device

Embodiments are disclosed for head tracking state detection based on correlated motion of a source device and a headset communicatively coupled to the source device. In an embodiment, a method comprises: obtaining, using one or more processors of a source device, source device motion data from a source device and headset motion data from a headset; determining, using the one or more processors, correlation measures using the source device motion data and the headset motion data; updating, using the one or more processors, a motion tracking state based on the determined correlation measures; and initiating head pose tracking in accordance with the updated motion tracking state.

Head tracking correlated motion detection for spatial audio applications

Systems, methods, devices and computer-readable storage mediums are disclosed for correcting a compass view using map data. In an implementation, a method comprises: receiving, by one or more sensors of a mobile device, sensor data; determining, by a processor of the mobile device, compass offset data for a compass view based on the sensor data and map data; determining, by the processor, a corrected compass view based on the compass offset data; and presenting, by the processor, the corrected compass view.

Correcting compass view using map data

An electronic device displays a messaging interface that allows a participant in a message conversation to capture, send, and/or play media content. The media content includes images, video, and/or audio. The media content is captured, sent, and/or played based on the electronic device detecting one or more conditions.

Message user interfaces for capture and transmittal of media and location content

Embodiments are disclosed for user posture transition detection and classification using a linked biomechanical model. In an embodiment, a method comprises: obtaining motion data from a headset worn by a user; selecting features of a linked biomechanical model based on a current posture state; determining at least one probability that a posture transition occurred based on an output of a classifier, where the output of the classifier is based on the selected features and the motion data; determining a posture transition based on the at least one probability; and performing at least one action based on detection of the posture transition.

Posture transition detection and classification using linked biomechanical model

Embodiments are disclosed for user posture change detection for triggering re-centering of spatial audio. In an embodiment, a method comprises: obtaining source device motion data from a source device and headset motion data from a headset worn by a user; estimating a gravity vector from one of the source device or headset motion data; splitting the source device and headset motion data into vertical and horizontal planes, the vertical plane in the direction of the estimated gravity vector and the horizontal plane perpendicular to the estimated gravity vector; calculating similarity measures based on the source device motion data and headset motion data in the vertical and horizontal planes over a time window; detecting a posture change event based on the calculated similarity measures; and resetting a head tracker error after the detected user posture change event.

User posture change detection for head pose tracking in spatial audio applications

In an embodiment, a method comprises: estimating a first gravity direction in a source device reference frame for a source device; estimating a second gravity direction in a headset reference frame for a headset; estimating a rotation transform from the headset frame into a face reference frame using the first and second estimated gravity directions, a rotation transform from a camera reference frame to the source device reference frame, and a rotation transform from the face reference frame to the camera reference frame; estimating a relative position and attitude using source device motion data, headset motion data and the rotation transform from the headset frame to the face reference frame; using the relative position and attitude to estimate a head pose; and using the estimated head pose to render spatial audio for playback on the headset.

Head to headset rotation transform estimation for head pose tracking in spatial audio applications

Embodiments are disclosed for head motion prediction for spatial audio applications. In an embodiment, a method comprises: obtaining motion data from a source device and a headset; obtaining transmission delays from a wireless stack of the source; estimating relative motion from the relative source device and headset motion data; calculating a first derivative of the relative motion data; forward predicting the estimated relative motion over the time delays using the first derivative and second derivative of relative motion; determining, using a head tracker, a head pose of the user based on the forward predicted relative motion data; and rendering, using the head pose, spatial audio for playback on the headset.

Head motion prediction for spatial audio applications

During an initialization of a head pose tracker for a spatial audio system, a spatial audio ambience bed is rotated about a boresight vector to align the boresight vector with a center channel of the ambience bed. The boresight is computed using source device motion data and headset motion data. The ambience bed includes the center channel and one or more other channels. An ambience bed reference frame is aligned with a horizontal plane of a headset reference frame, such that the ambience bed is horizontally level with a user's ears. A first estimated gravity direction is fixed (made constant) in the ambience bed reference frame. During head pose tracking, the ambience bed reference frame is rolled about the boresight vector to align a second estimated gravity direction in the headset reference frame with the first estimated gravity direction fixed in the ambience bed reference frame.

Inertially stable virtual auditory space for spatial audio applications

Embodiments are disclosed for disabling/re-enabling head tracking for spatial audio applications. In an embodiment, a method comprises: obtaining, using one or more processors of an auxiliary device worn by a user, motion data; tracking, using the one or more processors, the user's head based at least in part on the motion data; determining, using the one or more processors, whether or not the user is walking based at least in part on the motion data; in accordance with determining that the user is walking, determining if a source device configured to deliver spatial audio to the auxiliary device is static for a specified period of time; and in accordance with determining that the user is walking and the source device is static for the specified period of time, disabling the head tracking.

Disabling/re-enabling head tracking for distracted user of spatial audio application

An electronic device includes a touch-sensitive surface, a display, and a camera sensor. The device displays a message region for displaying a message conversation and receives a request to add media to the message conversation. Responsive to receiving the request, the device displays a media selection interface concurrently with at least a portion of the message conversation. The media selection interface includes a plurality of affordances for selecting media for addition to the message conversation, the plurality of affordances includes a live preview affordance, at least a subset of the plurality of affordances includes thumbnail representations of media available for adding to the message conversation, and the live preview affordance is associated with a live camera preview. Responsive to detecting selection of the live preview affordance, the device captures a new image based on the live camera preview and selects the new image for addition to the message conversation.

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.

Simplified audio navigation instructions

Growing community of inventors

Sunnyvale, CA, United States of America

Xiaoyuan Tu