Qualcomm Incorporated

Rashmi Kulkarni

Diyan Teng

Mehul Soman

Junsheng Han

Justin McGloin

Leonid Sheynblat

Martin Renschler

Justin Phelps Black

Jeffrey Shabel

Sanjiv Nanda

Danlu Zhang

Edward Harrison Teague

Moinul H Khan

Bohuslav Rychlik

Radu Pitigoi-Aron

Nhon Toai Quach

Leonard Henry Grokop

Wesley James Holland

Victor Kulik

Brian Lee Momeyer

Carlos Manuel Puig

Shankar Sadasivam

Mehrad Tavakoli

Disha Ahuja

Maurice Franklin Ribble

Simon Peter William Booth

Damir Didjusto

Philip T Mueller, Jr

Huang Huang

Songhe Cai

Jiaying Pan

Ling Feng Huang

Ricky Wai Kit Yuen

Satish Anand

Zhurang Zhao

Chih-Chi Cheng

Michael Mager

Nisarg Trivedi

Carl Victor Streeter

Raehan Ahmed Syed

Paul Holdaway

Ajinkya Shyam Ambre

Vinayak Muralidharan

Mahdi Shaghaghi

Todd Lemoine

Jeffrey David Shabel

Maurice Ribble

Systems and techniques are provided for calibrating tracking devices. For example, a process can include generating, at a primary tracking device of a tracking device network, pose information of the primary tracking device; obtaining relative pose information of a secondary tracking device of the tracking device network, wherein the relative pose information of the secondary tracking device comprises a relative position and orientation between the primary tracking device and the secondary tracking device; obtaining absolute pose information of the secondary tracking device using the pose information of the primary tracking device and the relative pose information of the secondary tracking device; and transmitting, at the primary tracking device, the absolute pose information to the secondary tracking device for performing a calibration action.

Calibration of interconnected tracking devices

Attention evaluation by an extended reality system, the system determining one or more regions of interest (ROI) for an image displayed to a user. The system may also receive eye tracking information indicating an area of the image that the user is looking at. The system may further generate focus statistics based on the area of the image at which the user is looking at and the one or more ROI; and output the generated focus statistics.

User attention determination for extended reality

A method for energy-efficient state change detection and classification of streaming sequential data includes receiving via a first prediction model, sequential data from a sensor. The first prediction model determines a change in an activity state based on the sequential data. An indication that the activity state has changed is transmitted to a second prediction. The second prediction model determines an updated activity state based on the sequential data. The updated activity state is sent to the first prediction model, after which the second prediction enters an inactive state.

Persistent two-stage activity recognition

System and method for determining positional and activity information of a mobile device in synchronization with the wake-up period of the mobile device to perform antenna beam management and adjusting the wake-up period based on the positional and activity information of the mobile device. A mobile device comprises: a memory; at least one sensor for detecting data: a processor communicatively coupled to the memory, the processor is configured to: synchronize the at least one sensor with a wake-up period of the mobile device; receive the data detected by the at least one sensor; determine positional information based on the received data; determine activity information based on the received data; estimate a forward position of the mobile device based on the positional information and the activity information; and perform a management of antenna beams of the mobile device based on the positional information, the activity information and the forward position.

Sensor aided beam management

Cardiovascular or respiratory data of a subject is measured using a multi-sensor system. The multi-sensor system includes a mm-wave FMCW radar sensor, an IMU sensor, and one or more proximity sensors. The mm-wave FMCW radar sensor may be selected and its view angle adjusted based on positioning data regarding the subject obtained from the one or more proximity sensors. Each of the mm-wave FMCW radar sensor and the IMU sensor may acquire cardiovascular or respiratory measurements of the subject, and the measurements may be fused for improved accuracy and performance.

Multi-sensor system for cardiovascular and respiratory tracking

Systems and techniques are described herein. For example, a process can include obtaining first sensor measurement data associated with a and second sensor measurement from one or more sensors. In some cases, the first measurement data can be associated with a first time and the second sensor measurement data can be associated with a second time occurring after the first time. In some aspects, the process includes determining that the first sensor measurement data and the second sensor measurement data satisfy at least one batching condition. In some examples, the process includes, based on determining that the first sensor measurement data and the second sensor measurement data satisfy the at least one batching condition, generating a sensor measurement data batch including the first sensor measurement data, the second sensor measurement data, and at least one target sensor measurement data. Ins examples the process includes outputting the sensor measurement data batch.

Sensor calibration

A device includes a memory and one or more processors. The memory is configured to store instructions. The one or more processors are configured to execute the instructions to obtain electrical activity data corresponding to electrical signals from one or more electrical sources within a user's head. The one or more processors are also configured to execute the instructions to render, based on the electrical activity data, audio data to adjust a location of a sound source in a sound field during playback of the audio data.

Audio adjustment based on user electrical signals

A machine learning model is trained for user activity detection and context detection on a mobile device. The machine learning model is configured to learn a statistical relationship between an always-on sensing modality of the mobile device and actual user context. Rather than user annotations, the machine learning model is enhanced and personalized for the always-on sensing modality by automated annotations obtained from non-always-on sensing modalities. The non-always-on sensing modality opportunistically provides an imperfect label of user context, where the imperfect label has a known associated probability of error.

Weakly supervised learning for improving multimodal sensing platform

In some aspects, a user equipment (UE) determines, using an inertial measurement unit, an orientation of the UE and determines, using ambient light sensors, an ambient light condition of the UE. The UE determines, using a machine learning module and based on the orientation and the ambient light condition, a position of the UE. If the position comprises an on-body position, the UE uses the machine learning module and touch data received by a touchscreen of the UE to determine whether the position comprises an in-hand position. If the position comprises the in-hand position, the UE determines, using the machine learning module and based on the orientation and the touch data, a grip mode. If the position comprises an off-body position, the UE determines, using the machine learning module and at least one of the inertial measurement unit or the ambient light sensors, a user presence or a user absence.

Passively determining a position of a user equipment (UE)

Various aspects of the present disclosure generally relate to object detection. In some aspects, a device may include a housing; an input device adjoined to the housing, the input device configured to receive an input associated with a press of a key of a plurality of keys; one or more transmitters disposed in the housing, the one or more transmitters configured to transmit one or more signals toward the plurality of keys; one or more receivers disposed in the housing, the one or more receivers configured to receive one or more return signals corresponding to the one or more signals; and a processor configured to determine a location of the key based at least in part on the one or more return signals.

Key press detection

In some aspects, the present disclosure provides a method for managing a command queue in a universal flash storage (UFS) host device. The method includes determining to power on a first subsystem of a system-on-a-chip (SoC), wherein the determination to power on the first subsystem is made by a second subsystem of the SoC based on detection of user identity data contained in a first image frame during an initial biometric detection process. In certain aspects, the second subsystem is configured to operate independent of the first subsystem and control power to the first subsystem. In certain aspects, the second subsystem includes a second optical sensor, a set of ambient sensors, and a second processor configured to detect, via a set of ambient sensors, an event comprising one or more of an environmental event outside of the device or a motion event of the device.

Multi-stage biometric authentication

Disclosed is a method and apparatus for power-efficiently processing sensor data. In one embodiment, the operations implemented include: configuring a sensor fusion engine and a peripheral controller with a general purpose processor; placing the general purpose processor into a low-power sleep mode; reading data from a sensor and storing the data into a companion memory with the peripheral controller; processing the data in the companion memory with the sensor fusion engine; and awaking the general purpose processor from the low-power sleep mode.

Algorithm engine for ultra low-power processing of sensor data

Disclosed is an apparatus and method for power efficient processor scheduling of features. In one embodiment, features may be scheduled for sequential computing, and each scheduled feature may receive a sensor data sample as input. In one embodiment, scheduling may be based at least in part on each respective feature's estimated power usage. In one embodiment, a first feature in the sequential schedule of features may be computed and before computing a second feature in the sequential schedule of features, a termination condition may be evaluated.

Sequential feature computation for power efficient classification

Systems, methods, and computer programs are disclosed for reducing motion-to-photon latency and memory bandwidth in a virtual reality display system. An exemplary method involves receiving sensor data from one or more sensors tracking translational and rotational motion of a user for a virtual reality application. An updated position of the user is computed based on the received sensor data. The speed and acceleration of the user movement may be computed based on the sensor data. The updated position, the speed, and the acceleration may be provided to a warp engine configured to update a rendered image before sending to a virtual reality display based on one or more of the updated position, the speed, and the acceleration.

Systems and methods for reducing motion-to-photon latency and memory bandwidth in a virtual reality system

Aspects of the invention are related to a method for synchronizing a first sensor clock of a first sensor. The exemplary method comprises: correcting the first sensor clock for a first time, transferring data from the first sensor, and correcting the first sensor clock for a second time, wherein a time interval between two corrections of the first sensor clock is selected such that the first sensor clock is sufficiently aligned with a processor clock of a processor over the time interval.

System and methods of reducing energy consumption by synchronizing sensors

Growing community of inventors

Redwood City, CA, United States of America

Rashmi Kulkarni