Qualcomm Incorporated

Justin McGloin

Rashmi Kulkarni

Radu Pitigoi-Aron

Mehul Soman

Diyan Teng

Nikhil Jain

Joel Linsky

Ramin Samadani

Mark Leo Moeglein

Zoltan F Biacs

James Robert Chapman

Sunwoo Park

HeeGyum Kim

Nisarg Trivedi

Won-Seon Choi

Innovative techniques and apparatuses to control metaverse presentation are disclosed. The proposed techniques and apparatuses allow prevention of overindulgence to metaverse presentation based on user biometrics (e.g., oxygen saturation, heart rate, body temperature, blood pressure, etc.). If the biometrics indicate that the user's health is compromised due to the metaverse presentation, the metaverse presentation (visual, audio, haptic outputs) may be altered to reduce the user's overindulgence.

Protection of user's health from overindulgence in metaverse

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

Described are techniques for sharing data among a group of mobile devices that are registered to the same user. In some instances, the shared data is sensor data captured by a mobile device or media content being processed by the mobile device. The mobile devices in the group are configured to wirelessly communicate with each other and to share status information. Using the status information, the mobile devices can keep each other updated as to the operational status of each individual mobile device and, in some aspects, the status of a mobile device with respect to the user. The sensor data or media content can be forwarded to a different mobile device in response to a determination made based on the status information. For instance, forwarding can be based on a rule that specifies one or more conditions relating to the status of a mobile device and/or user status.

Data sharing among user devices

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)

Disclosed is an apparatus and method for automatically configuring a mobile device for collecting and inferring heart rate data of a user. The method may include capturing heart rate data for a user with a heart rate sensor that is coupled with a mobile device. The method may also include monitoring a activity state of the user from activity data captured by the mobile device, and detecting a constant activity state of the user. The method may also include inferring heart rate data for the user from the captured heart rate data during a period in which the user remains in the constant activity state. The method may also include providing the inferred heart rate data, as captured heart rate data, to a heart rate calculator during the period in which the user remains in the constant activity state.

Methods and systems for reducing energy consumption of a heart rate monitor

Embodiments of the invention provide for a sensor system with enhanced low-power features. Embodiments can include transmission of sensor data from a transmitter unit to a receiver unit. The sensor data can flag the sensor data with a particular header ID, enabling the receiver unit to route the sensor data to a low-power processing unit within the receiver unit without using the receiver unit's higher-power application processer. Embodiments can also utilize a proprietary encryption engine to provide a supplementary encryption layer to any encryption utilized in the wireless protocol. The transmitter unit can also compress and batch the sensor data for sending, to further increase power savings.

Wireless low-energy secure data transfer

A system and method of preprocessing reference data in a reference assisted location technology are disclosed. A plurality of reference stations each provides reference data over a network to a preprocessing. The preprocessor analyzes the data from the multiple reference stations and eliminates multiples sets of redundant data. In addition, the preprocessor can perform quality assurance checks on the data as well as the status of the reference stations themselves. Non-redundant data may also be preprocessed to perform differential GPS calculations. The preprocessed data is transmitted to one or more position servers for additional processing and position determination.

Growing community of inventors

Los Altos, CA, United States of America

Justin McGloin