Abstract: 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.

Abstract: Certain aspects of the present disclosure provide techniques and apparatus for improved machine learning. Voice data from a first user is received. In response to determining that the voice data includes an utterance of a defined keyword, a user verification score is generated by processing the voice data using a first user verification machine learning (ML) model, and a quality of the voice data is determined. In response to determining that the user verification score and determined quality satisfy one or more defined criteria, a second user verification ML model is updated based on the voice data.

Abstract: Certain aspects of the present disclosure provide techniques and apparatus for a training and using machine learning models in multi-device network environments. An example computer-implemented method for network communications performed by a host device includes extracting a feature set from a data set associated with a client device using a client-device-specific feature extractor, wherein the feature set comprises a subset of features in a common feature space, training a task-specific model based on the extracted feature set and one or more other feature sets associated with other client devices, wherein the feature sets associated with the other client devices comprise one or more subsets of features in the common feature space, and deploying, to each respective client device of a plurality of client devices, a respective version of the task-specific model.

Abstract: Certain aspects of the present disclosure provide techniques for operating a wireless device pursuant to radio frequency (RF) exposure compliance. A method that may be performed by a wireless device includes switching sensing circuitry to a first mode in response to one or more first criteria being satisfied; switching the sensing circuitry to a second mode in response to one or more second criteria being satisfied; and transmitting a signal at a transmit power determined based at least in part on a radio frequency (RF) exposure limit, and if the sensing circuitry is operating in the second mode, on one or more measurements associated with the sensing circuitry.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Techniques for securing transactions on a mobile device are provided. An example method according to these techniques includes receiving an input of a code to authorize a transaction in a security sensitive application, authenticating the transaction responsive to the input of the code, monitoring sensor information indicative of a context change, and authorizing subsequent transactions responsive to the sensor information indicating that the context change has not occurred since receiving the input of the code.

Abstract: A user equipment (UE) controls power consumption of the UE based on mobility information and channel conditions experienced by the UE. In one instance, the UE determines its level of mobility based on a Doppler frequency spread of received communications. The UE disables a motion sensor when the level of mobility is above a first threshold. The UE then controls the communications based on the motion sensor and channel conditions experienced by the UE when the level of mobility is below the first threshold.

Abstract: 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.

Abstract: A user in an identified adoption group is periodically queried regarding use of a product. Results of the querying are received and evaluated. The evaluating includes aggregating the results by category, computing a proportion of total results for each category, and generating a first user emphasis vector based on the proportion of total results for each category. Based on the evaluating, a determination is made whether to incorporate the results of the querying into a representative result for an evaluation group.

Abstract: 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.