Abstract: Systems, methods, and non-transitory media are provided for a vehicle and mobile device interface for vehicle occupant assistance. An example method can include determining, based on one or more images of an interior portion of a vehicle, a pose of a mobile device relative to a coordinate system of the vehicle; determine a state of an occupant of the vehicle; and send, to the vehicle, data indicating the state of the occupant and the pose of the mobile device relative to the coordinate system of the vehicle.

Abstract: Various embodiments include systems and methods for improving the user experience with LXMs. A computing device may be configured to receive a user prompt, observe user responses to an output received from a LXM in response to a prompt that is at least partially based on the user's prompt, and take an action to improve the user's experience with the LXM based on the observed user response.

Abstract: Various embodiments include systems and methods for generating a prompt for a generative artificial intelligence (AI) models. A processing system including at least one processor may be configured to recognize a user of the computing device, obtain user context information from a source of physical context information in the computing device, receive a user prompt for the large generative AI model (LXM), select a user profile from among a plurality of user profiles based on the user, the user context information and the user prompt, generate an enhanced prompt based on the user prompt and information included in the selected user profile, and submit the enhanced prompt to the LXM.

Abstract: Systems, methods, and non-transitory media are provided for a vehicle and mobile device interface for vehicle occupant assistance. An example method can include determining, based on one or more images of an interior portion of a vehicle, a pose of a mobile device relative to a coordinate system of the vehicle; determine a state of an occupant of the vehicle; and send, to the vehicle, data indicating the state of the occupant and the pose of the mobile device relative to the coordinate system of the vehicle.

Abstract: Systems and techniques are provided for conditioning virtual representatives. For example, a method can include obtaining, by a conditioning engine, a baseline model for a virtual representative; obtaining, by the conditioning engine, one or more conditioning inputs configured to condition an action in one or more multi-user experiences of the virtual representative; generating, based on the baseline model and the one or more conditioning inputs configured to condition an action in one or more multi-user experiences of the virtual representative, a conditioned model for the virtual representative; and outputting the conditioned model for the virtual representative.

Abstract: Systems and techniques are provided for coordinating multi-user experiences. For example, a process can include obtaining a plurality of settings associated with a plurality of multi-user experience participants. The plurality of settings includes one or more arbitrated settings and one or more non-arbitrated settings. The process can include arbitrating, by a settings arbitration engine, the one or more arbitrated settings to generate one or more adjusted settings for each arbitrated setting. The process can include generating, by an experience adaptation engine, an adapted multi-user experience, wherein the adapted multi-user experience is configured to enforce the one or more adjusted settings for each arbitrated setting.

Abstract: Systems and techniques are provided for adapting digital content. For example, a process can include obtaining a digital content comprising a default configuration for outputting the digital content to a device; outputting, by the device, the digital content based on the default configuration for outputting the digital content. The process can include obtaining, from a monitoring engine, content interaction information associated with outputting, by the device, the digital content based on the default configuration for outputting the digital content. The monitoring engine is configured to monitor one or more interactions between one or more users of the device and the digital content. The process can include generating, based on the content interaction information, a content adaptation for the digital content. The process can include outputting, by the device, the content adaptation for the digital content.

Abstract: receiving, by a control system, client device information from one or more client devices and transmitting, by the control system, sensor data request information to the one or more client devices. The client device information may indicate at least client device type information and client device sensor information. The sensor data request information may indicate one or more requested sensor data types and one or more requested sensor data transmission parameters. The one or more requested sensor data transmission parameters may indicate one or more threshold parameters for requested sensor data transmission, one or more variance parameters for requested sensor data transmission, or a combination thereof. Some examples may involve receiving, by the control system, sensor data from the one or more client devices based, at least in part, on the one or more requested sensor data transmission parameters.

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: Various embodiments include methods and implementing computing devices, for distributing compute operation across connected wired or wireless computing devices. Various embodiments may include establishing an ad hoc communication connection with a second computing device, in which the ad hoc communication connection is part of a network of a plurality of wired or wireless computing devices communicatively connected via a plurality of ad hoc communication connections, receiving context information of the second wired or computing device via the ad hoc communication connection, selecting at least one sensor wired or wireless computing device from the plurality of wired or wireless computing devices, selecting at least one execution wired or wireless computing device from the plurality of wired or wireless computing devices, assigning a data gathering part of a work item to the sensor computing device, and assigning an execution part of the work item to the execution wired or wireless computing device.

Abstract: Systems, methods, and non-transitory media are provided for a vehicle and mobile device interface for vehicle occupant assistance. An example method can include determining, based on one or more images of an interior portion of a vehicle, a pose of a mobile device relative to a coordinate system of the vehicle; determine a state of an occupant of the vehicle; and send, to the vehicle, data indicating the state of the occupant and the pose of the mobile device relative to the coordinate system of the vehicle.

Abstract: Systems, methods, and non-transitory media are provided for a vehicle and mobile device interface for vehicle occupant assistance. An example method can include determining, based on one or more images of an interior portion of a vehicle, a position of a mobile device relative to a coordinate system of the vehicle; receiving, from the vehicle, data associated with one or more sensors of the vehicle; and displaying, using a display device of the mobile device, virtual content based on the data associated with the one or more sensors and the position of the mobile device relative to the coordinate system of the vehicle.

Abstract: In general, techniques are described for ensuring occupant awareness in vehicles. A device comprising a processor may be configured to perform the techniques. The processor may be configured to determine an event which will change an operating characteristic of the vehicle, and determine an extent of time remaining until an estimated occurrence of the expected event. The processor may also be configured to determine a process by which to inform the occupant of an operating context of the vehicle based on the determined extent of time remaining until the estimated occurrence the expected event, and perform the process by which to inform the occupant of the operating context of the vehicle. The device may also include a memory configured to store the process.

Abstract: In general, techniques are described for ensuring occupant awareness in vehicles. A device comprising a processor may be configured to perform the techniques. The processor may be configured to determine an event which will change an operating characteristic of the vehicle, and determine an extent of time remaining until an estimated occurrence of the expected event. The processor may also be configured to determine a process by which to inform the occupant of an operating context of the vehicle based on the determined extent of time remaining until the estimated occurrence the expected event, and perform the process by which to inform the occupant of the operating context of the vehicle. The device may also include a memory configured to store the process.

Abstract: System and method for providing additional channels to an existing communications system. A preferred embodiment comprises a coprocessor (such as coprocessor 215) coupled to a modem (such as modem 205), wherein transmissions from the modem are routed to the coprocessor. The coprocessor then can insert transmissions that use the additional channels into the transmission from the modem. Timing consistency can be ensured by the coprocessor using scrambling and channelization codes that are provided by the modem. The use of the coprocessor can allow the use of an existing design for the modem rather than creating a new design when a revision to a technical standard is released, saving time and money for equipment manufacturers.

Abstract: A method is provided to decode data encoded by any block code in a manner that substantially improves the error correction capability of the block codes, and that is independent of the encoder. The structure associated with the method desirably allows the testing of those hypotheses that are known to exist, such that one can use the a priori knowledge of the possible set of hypotheses to only search from among them. The method of decoding data is both advantageous and desirable since knowing the subset of the code word space that is being utilized in essence allows the distance between the code words to be increased yielding significant decoding benefits.

Abstract: System and method for improving the detection performance of a wirelessly transmitted signal. A preferred embodiment comprises specifying a desired response for missed channel detection and false alarm probabilities for a plurality of signal qualities and determining if a channel detection threshold is based on missed channel detection or false alarm probabilities for the plurality of signal qualities. A signal quality estimate of a channel can be inferred from a signal quality measurement of a second channel, wherein the channel and the second channel are sourced by a single transmitter. The channel detection threshold can be adjusted based upon the previously determined required response and the signal quality estimate of the channel.

Abstract: System and method for providing additional channels to an existing communications system. A preferred embodiment comprises a coprocessor (such as coprocessor 215) coupled to a modem (such as modem 205), wherein transmissions from the modem are routed to the coprocessor. The coprocessor then can insert transmissions that use the additional channels into the transmission from the modem. Timing consistency can be ensured by the coprocessor using scrambling and channelization codes that are provided by the modem. The use of the coprocessor can allow the use of an existing design for the modem rather than creating a new design when a revision to a technical standard is released, saving time and money for equipment manufacturers.

Abstract: System and method for improving the detection performance of a wirelessly transmitted signal. A preferred embodiment comprises specifying a desired response for missed channel detection and false alarm probabilities for a plurality of signal qualities and determining if a channel detection threshold is based on missed channel detection or false alarm probabilities for the plurality of signal qualities. A signal quality estimate of a channel can be inferred from a signal quality measurement of a second channel, wherein the channel and the second channel are sourced by a single transmitter. The channel detection threshold can be adjusted based upon the previously determined required response and the signal quality estimate of the channel.