Abstract: Methods, systems, apparatus, including computer programs encoded on a computer storage medium, for spatialized audio feedback from automated assistants. In one aspect, the method includes actions of determining that a user input has been received at a client device, identifying, based on sensor data, one or more points of interest of an environment in which the client device is located and an orientation of a user of the client device relative to the one or more points of interest, identifying, based on processing the user input and the sensor data, a natural language response providing information relevant corresponds to a particular point of interest, determining, based on the orientation of the user of the client device relative to the particular point of interest, one or more spatial audio parameters to be used to provision the natural language response to the user, and causing the natural language response to be audibly rendered at the client device using one or more of the spatial audio parameters.

Abstract: A system for controlling a head mounted device (HMD). The system includes a processor of a controller of the HMD connected to an Internal Measurement Unit (IMU) and a memory on which are stored machine-readable instructions that when executed by the processor, cause the processor to: receive Internal Measurement Unit (IMU) data generated in response to vibrations produced by voiced nasal consonant vocalizations produced by an HMD user, analyze the IMU data to determine whether the IMU data corresponds to an HMD control command, and responsive to a determination that the IMU data corresponds to the HMD control command, execute the HMD control command.

Abstract: Disclosed implementations for generating personalized audio. Sensor data corresponding with at least one physical characteristic of a user is received. A three-dimensional mesh of the user is updated based on the sensor data. An impulse response for the user is determined based on the three-dimensional mesh. An audio stream is generated based on the impulse response.

Abstract: To determine the location of a user, a computing device determines a current location of the user and a destination location. The computing device then determines a plurality of paths for traveling from the current location to the destination location. As the user travels to the destination location, the computing device estimates a plurality of waypoints traversed by the user. The computing device compares the estimated plurality of waypoints to each of the plurality of paths to identify a path of the plurality of paths in which the user travels to the destination location. Then the computing device adjusts the estimated plurality of waypoints according to the identified path to determine precise waypoints for the user.

Abstract: A method for facilitating a Parkinson's Disease (“PD”) assessment of a patient includes capturing first video of a patient performing first test movements while holding the mobile device; capturing second video of the patient performing second test movements while maintaining the mobile device on their person; capturing third video of the patient performing third test movements including standing and walking; capturing one or more IMU readings using an IMU of the mobile device; processing the first video, the second video, and the third video according to (i) a hand landmark model to generate one or more hand biomarkers, (ii) a face landmark model to generate one or more face biomarkers, and (iii) a body landmark model to generate one or more body biomarkers; and determining an assessment score based on a standardized PD assessment by processing the biomarkers.

Abstract: An example method is provided. Unverified interaction data descriptive of interactions of a user with the computing device can be obtained. Feature values that embed characteristics of the unverified interaction data can be generated using a machine-learned embedding network of a machine-learned verification pipeline. A user account associated with the unverified interaction data can be determined using a verification model of the machine-learned verification pipeline.

Abstract: This document describes systems and techniques for presenting intermediate media to a user that has presented a media request. In aspects, a media request for requested media is received from an input device. The media request is provided to a media service to serve the requested media to a requested device. The media request is processed to identify attributes of the media request indicative of a subject matter of the requested media. Based on the identified attributes of the media request, intermediate media is accessed including one or more images related to the identified attributes. The intermediate media is delivered to the requested device. Accordingly, the intermediate media provides content to engage the user while the user waits for the requested media to be delivered.

Abstract: A method can include determining, by a correlation device, that a correlation event has occurred based on at least a first device and a second device having corresponding directional profiles, and responsive to occurrence of the correlation event, causing the first device to transfer a state of the first device by sending a state information message to the second device.

Abstract: Methods and systems for crowd source-based time marking of media items at a platform are provided herein. A media item is provided to first client devices associated with first users of a platform. An indication is received from each of the first client devices of an interesting content segment of the media item as selected by a first user of the platform. At least one content segment of the media item to be associated with a bookmark for a timeline of the media item is determined. The at least one content segment is determined in view of the indication received from each of the first client devices. The media item and the indication of the bookmark is provided to a second client device for presentation to a second user of the platform.

Abstract: According an embodiment, a computing device can: identify, in a chat view associated with a video chat session, a first authorized participant and a second authorized participant of the video chat session; render, in the chat view, first visual data indicative of the first authorized participant and second visual data indicative of the second authorized participant based at least in part on identification of the first authorized participant and the second authorized participant, respectively; define, in the chat view, a chat zone indicative of a reference location of the first authorized participant; determine that the first authorized participant moved outside the chat zone; and/or conceal, in the chat view, the first visual data indicative of the first authorized participant based at least in part on determination that the first authorized participant moved outside the chat zone.

Abstract: At least one sensor signal may be received from at least one motion sensor of a head-mounted device (HMD), the HMD being associated with a user interface (UI) displaying a UI selection element. A first value of the at least one sensor signal corresponding to a first position of the HMD and a second value of the at least one sensor signal corresponding to a second position of the HMD may be determined, and an initialization point within the UI may also be determined. Then, a relative displacement of the UI selection element may be determined, based on the initialization point, the first value, and the second value, and the UI selection element may be moved within the UI, based on the relative displacement.

Abstract: Systems and methods for multi-view stereo reconstruction using ultra wideband (UWB) communication are provided. Example techniques may include receiving a first two-dimensional image of a three-dimensional object captured by a first device, receiving a second two-dimensional image of the three-dimensional object captured by a second device, receiving an indication of timing and directionality associated with UWB signals exchanged between the first device and the second device, determining relative positions and relative poses of the first device and the second device with respect to one another based on the indication of the timing and directionality associated with the UWB signals exchanged between the first device and the second device, and generating a three-dimensional image of the three-dimensional object based on the first two-dimensional image, the second two-dimensional image, and the relative positions and relative poses of the first device and the second device with respect to one another.

Abstract: Example embodiments of the present disclosure provide for an example method including obtaining location data associated with a first and second computing device. Example method can include determining an on-user device status for the each of the first and second computing devices. Example method can include obtaining inertial measurement unit (IMU) sensor data from each of the first and second computing devices. Example method can include inputting the IMU sensor data from each of the first and second computing devices into a machine learned model. Example method can include obtaining, from the machine learned model, output data indicative of a predicted location. Example method can include comparing the output data indicative of the predicted location to data indicative of a location of a target subzone. Example method can include transmitting data which instructs a user interface of the first computing device to provide a content item for display.

Abstract: To determine a location of a user, a computing device transmits an audio signal via a speaker and receives a reflected audio signal via a microphone. The computing device obtains sensor data from at least one of: one or more positioning sensors, one or more accelerometers, one or more gyroscopes, or one or more inertial measurement units, and determines a location of a user based on (i) a round trip time of the audio signal and the reflected audio signal, and (ii) the sensor data.

Abstract: A head-mounted display device may initiate display of a user interface on a display of the head-mounted display device. A head-mounted display device may compute an angular distance between a previous head orientation and a current head orientation based on head movement data. A head-mounted display device may apply a gain value to the angular distance to move a position indicator to an updated position within a boundary of the user interface, the user interface configured to remain fixed to a location in the display regardless of head movements.

Abstract: Smart glasses including a first audio device, a second audio device, a frame including a first portion, a second portion, and a third portion, the second portion and the third portion are moveable in relation to the first portion, the second portion including the first audio device and the third portion including the second audio device, and a processor configured to cause the first audio device to generate a signal, receive the signal via the second audio device, estimate a distance based on the received signal, and determine a configuration of the frame.

Abstract: Methods, systems, and apparatus, for sensing a touch on a display panel including an array of pixels. A method includes controlling first pixels to operate in an illumination state; controlling second pixels to repeatedly switch between operating in the illumination state and operating in a sensing state; generating sensing signals indicative of levels of light detected by the second pixels; detecting a touch input to the display panel based on the generated sensing signals; and in response to detecting the touch input to the display panel, changing at least one of (i) a frequency at which the second pixels switch between operating in the illumination state and in the sensing state, (ii) a duty cycle for operating the second pixels in the sensing state, or (iii) which of the pixels in the array of pixels are controlled to switch between operating in the illumination state and in the sensing state.

Abstract: Methods, systems, and media for identifying a plurality of sets of coordinates for a plurality of devices are provided. In some embodiments, the method comprises: identifying each device in a plurality of devices associated with a user account; instructing the plurality of devices to perform an audio sequence; receiving a plurality of transit times from the plurality of devices; determining a plurality of distances based on the plurality of transit times; determining a plurality of sets of coordinates based on the plurality of distances; associating to each of the plurality of devices a corresponding unique one of the plurality of sets of coordinates; and causing at least one of the plurality of devices to play spatial audio determined from the plurality of sets of coordinates.

Abstract: Methods, systems, and apparatus, including medium-encoded computer program products, for aggregating accounts using machine learning. User interaction data can be obtained for a user and can describe interactions by the user with a given account of multiple different accounts assigned to the user on one or more computer systems. An input that includes the user interaction data is processed using a machine learning model that is configured to produce a result that includes a first account embedding that differs from the user interaction data. From at least the first account embedding, an account group is determined that corresponds to the user interaction data. A first action is performed based on the account group, wherein the first action differs from a second action that would have been performed based on a different account group that is not the account group.

Abstract: A method for generating a video transition sequence between a first and a second video is provided. The method first identifies and embeds starting and ending images for the video transition sequence into a latent vector space. The method then interpolates to generate an interpolation trajectory between the starting and ending image embeddings within the latent vector space. The method then reconstructs the interpolation trajectory from the latent vector space to a final video transition sequence in the original image vector space. This video transition sequence is then displayed so as to smoothly transition between two videos.