Abstract: Disclosed is a technical solution to process a video that captures actions to be performed for completing a task based on a chronological sequence of stages within the task. An example system may identify an action sequence from an instruction for the task. The system inputs the action sequence into a trained model (e.g., a recurrent neural network), which outputs the chronological sequence of stages. The RNN may be trained through self-supervised learning. The system may input the video and the chronological sequence of stages into another trained model, e.g., a temporal convolutional network. The other trained model may include hidden layers arranged before an attention layer. The hidden layers may extract features from the video and feed the features into the attention layer. The attention layer may determine attention weights of the features based on the chronological sequence of stages.

Abstract: Systems, apparatuses, and methods include technology that generates final frame predictions for a first plurality of frames of a video, where the first plurality of frames is associated with unlabeled data. The technology predicts an ordered list of actions for the first plurality of frames based on the final frame predictions, and temporally aligning the ordered list of actions to the final frame predictions to generate labels.

Abstract: Herein is disclosed a virtual embodiment display system comprising one or more image sensors, configured to receive one or more images of a vehicle occupant; one or more processors, configured to determine a gaze direction of the vehicle occupant from the one or more images; select a display location corresponding to the determined gaze direction; and control an image display device to display a virtual embodiment of an intelligent agent at the display location; the image display device, configured to display the virtual embodiment of the intelligent agent at the selected display location according to the one or more processors.

Abstract: Various systems and methods for personal sensory drones are described herein. A personal sensory drone system includes a drone remote control system comprising: a task module to transmit a task to a drone swarm for the drone swarm to execute, the drone swarm including at least two drones; a transceiver to receive information from the drone swarm related to the task; and a user interface module to present a user interface based on the information received from the drone swarm.

Abstract: Herein is disclosed a virtual embodiment display system comprising one or more image sensors, configured to receive one or more images of a vehicle occupant; one or more processors, configured to determine a gaze direction of the vehicle occupant from the one or more images; select a display location corresponding to the determined gaze direction; and control an image display device to display a virtual embodiment of an intelligent agent at the display location; the image display device, configured to display the virtual embodiment of the intelligent agent at the selected display location according to the one or more processors.

Abstract: Various systems and methods for personal sensory drones are described herein. A personal sensory drone system includes a drone remote control system comprising: a task module to transmit a task to a drone swarm for the drone swarm to execute, the drone swarm including at least two drones; a transceiver to receive information from the drone swarm related to the task; and a user interface module to present a user interface based on the information received from the drone swarm.

Abstract: Herein is disclosed a virtual embodiment display system comprising one or more image sensors, configured to receive one or more images of a vehicle occupant; one or more processors, configured to determine a gaze direction of the vehicle occupant from the one or more images; select a display location corresponding to the determined gaze direction; and control an image display device to display a virtual embodiment of an intelligent agent at the display location; the image display device, configured to display the virtual embodiment of the intelligent agent at the selected display location according to the one or more processors.

Abstract: Various systems and methods for personal sensory drones are described herein. A personal sensory drone system includes a drone remote control system comprising: a task module to transmit a task to a drone swarm for the drone swarm to execute, the drone swarm including at least two drones; a transceiver to receive information from the drone swarm related to the task; and a user interface module to present a user interface based on the information received from the drone swarm.

Abstract: Herein is disclosed a virtual embodiment display system comprising one or more image sensors, configured to receive one or more images of a vehicle occupant; one or more processors, configured to determine a gaze direction of the vehicle occupant from the one or more images; select a display location corresponding to the determined gaze direction; and control an image display device to display a virtual embodiment of an intelligent agent at the display location; the image display device, configured to display the virtual embodiment of the intelligent agent at the selected display location according to the one or more processors.

Abstract: Various systems and methods for personal sensory drones are described herein. A personal sensory drone system includes a drone remote control system comprising: a task module to transmit a task to a drone swarm for the drone swarm to execute, the drone swarm including at least two drones; a transceiver to receive information from the drone swarm related to the task; and a user interface module to present a user interface based on the information received from the drone swarm.

Abstract: A system of using a drone for network connectivity, the system may comprise: a connectivity module to: detect an error associated with network traffic on a network connection utilized by a user device; query a connection datastore to retrieve at least one access point location that at least one device of the user has utilized within a predetermined period; a drone coordination module to: transmit configuration settings to a drone, the configuration settings including the at least one access point location and a mode of operation for the drone; and route at least a portion of the network traffic of the user device to the drone for transmission according to the configuration settings.

Abstract: A system of using a drone for network connectivity, the system may comprise: a connectivity module to: detect an error associated with network traffic on a network connection utilized by a user device; query a connection datastore to retrieve at least one access point location that at least one device of the user has utilized within a predetermined period; a drone coordination module to: transmit configuration settings to a drone, the configuration settings including the at least one access point location and a mode of operation for the drone; and route at least a portion of the network traffic of the user device to the drone for transmission according to the configuration settings.