Abstract: In one embodiment, a method includes receiving an input video comprising a plurality of image frames including an object of interest. Based on the plurality of image frames, a motion associated with the object of interest is determined, and the plurality of image frames are classified using a machine-learning model to identify one of the plurality of image frames that indicates a moment of perception of the determined motion.

Abstract: In one embodiment, a method includes by a computing device, detecting a sensory input, identifying, using a machine-learning model, one or more attributes associated with the machine-learning model, wherein the attributes are identified based on the sensory input in accordance with the model's training, and presenting the attributes as output. The identifying may be performed at least in part by an inference engine that interacts with the model. The sensory input may include an input image received from a camera, and the model may identify the attributes based on an input object in the input image in accordance with the model's training. The model may include a convolutional neural network trained using training data that associates training sensory input with the attributes. The training sensory input may include a training image of a training object, and the input object may be classified in the same class as the training object.

Abstract: In one embodiment, a method includes receiving an input video comprising a plurality of image frames including an object of interest. Based on the plurality of image frames, a motion associated with the object of interest is determined, and the plurality of image frames are classified using a machine-learning model to identify one of the plurality of image frames that indicates detection of the determined motion.

Abstract: In one embodiment, a method includes receiving a user request from a client device associated with a user, accessing an instructional file comprising one or more binary inference engines and one or more encrypted model data corresponding to the one or more binary inference engines, respectively, selecting a binary inference engine from the one or more binary inference engines in the accessed instructional file based on the user request, sending a validation request for a permission to execute the binary inference engine to a licensing server, receiving the permission from the licensing server, decrypting the encrypted model data corresponding to the binary inference engine by a decryption key, executing the binary inference engine based on the user request and the decrypted model data, and sending one or more execution results responsive to the execution of the binary inference engine to the client device.

Abstract: In one embodiment, a computing device receives, from one or more cameras, a video stream comprising multiple frames, where the video stream is received at a first quality. The computing device analyzes, using a machine-learning model, images in the frames, where the machine-learning model has been trained to detect one or more objects-of-interest in the images. The computing device identifies a sequence-of-interest including consecutive frames of the video stream, where at least one object-of-interest was detected in at least one of the consecutive frames. The computing device generates a video package including the sequence-of-interest.

Abstract: In one embodiment, a computing device receives, from one or more cameras, a video stream comprising multiple frames, where the video stream is received at a first quality. The computing device analyzes, using a machine-learning model, images in the frames, where the machine-learning model has been trained to detect one or more objects-of-interest in the images. The computing device identifies a sequence-of-interest including consecutive frames of the video stream, where at least one object-of-interest was detected in at least one of the consecutive frames. The computing device generates a video package including the sequence-of-interest.

Abstract: In one embodiment, a method includes by a computing device, detecting a sensory input, identifying, using a machine-learning model, one or more attributes associated with the machine-learning model, wherein the attributes are identified based on the sensory input in accordance with the model's training, and presenting the attributes as output. The identifying may be performed at least in part by an inference engine that interacts with the model. The sensory input may include an input image received from a camera, and the model may identify the attributes based on an input object in the input image in accordance with the model's training. The model may include a convolutional neural network trained using training data that associates training sensory input with the attributes. The training sensory input may include a training image of a training object, and the input object may be classified in the same class as the training object.

Abstract: Systems, methods, and computer-readable media for sending links or hotlinks to a device are provided. The device may be on a wireless network, a wired network, or directly coupled to the device sending the link. The link/hotlink contains an associated action that is performed by the device receiving the link. The associated action may be performed when a user interface is activated or automatically when the link/hotlink is received by the device. The action can be a broadcast/streaming action, Internet action, call action, download action, or upload action. The broadcast action instructs the device to tune to a particular broadcast, the streaming action instructs the device to stream specified content/media, the internet action instructs the device to access an Internet site, and the call action instructs the device to call a telephone number. Other embodiments may be described and/or claimed.

Abstract: In one embodiment, a method includes receiving an input video comprising a plurality of image frames including an object of interest. Based on the plurality of image frames, a motion associated with the object of interest is determined, and the plurality of image frames are classified using a machine-learning model to identify one of the plurality of image frames that indicates detection of the determined motion.

Abstract: In one embodiment, a method includes by a computing device, detecting a sensory input, identifying, using a machine-learning model, one or more attributes associated with the machine-learning model, wherein the attributes are identified based on the sensory input in accordance with the model's training, and presenting the attributes as output. The identifying may be performed at least in part by an inference engine that interacts with the model. The sensory input may include an input image received from a camera, and the model may identify the attributes based on an input object in the input image in accordance with the model's training. The model may include a convolutional neural network trained using training data that associates training sensory input with the attributes. The training sensory input may include a training image of a training object, and the input object may be classified in the same class as the training object.

Abstract: In one embodiment, a computing device receives, from one or more cameras, a video stream comprising multiple frames, where the video stream is received at a first quality. The computing device analyzes, using a machine-learning model, images in the frames, where the machine-learning model has been trained to detect one or more objects-of-interest in the images. The computing device identifies a sequence-of-interest including consecutive frames of the video stream, where at least one object-of-interest was detected in at least one of the consecutive frames. The computing device generates a video package including the sequence-of-interest.

Abstract: In one embodiment, a method includes receiving a machine-learning model trained to detect a specified motion using multiple videos, wherein each video has at least one frame labeled as a moment of perception of the specified motion, identifying an object-of-interest depicted in an input video, detecting a motion of the object-of-interest, determining that the detected motion is the specified motion, and classifying one of the frames of the input video as the moment of perception of the specified motion.

Abstract: Systems, methods, and computer-readable media for sending links or hotlinks to a device are provided. The device may be on a wireless network, a wired network, or directly coupled to the device sending the link. The link/hotlink contains an associated action that is performed by the device receiving the link. The associated action may be performed when a user interface is activated or automatically when the link/hotlink is received by the device. The action can be a broadcast/streaming action, Internet action, call action, download action, or upload action. The broadcast action instructs the device to tune to a particular broadcast, the streaming action instructs the device to stream specified content/media, the internet action instructs the device to access an Internet site, and the call action instructs the device to call a telephone number. Other embodiments may be described and/or claimed.

Abstract: In one embodiment, a method includes receiving a machine-learning model trained to detect a specified motion using multiple videos, wherein each video has at least one frame labeled as a moment of perception of the specified motion, identifying an object-of-interest depicted in an input video, detecting a motion of the object-of-interest, determining that the detected motion is the specified motion, and classifying one of the frames of the input video as the moment of perception of the specified motion.

Abstract: Systems, methods, and computer-readable media for sending hotlinks to a device are provided. The device may be on a wireless network, a wired network, or directly coupled to the device sending the hot link. The hot link contains an associated action that is performed by the device receiving the hot link. The associated action may be performed when a user interface is activated or automatically when the hot link is received by the device. The action can be a broadcast action, Internet action, call action, download action, or upload action. The broadcast action instructs the device to tune to a particular broadcast in response to the selection of the user interface. The internet action instructs the device to access an Internet site in response to the selection of the user interface. The call action instructs the device to call a telephone number in response to the selection of use interface.

Abstract: In one embodiment, a method includes by a computing device, detecting a sensory input, identifying, using a machine-learning model, one or more attributes associated with the machine-learning model, wherein the attributes are identified based on the sensory input in accordance with the model's training, and presenting the attributes as output. The identifying may be performed at least in part by an inference engine that interacts with the model. The sensory input may include an input image received from a camera, and the model may identify the attributes based on an input object in the input image in accordance with the model's training. The model may include a convolutional neural network trained using training data that associates training sensory input with the attributes. The training sensory input may include a training image of a training object, and the input object may be classified in the same class as the training object.

Abstract: In one embodiment, a method includes receiving a user request from a client device associated with a user, accessing an instructional file comprising one or more binary inference engines and one or more encrypted model data corresponding to the one or more binary inference engines, respectively, selecting a binary inference engine from the one or more binary inference engines in the accessed instructional file based on the user request, sending a validation request for a permission to execute the binary inference engine to a licensing server, receiving the permission from the licensing server, decrypting the encrypted model data corresponding to the binary inference engine by a decryption key, executing the binary inference engine based on the user request and the decrypted model data, and sending one or more execution results responsive to the execution of the binary inference engine to the client device.

Abstract: A mobile communication device is equipped with hardware and/or software components to enable the device to output a data in a form of a radio frequency signal, emulating outputting of the data by either an active or a passive RFID transponder. The data may be a security key or an identifier. Emulation of an active RFID transponder includes facilitating selection of the data and instruction to output by a user. Emulation of a passive RFID transponder includes detecting for proximal presence of a RFID reader. Either case, provision of the data to the mobile communication device may include provision of associated signal attribute(s).

Abstract: Systems, methods, and computer-readable media for sending hotlinks to a device are provided. The device may be on a wireless network, a wired network, or directly coupled to the device sending the hot link. The hot link contains an associated action that is performed by the device receiving the hot link. The associated action may be performed when a user interface is activated or automatically when the hot link is received by the device. The action can be a broadcast action, Internet action, call action, download action, or upload action. The broadcast action instructs the device to tune to a particular broadcast in response to the selection of the user interface. The internet action instructs the device to access an Internet site in response to the selection of the user interface. The call action instructs the device to call a telephone number in response to the selection of use interface.

Abstract: The invention sends a hot link to a device. The device may be on a wireless network, a wired network, or directly coupled to the device sending the hot link. The hot link contains an associated action that is performed by the device receiving the hot link. The associated action may be performed when a user interface is activated or automatically when the hot link is received by the device. The action can be a broadcast action, Internet action, call action, download action, or upload action. The broadcast action instructs the device to tune to a particular broadcast in response to the selection of the user interface. The internet action instructs the device to access an Internet site in response to the selection of the user interface. The call action instructs the device to call a telephone number in response to the selection of use interface.