An application gateway including application service programming positioned at a user premises can provide voice controlled and managed services to a user and one or more endpoint devices associated with the application gateway. The application gateway can be controlled remotely by the application service provider through a service management center and configured to execute an application service provided from the application service provider. The application gateway can execute the application service at the user premises upon voice command by a user and independent of application services executing on the application service provider's network. An application service logic manager can communicate with an application service enforcement manager to verify that the request conforms with the policy and usage rules associated with the application service in order to authorize execution of the application service on the application gateway, either directly or through endpoint devices.

An application gateway including application service programming positioned at a user premises can provide voice controlled and managed services to a user and one or more endpoint devices associated with the application gateway. The application gateway can be controlled remotely by the application service provider through a service management center and configured to execute an application service provided from the application service provider. The application gateway can execute the application service at the user premises upon voice command by a user and independent of application services executing on the application service provider's network. An application service logic manager can communicate with an application service enforcement manager to verify that the request conforms with the policy and usage rules associated with the application service in order to authorize execution of the application service on the application gateway, either directly or through endpoint devices.

Certain exemplary embodiments relate to entertainment systems and, more particularly, to systems that incorporate digital downloading jukebox features and improved user interfaces. For instance, a smart search may be provided, e.g., where search results vary based on the popularity of songs within the venue, in dependence on songs being promoted, etc. As another example, a tile-based approach to organizing groupings of songs is provided. Groupings may involve self-populating collections of songs that combine centrally-promoted songs, songs in a given genre that are popular across an audiovisual distribution network, and songs that are locally popular and match up with the given genre (e.g., because of shared attributes such as same or similar genre, artist, etc.). Different tile visual presentations also are contemplated, as are different physical jukebox designs. In certain example embodiments, a sealed core unit with the “brains” of the jukebox is insertable into a docking station.

Certain exemplary embodiments relate to entertainment systems and, more particularly, to systems that incorporate digital downloading jukebox features and improved user interfaces. For instance, a smart search may be provided, e.g., where search results vary based on the popularity of songs within the venue, in dependence on songs being promoted, etc. As another example, a tile-based approach to organizing groupings of songs is provided. Groupings may involve self-populating collections of songs that combine centrally-promoted songs, songs in a given genre that are popular across an audiovisual distribution network, and songs that are locally popular and match up with the given genre (e.g., because of shared attributes such as same or similar genre, artist, etc.). Different tile visual presentations also are contemplated, as are different physical jukebox designs. In certain example embodiments, a sealed core unit with the “brains” of the jukebox is insertable into a docking station.

A network device is configured to (i) play back a media item indicated by a remote playback queue provided by a cloud-based computing system, (ii) receive an indication that a playback device is available for playback, (iii) display a now playing screen including (a) information identifying the media item, and (b) an icon that indicates that the network device is not in a connected state with any other network device, (iv) receive a first input selecting the icon, (v) in response to the first input, display a list of one or more available network devices including the playback device, (vi) receive a second input selecting the playback device from the list (vii) after receiving the second input, update the list to indicate that the playback device is selected for playback of the remote playback queue, and (viii) transfer playback of the remote playback queue from the network device to the playback device.

A network device is configured to (i) play back a media item indicated by a remote playback queue provided by a cloud-based computing system, (ii) receive an indication that a playback device is available for playback, (iii) display a now playing screen including (a) information identifying the media item, and (b) an icon that indicates that the network device is not in a connected state with any other network device, (iv) receive a first input selecting the icon, (v) in response to the first input, display a list of one or more available network devices including the playback device, (vi) receive a second input selecting the playback device from the list (vii) after receiving the second input, update the list to indicate that the playback device is selected for playback of the remote playback queue, and (viii) transfer playback of the remote playback queue from the network device to the playback device.

The present invention relates to a lifelog device utilizing audio recognition and a method therefor, and to a device capable of recording and classifying audio lifelogs by means of an artificial intelligence algorithm. To this end, the lifelog device of the present invention comprises: an input unit for inputting lifelog data including an audio signal; and analysis unit for analyzing the inputted data; a determination unit for classifying the class of the data on the basis of the analyzed analysis value; and a recording unit for recording the inputted data and the classified class of the data.

The present invention relates to a lifelog device utilizing audio recognition and a method therefor, and to a device capable of recording and classifying audio lifelogs by means of an artificial intelligence algorithm. To this end, the lifelog device of the present invention comprises: an input unit for inputting lifelog data including an audio signal; and analysis unit for analyzing the inputted data; a determination unit for classifying the class of the data on the basis of the analyzed analysis value; and a recording unit for recording the inputted data and the classified class of the data.

An electronic device and method for recommendation of audio based on video analysis is provided. The electronic device receives one or more frames of a first scene of a plurality of scenes of a video. The first scene includes a set of objects. The electronic device applies a trained neural network model on the received one or more frames to detect the set of objects. The electronic device determines an impact score of each object of the detected set of objects of the first scene based on the application of the trained neural network model on the set of objects. The electronic device further selects at least one first object from the set of objects based on the impact score of each object, and recommends one or more first audio tracks as a sound effect for the first scene based on the selected at least one first object.

An electronic device and method for recommendation of audio based on video analysis is provided. The electronic device receives one or more frames of a first scene of a plurality of scenes of a video. The first scene includes a set of objects. The electronic device applies a trained neural network model on the received one or more frames to detect the set of objects. The electronic device determines an impact score of each object of the detected set of objects of the first scene based on the application of the trained neural network model on the set of objects. The electronic device further selects at least one first object from the set of objects based on the impact score of each object, and recommends one or more first audio tracks as a sound effect for the first scene based on the selected at least one first object.