Abstract: An electronic device stores, for a user of a media-providing service, a playback history that includes information about media items that have previously been consumed by the user. The electronic device receives a request to search for media content including search criteria. In response to the request, and without additional user intervention, the electronic device generates a vector representation of the user using media items from the playback history of the user that are relevant to the search criteria. The electronic device identifies one or more media content items from a media content library that match the vector representation of the user and the search criteria and provides, to the user, the one or more media content items.

Abstract: A system for refinement of a voice query interpretation interprets a voice query received at a voice-enabled device to identify commands responsive to the voice query for execution at the voice-enabled device, and enables refinement of the interpretation of the voice query through a graphical user interface generated and displayed at a GUI-capable device. The graphical user interface includes a set of selectable options relating to the voice query and identifying a refinement of the interpretation of the voice query to enable control and/or adjustment of commands to be executed by the voice-enabled device. For example, if one of the selectable options is selected, then a command associated with the selected option is identified and executed by the voice-enabled device.

Abstract: A system for refinement of a voice query interpretation interprets a voice query received at a voice-enabled device to identify commands responsive to the voice query for execution at the voice-enabled device, and enables refinement of the interpretation of the voice query through a graphical user interface generated and displayed at a GUI-capable device. The graphical user interface includes a set of selectable options relating to the voice query and identifying a refinement of the interpretation of the voice query to enable control and/or adjustment of commands to be executed by the voice-enabled device. For example, if one of the selectable options is selected, then a command associated with the selected option is identified and executed by the voice-enabled device.

Abstract: A system for refinement of a voice query interpretation interprets a voice query received at a voice-enabled device to identify commands responsive to the voice query for execution at the voice-enabled device, and enables refinement of the interpretation of the voice query through a graphical user interface generated and displayed at a GUI-capable device. The graphical user interface includes a set of selectable options relating to the voice query and identifying a refinement of the interpretation of the voice query to enable control and/or adjustment of commands to be executed by the voice-enabled device. For example, if one of the selectable options is selected, then a command associated with the selected option is identified and executed by the voice-enabled device.

Abstract: A system for refinement of a voice query interpretation interprets a voice query received at a voice-enabled device to identify commands responsive to the voice query for execution at the voice-enabled device, and enables refinement of the interpretation of the voice query through a graphical user interface generated and displayed at a GUI-capable device. The graphical user interface includes a set of selectable options relating to the voice query and identifying a refinement of the interpretation of the voice query to enable control and/or adjustment of commands to be executed by the voice-enabled device. For example, if one of the selectable options is selected, then a command associated with the selected option is identified and executed by the voice-enabled device.

Abstract: A system for refinement of a voice query interpretation interprets a voice query received at a voice-enabled device to identify commands responsive to the voice query for execution at the voice-enabled device, and enables refinement of the interpretation of the voice query through a graphical user interface generated and displayed at a GUI-capable device. The graphical user interface includes a set of selectable options relating to the voice query and identifying a refinement of the interpretation of the voice query to enable control and/or adjustment of commands to be executed by the voice-enabled device. For example, if one of the selectable options is selected, then a command associated with the selected option is identified and executed by the voice-enabled device.

Abstract: An educational system that includes a content item selector configured to select at least one content item from a database so that the reaction of the user required by the at least one content item matches according to a predetermined criteria a prediction of how the user will react to the type of user reaction required by the at least one content item; and a content item output which presents the selected at least one content item to the user.

Abstract: A multimedia-based language learning method and system which includes implementing via one or more processors the steps of—receiving an input of multimedia content, where the multimedia content comprises a plurality of component tracks; separating the multimedia content into multimedia sections in which the plurality of component tracks share a same start and end time; retrieving a user model representing a learner's knowledge and/or interest in a foreign language; automatically assigning one or more learner-specific evaluations to the multimedia sections by evaluating one or more of the component tracks based on the user model within each of the multimedia sections; and adapting the multimedia content within the multimedia sections based on the assigned learner-specific evaluations to render the multimedia content more useful to the learner for learning the foreign language.

Abstract: A computer-implemented method for automatically generating learning exercises, including determining a target learning item in response to an event, obtaining a knowledge level of a learner in relation to the target learning item based on a model of the learner as produced by an automated learner model, associating a level of difficulty with the obtained knowledge level of the learner, retrieving a learning exercise pattern from an exercise pattern database, automatically generating a learning exercise relating to the retrieved learning exercise pattern based on the model of the learner and the associated level of difficulty; and presenting the learning exercise to the learner via an exercise interface.

Abstract: A computer-implemented adaptive learning method is disclosed. The method is intended for performance within the context of a task being carried out by a user. At least one of a sequence of elements presented to the user as part of the task is designated as a learning item. A learning object is selected in dependence upon the designated learning item, information relating to previous performance of the learning method in relation to the user, and a predetermined scheme devised to manage an overall learning process for the user. Presentation of the selected learning object to the user is intended to advance the user's knowledge of the designated learning item in some way. Once the learning object has been presented to the user, the information is updated in dependence upon the presented learning object and/or how the user interacts with or responds to the presented learning object.