A computing system is described herein, where the computing system is configured to perform a search over a computer-readable index based upon a query for a user. The computer-readable index includes an identifier for a livestream video that is currently being livestreamed by way of a livestreaming service and values for respective attributes of the livestream video. The values for the respective attributes are updated as content of the livestream video alters over time. The livestream video is identified from amongst several livestream videos based upon the search, where the video is identified due to a set of values specified in the query corresponding to the values for the respective attributes in the computer-readable index. Upon the livestream video being identified, an identifier of the livestream video is transmitted to a client computing device of the user.

Embodiments of the present disclosure provide methods, systems, and apparatuses for computing a channel incrementality ratio using a machine learning model.

Embodiments of the present disclosure provide methods, systems, and apparatuses for computing a channel incrementality ratio using a machine learning model.

A voice acquisition section 126 acquires a voice of a user viewing content. A recording processing section 116 records content data and user voice data and generates a content file in which the content data and the user voice data are associated with a recording time. A voice detection section 136 detects whether or not the voice of the user has been acquired by the voice acquisition section 126. An event information generation section 140 generates event information including a start time and an end time of the acquisition of the voice of the user. A communication section 102 transmits the event information to a server.

A voice acquisition section 126 acquires a voice of a user viewing content. A recording processing section 116 records content data and user voice data and generates a content file in which the content data and the user voice data are associated with a recording time. A voice detection section 136 detects whether or not the voice of the user has been acquired by the voice acquisition section 126. An event information generation section 140 generates event information including a start time and an end time of the acquisition of the voice of the user. A communication section 102 transmits the event information to a server.

Embodiments of the present disclosure provide methods, systems, and apparatuses for computing a channel incrementality ratio using a machine learning model.

Embodiments of the present disclosure provide methods, systems, and apparatuses for computing a channel incrementality ratio using a machine learning model.

A display device according to the present disclosure may comprise: a display which displays a content; and a controller which, upon receiving a voice command, controls the display to display content-related information corresponding to a scene at the time of receiving the voice command, wherein the controller may change arrangement of the content-related information on the basis of a category requested by the voice command.

Methods and systems are provided for encoding audio data from an audio file, wherein the audio data comprises audio samples. Audio data is segmented from the audio file in order to obtain at least one segment. Each segment comprises a time interval of the audio data, and each segment also comprises a plurality of audio samples grouped in frames. A segment index and a description stream containing the segment index is then generated. The segment index comprises the position of the segments within the audio file. A segment stream containing the audio data of one particular segment is then generated. At least part of the audio data is encrypted during the generation of the segment stream with an encryption key.

Example embodiments provide systems and methods for crowdsourced skipping of media content portions. In an example method, a plurality of content tags are received from a plurality of media content devices over a communication network. Each of the plurality of content tags may designate a location within a media content item. The content tags are processed to generate aggregated content tags for the media content item. The aggregated content tags may designate one or more portions of the media content item for modified playback. The aggregated content tags for the media content item are transmitted over the communication network to a first media content device separate from the plurality of media content devices.