Aspects of the subject disclosure may include, for example, receiving viewership data for a plurality of devices associated with a household, the devices for viewing content items received over a network from a network provider at the household, concatenating respective viewership data for respective devices of the plurality of devices, forming respective device documents, vectorizing the respective device documents to form vectorized device documents, concatenating the vectorized device documents to form a household corpus of viewership data for the household, training a clustering model on the household corpus of viewership data to form a household topological fingerprint (HTF) for the household, the HTF forming a vector classification of viewership patterns for the household, and selecting content items for the household based on the HTF. Other embodiments are disclosed.

Aspects of the subject disclosure may include, for example, capturing first monitoring information associated with an audience that is viewing a presentation, determining an actual audience engagement level by performing pattern recognition on the first monitoring information, comparing the actual audience engagement level with an expected audience engagement level to generate an engagement score for the presentation, and transmitting, to a device, a haptic feedback according to the engagement score during the presenting of the presentation. Other embodiments are disclosed.

Aspects of the subject disclosure may include, for example, capturing first monitoring information associated with an audience that is viewing a presentation, determining an actual audience engagement level by performing pattern recognition on the first monitoring information, comparing the actual audience engagement level with an expected audience engagement level to generate an engagement score for the presentation, and transmitting, to a device, a haptic feedback according to the engagement score during the presenting of the presentation. Other embodiments are disclosed.

Aspects of the subject disclosure may include, for example, a method that includes obtaining metadata from media content and consumed by network subscribers; determining for each network subscriber a consumer context associated with the media content; and determining a media consumption pattern for each network subscriber based on the metadata and the consumer context, thereby generating a plurality of media consumption patterns. The method further includes aggregating the media consumption patterns; determining, based on the aggregated media consumption patterns, a media consumption trend for the network subscribers; and correlating the media consumption trend with a profile including a current activity for a network subscriber of the plurality of network subscribers, thereby generating a recommendation for the network subscriber regarding new media content not previously consumed by the network subscriber. The method also includes communicating the recommendation to the network subscriber. Other embodiments are disclosed.

A computer-implemented method of using video program viewer interaction data that has been loaded to a media measurement database as input to a measurement engine which then calculates Linear, DVR, and VOD asset viewing activity at three levels: (a) Video Program, (b) Video Program Airing, (c) Video Program Airing Segment, where each level provides summary metrics for groupings of Demographic, Geographic, and/or Device Characteristic, and also second-by-second viewing metrics, including counting advertising impressions, within the Demographic, Geographic, Device groupings. System also accounts for reduced value of ad viewing when viewer is using trick plays or when viewer delays viewing recorded content. Together these metrics provide detailed information on customer viewing behavior which can be used to drive business decisions for service providers, advertisers and content producers. Additionally, a viewing histogram analysis is produced.

A computer-implemented method of using video program viewer interaction data that has been loaded to a media measurement database as input to a measurement engine which then calculates Linear, DVR, and VOD asset viewing activity at three levels: (a) Video Program, (b) Video Program Airing, (c) Video Program Airing Segment, where each level provides summary metrics for groupings of Demographic, Geographic, and/or Device Characteristic, and also second-by-second viewing metrics, including counting advertising impressions, within the Demographic, Geographic, Device groupings. System also accounts for reduced value of ad viewing when viewer is using trick plays or when viewer delays viewing recorded content. Together these metrics provide detailed information on customer viewing behavior which can be used to drive business decisions for service providers, advertisers and content producers. Additionally, a viewing histogram analysis is produced.

Viewers of a video stream may generate video clips including different portions of the video stream. Popularity measures may be computed for the video clips, for example based on tracked actions associated with the video clips, such as frequency of playing and sharing of the video clips. The popularity measures may be used to select and provide video clips to viewers. Video items may be generated by streamers, such as may include selected portions of video of streamers playing a game. A video item generated by a particular streamer may be displayed to viewers only during times when that particular streamer is participating in an active game session. Viewers of the video item may provide input that allows viewers to receive a live stream of the streamer's active game session that it is being played simultaneously with the display of the video item.

An example apparatus includes an association controller to generate a tree structure association for a total audience size that accessed a plurality of media items, the tree structure association including a first node representative of a first media item accessed by first audience members of the total audience size and a second node representative of a second media item accessed by second audience members of the total audience size, a matrix generator to generate a matrix by selecting a sum of probabilities value corresponding to the tree structure association, the sum of probabilities value representative of a probability of the first audience members accessing the first media item and storing the sum of probabilities value in an element of the matrix, and a commercial solver to estimate a deduplicated audience size of the total audience size using the matrix.

A methods and system for receiving viewer input to a television program may include the step of receiving the television program and receiving, during presentation of the television program on a television, at a media box interposed between a media feed and the television, an input from a viewer device. The input may be processed to determine a desired graphic and a designated location for the desired graphic. Based upon the determination, the media box may modify the media feed to create a modified video feed comprising the desired graphic. The modified video feed may be output to the television, whereby the television presents the program with the graphic displayed on program at the designated location. Finally, the media box may report the input to analytic service.

Systems and methods for effectively and efficiently receiving, analyzing, and using subscriber content choice information are described. One embodiment of the present invention includes a subscriber-action database, such as a clickstream database, a media-content database, and a merge processor electronically connected to the two databases for merging data in the subscriber-action database with data in the media-content database to create a subscriber content-choice database. The media-content database may include programming and/or advertising data. The media-content database may also include category and context information to provide the capability to comprehensively analyze data in the subscriber content-choice database.