Abstract: A user device (e.g., voice assistant device, voice enabled device, smart device, computing device, etc.) may receive/detect audio content (e.g., speech, etc.) that includes a wake word and/or words similar to a wake word. The user device may require a wake word, a portion of the wake word, or words similar to the wake word to be detected prior to interacting with a user. The user device may, based on characteristics of the audio content, determine if the audio content originates from an authorized user. The user device may decrease and/or increase scrutiny applied to wake word detection based on whether audio content originates from an authorized user.

Abstract: Systems and methods are described for improving content classifications and metadata for a plurality of content items. The content items may comprise various forms of media content. A content recommendation processing system may automatically annotate and/or classify content items with a plurality of metadata tag information describing content items. The disclosed system and methods may also determine and/or assign applicability scores indicating a level or degree of suitability for annotating a content item with particular metadata tag information, and may utilize applicability scores to provide content to calibrate metadata information for a plurality of content items, and further to provide content recommendations to users.

Abstract: Methods and apparatus for generating personalized menus and a media content interface are provided. In one example, different device specific media asset lists are generated at different user devices for the same user based on the media consumption history at each device. In another example, media asset listings are displayed for multicast and on-demand media assets determined to be of interest to the user. An on-demand media asset is listed at a time when no other multicast media assets are listed. In another example, media asset listings for successive episodes of a media series are displayed in sequential order in response to an input to view successive episodes of a media series. In another example, a text search for media content is performed in which the search priority of media provider results is decreased relative to the search priority of media asset results as additional characters are added to a search string.

Abstract: Methods and apparatus for generating personalized menus and a media content interface are provided. In one example, different device specific media asset lists are generated at different user devices for the same user based on the media consumption history at each device. In another example, media asset listings are displayed for multicast and on-demand media assets determined to be of interest to the user. An on-demand media asset is listed at a time when no other multicast media assets are listed. In another example, media asset listings for successive episodes of a media series are displayed in sequential order in response to an input to view successive episodes of a media series. In another example, a text search for media content is performed in which the search priority of media provider results is decreased relative to the search priority of media asset results as additional characters are added to a search string.

Abstract: Aspects described herein provide techniques for providing content recommendations such as, for example, recommendations for video content provided by a cable network. The content recommendation techniques described herein determine the extent to which previously consumed content should contribute to content considered for recommendation to a user based on the consumption time of the previously consumed content. The consumption habits of users have been observed to occur at regular intervals. These intervals are employed to adjust contribution factors for the previously consumed content when making content recommendations.

Abstract: Systems and methods for transforming a similarity matrix used to generate content item recommendation lists are described. The system may determine a weight (used to transform the similarity matrix) sufficient to place one or more content items on the content item recommendation list or at a particular position on the recommendation list. The transformed similarity matrix may further be reduced in size, and content recommendations may be generated using the transformed or reduced similarity matrix.

Abstract: Systems and methods are described for improving content classifications and metadata for a plurality of content items. The content items may comprise various forms of media content. A content recommendation processing system may automatically annotate and/or classify content items with a plurality of metadata tag information describing content items. The disclosed system and methods may also determine and/or assign applicability scores indicating a level or degree of suitability for annotating a content item with particular metadata tag information, and may utilize applicability scores to provide content to calibrate metadata information for a plurality of content items, and further to provide content recommendations to users.

Abstract: Systems and methods are described for improving content classifications and metadata for a plurality of content items. The content items may comprise various forms of media content. A content recommendation processing system may automatically annotate and/or classify content items with a plurality of metadata tag information describing content items. The disclosed system and methods may also determine and/or assign applicability scores indicating a level or degree of suitability for annotating a content item with particular metadata tag information, and may utilize applicability scores to provide content to calibrate metadata information for a plurality of content items, and further to provide content recommendations to users.

Abstract: Aspects described herein provide techniques for providing content recommendations such as, for example, recommendations for video content provided by a cable network. The content recommendation techniques described herein determine the extent to which previously consumed content should contribute to content considered for recommendation to a user based on the consumption time of the previously consumed content. The consumption habits of users have been observed to occur at regular intervals. These intervals are employed to adjust contribution factors for the previously consumed content when making content recommendations.

Abstract: According to some aspects, methods and systems may include receiving, by a computing device, metadata identifying an event occurring in a video program, and determining an expected motion of objects in the identified event. The methods and systems may further include analyzing motion energy in the video program to identify video frames in which the event occurs, and storing information identifying the video frames in which the event occurs.

Abstract: According to some aspects, methods and systems may include receiving, by a computing device, metadata identifying an event occurring in a video program, and determining an expected motion of objects in the identified event. The methods and systems may further include analyzing motion energy in the video program to identify video frames in which the event occurs, and storing information identifying the video frames in which the event occurs.

Abstract: Methods and systems for performing a search are disclosed. An example method can comprise a server receiving a search query, wherein the search query comprises one or more keywords. The server can determine a first score indicating importance of a search term to one or more content items associated with a corpus. The server can receive past searches, each comprising a search term and a selected result, and determine a second score indicating importance of a search term to one or more of the content items based on the past searches. The server can determine a third score indicating an importance of each content item to the one or more keywords based on the first score and the second score. One or more of the plurality of content items can be sent to a user based on the third score.

Abstract: Systems and methods are described for generating recommendations for content items and ranking categories of content based on a user's consumption history. The content items may comprise various forms of media content, including, video, audio, Internet webpages, etc. When a user or consumption device accesses content items, a computing device may monitor the amount of the content items consumed by a user over one or more consumption sessions. In one embodiment, a user may identify content preferences and/or provide other input to the recommendation system to further customize content rankings and recommendations.

Abstract: Systems and methods for transforming a similarity matrix used to generate content item recommendation lists are described. The system may determine a weight (used to transform the similarity matrix) sufficient to place one or more content items on the content item recommendation list or at a particular position on the recommendation list. The transformed similarity matrix may further be reduced in size, and content recommendations may be generated using the transformed or reduced similarity matrix.

Abstract: Methods and apparatus for generating personalized menus and a media content interface are provided. In one example, different device specific media asset lists are generated at different user devices for the same user based on the media consumption history at each device. In another example, media asset listings are displayed for multicast and on-demand media assets determined to be of interest to the user. An on-demand media asset is listed at a time when no other multicast media assets are listed. In another example, media asset listings for successive episodes of a media series are displayed in sequential order in response to an input to view successive episodes of a media series. In another example, a text search for media content is performed in which the search priority of media provider results is decreased relative to the search priority of media asset results as additional characters are added to a search string.

Abstract: Aspects described herein provide techniques for providing content recommendations such as, for example, recommendations for video content provided by a cable network. The content recommendation techniques described herein determine the extent to which previously consumed content should contribute to content considered for recommendation to a user based on the consumption time of the previously consumed content. The consumption habits of users have been observed to occur at regular intervals. These intervals are employed to adjust contribution factors for the previously consumed content when making content recommendations.

Abstract: According to some aspects, methods and systems may include receiving, by a computing device, metadata identifying an event occurring in a video program, and determining an expected motion of objects in the identified event. The methods and systems may further include analyzing motion energy in the video program to identify video frames in which the event occurs, and storing information identifying the video frames in which the event occurs.