Abstract: The present disclosure provides user interface methods of and systems for displaying at least one available action overlaid on an image, comprising displaying an image; selecting at least one action and assigning a ranking weight thereto based on at least one of (1) image content, (2) current device location, (3) location at which the image was taken, (4) date of capturing the image; (5) time of capturing the image; and (6) a user preference signature representing prior actions chosen by a user and content preferences learned about the user; and ranking the at least one action based on its assigned ranking weight.

Abstract: A method of selecting and presenting content on a first system based on user preferences learned on a second system is provided. The method includes receiving a user's input for identifying items of the second content system and, in response thereto, presenting a subset of items of the second content system and receiving the user's selection actions thereof. The method includes analyzing the selected items to learn the user's content preferences for the content of the second content system and determining a relationship between the content of the first and second content systems to determine preferences relevant to items of the first content system. The method includes, in response subsequent user input for items of the first content system, selecting and ordering a collection of items of the first content system based on the user's learned content preferences determined to be relevant to the items of the first content system.

Abstract: The present disclosure provides user interface methods of and systems for displaying at least one available action overlaid on an image, comprising displaying an image; selecting at least one action and assigning a ranking weight thereto based on at least one of (1) image content, (2) current device location, (3) location at which the image was taken, (4) date of capturing the image; (5) time of capturing the image; and (6) a user preference signature representing prior actions chosen by a user and content preferences learned about the user; and ranking the at least one action based on its assigned ranking weight.

Abstract: Disclosed systems, methods, and computer readable media can detect an association between semantic entities and generate semantic information between entities. For example, semantic entities and associated semantic collections present in knowledge bases can be identified. A time period can be determined and divided into time slices. For each time slice, word embeddings for the identified semantic entities can be generated; a first semantic association strength between a first semantic entity input and a second semantic entity input can be determined; and a second semantic association strength between the first semantic entity input and semantic entities associated with a semantic collection that is associated with the second semantic entity can be determined. An output can be provided based on the first and second semantic association strengths.

Abstract: A method and system are provided for identifying a television content item desired by a television viewer from a set of television content items. Each of the television content items has one or more associated descriptors. The system receives from the television viewer a reduced text search entry directed at identifying the desired television content item. The search entry is a prefix substring of one or more words relating to the desired television content item. The system dynamically identifies a group of one or more television content items from the set of television content items having one or more descriptors matching the search entry as the television viewer enters each character of the search entry. The system then transmits the names of the one or more television content items of the identified group to be displayed on a device operated by the television viewer.

Abstract: Disclosed systems, methods, and computer readable media can detect an association between semantic entities and generate semantic information between entities. For example, semantic entities and associated semantic collections present in knowledge bases can be identified. A time period can be determined and divided into time slices. For each time slice, word embeddings for the identified semantic entities can be generated; a first semantic association strength between a first semantic entity input and a second semantic entity input can be determined; and a second semantic association strength between the first semantic entity input and semantic entities associated with a semantic collection that is associated with the second semantic entity can be determined. An output can be provided based on the first and second semantic association strengths.

Abstract: A method of selecting and presenting content on a first system based on user preferences learned on a second system is provided. The method includes receiving a user's input for identifying items of the second content system and, in response thereto, presenting a subset of items of the second content system and receiving the user's selection actions thereof. The method includes analyzing the selected items to learn the user's content preferences for the content of the second content system and determining a relationship between the content of the first and second content systems to determine preferences relevant to items of the first content system. The method includes, in response subsequent user input for items of the first content system, selecting and ordering a collection of items of the first content system based on the user's learned content preferences determined to be relevant to the items of the first content system.

Abstract: A method of selecting and presenting content on a first system based on user preferences learned on a second system is provided. The method includes receiving a user's input for identifying items of the second content system and, in response thereto, presenting a subset of items of the second content system and receiving the user's selection actions thereof. The method includes analyzing the selected items to learn the user's content preferences for the content of the second content system and determining a relationship between the content of the first and second content systems to determine preferences relevant to items of the first content system. The method includes, in response subsequent user input for items of the first content system, selecting and ordering a collection of items of the first content system based on the user's learned content preferences determined to be relevant to the items of the first content system.

Abstract: A method of selecting and presenting content based on learned user preferences is provided. The method includes receiving search input from the user for identifying desired content items and receiving content selection actions from the user. The method further includes analyzing the date, day, and time of content selection actions by the user and analyzing descriptive terms associated with the selected content items to learn a periodicity of user selections of similar content items. In response to subsequent searches by the user, the method calls for selecting and ordering a collection of content items for presentation to the user based on comparing the user's search input to descriptive terms associated with content items and based on the learned periodicities of the user.

Abstract: A user-interface method of selecting and presenting a collection of content items based on user navigation and selection actions associated with the content is provided. The method includes associating a relevance weight on a per user basis with content items to indicate a relative measure of likelihood that the user desires the content item. The method includes receiving a user's navigation and selections actions for identifying desired content items, and in response, adjusting the associated relevance weight of the selected content item and group of content items containing the selected item. The method includes, in response to subsequent user input, selecting and presenting a subset of content items and content groups to the user ordered by the adjusted associated relevance weights assigned to the content items and content groups.

Abstract: A method and system are provided for identifying a television content item desired by a television viewer from a set of television content items. Each of the television content items has one or more associated descriptors. The system receives from the television viewer a reduced text search entry directed at identifying the desired television content item. The search entry is a prefix substring of one or more words relating to the desired television content item. The system dynamically identifies a group of one or more television content items from the set of television content items having one or more descriptors matching the search entry as the television viewer enters each character of the search entry. The system then transmits the names of the one or more television content items of the identified group to he displayed on a device operated by the television viewer.

Abstract: A method of selecting and presenting content based on learned user preferences is provided. The method includes providing a content System including a set of content items organized by genre characterizing the content items, and wherein the set of content items contains microgenre metadata further characterizing the content items. The method also includes receiving search input from the user for identifying desired content items and, in response, presenting a subset of content items to the user. The method further includes receiving content item selection actions from the user and analyzing the microgenre metadata within the selected content items to learn the preferred microgenres of the user. The method includes, in response to receiving subsequent user search input, selecting and presenting content items in an order that portrays as relatively more relevant those content items containing microgenre metadata that more closely match the learned microgenre preferences of the user.

Abstract: A user-interface method of selecting and presenting a collection of content items based on user navigation and selection actions associated with the content is provided. The method includes associating a relevance weight on a per user basis with content items to indicate a relative measure of likelihood that the user desires the content item. The method includes receiving a user's navigation and selections actions for identifying desired content items, and in response, adjusting the associated relevance weight of the selected content item and group of content items containing the selected item. The method includes, in response to subsequent user input, selecting and presenting a subset of content items and content groups to the user ordered by the adjusted associated relevance weights assigned to the content items and content groups.

Abstract: A method of selecting and presenting content based on learned user preferences is provided. The method includes providing a content system including a set of content items organized by genre characterizing the content items, and wherein the set of content items contains microgenre metadata further characterizing the content items. The method also includes receiving search input from the user for identifying desired content items and, in response, presenting a subset of content items to the user. The method further includes receiving content item selection actions from the user and analyzing the microgenre metadata within the selected content items to learn the preferred microgenres of the user. The method includes, in response to receiving subsequent user search input, selecting and presenting content items in an order that portrays as relatively more relevant those content items containing microgenre metadata that more closely match the learned microgenre preferences of the user.

Abstract: The present disclosure provides user interface methods of and systems for displaying at least one available action overlaid on an image, comprising displaying an image; selecting at least one action and assigning a ranking weight thereto based on at least one of (1) image content, (2) current device location, (3) location at which the image was taken, (4) date of capturing the image; (5) time of capturing the image; and (6) a user preference signature representing prior actions chosen by a user and content preferences learned about the user; and ranking the at least one action based on its assigned ranking weight.

Abstract: Disclosed systems, methods, and computer readable media can detect an association between semantic entities and generate semantic information between entities. For example, semantic entities and associated semantic collections present in knowledge bases can be identified. A time period can be determined and divided into time slices. For each time slice, word embeddings for the identified semantic entities can be generated; a first semantic association strength between a first semantic entity input and a second semantic entity input can be determined; and a second semantic association strength between the first semantic entity input and semantic entities associated with a semantic collection that is associated with the second semantic entity can be determined. An output can be provided based on the first and second semantic association strengths.

Abstract: The present disclosure provides user interface methods of and systems for displaying at least one available action overlaid on an image, comprising displaying an image; selecting at least one action and assigning a ranking weight thereto based on at least one of (1) image content, (2) current device location, (3) location at which the image was taken, (4) date of capturing the image; (5) time of capturing the image; and (6) a user preference signature representing prior actions chosen by a user and content preferences learned about the user; and ranking the at least one action based on its assigned ranking weight.

Abstract: A method of selecting and presenting content based on learned user preferences is provided. The method includes providing a content system including a set of content items organized by genre characterizing the content items, and wherein the set of content items contains microgenre metadata further characterizing the content items. The method also includes receiving search input from the user for identifying desired content items and, in response, presenting a subset of content items to the user. The method further includes receiving content item selection actions from the user and analyzing the microgenre metadata within the selected content items to learn the preferred microgenres of the user. The method includes, in response to receiving subsequent user search input, selecting and presenting content items in an order that portrays as relatively more relevant those content items containing microgenre metadata that more closely match the learned microgenre preferences of the user.

Abstract: Systems and methods are provided herein for enabling a user to easily revert back to a conceptual boundary of a media stream that is not predefined by an editor or user. In some embodiments, control circuitry may execute a natural language engine to heuristically determine a plurality of conceptual boundaries of the media stream as the media stream is presented. When control circuitry executes the natural language engine, control circuitry may identify a semantic clue in the media stream and may determine a context of the semantic clue. Control circuitry may, based on the semantic clue and the context, determine that the semantic clue occurs at a conceptual boundary. In response to a user command to return to a previously presented conceptual boundary of the plurality of conceptual boundaries, control circuitry may cause the media stream to be presented from the previously presented conceptual boundary.

Abstract: A method of selecting and presenting content based on learned user preferences is provided. The method includes providing a content system including a set of content items organized by genre characterizing the content items, and wherein the set of content items contains microgenre metadata further characterizing the content items. The method also includes receiving search input from the user for identifying desired content items and, in response, presenting a subset of content items to the user. The method further includes receiving content item selection actions from the user and analyzing the microgenre metadata within the selected content items to learn the preferred microgenres of the user. The method includes, in response to receiving subsequent user search input, selecting and presenting content items in an order that portrays as relatively more relevant those content items containing microgenre metadata that more closely match the learned microgenre preferences of the user.