Verizon Patent and Licensing Inc.

Praveen Kumar Bandaru

Haripriya Srinivasaraghavan

Senthil K Raghavan

Vamshi K Gillipalli

Abhishek A Desai

Yogalakshmi Narayanasamy

Veena Chugani

Zhiqun Wang

Sirisha Sripathi

Disclosed is a system for generating personalized recommendations based on dynamic and customized content selections and modeling of the content selections. The system may receive a request with an identifier and a query, and may obtain a particular recommendation configuration based the identifier and the query. The system may retrieve a set of content that satisfies the query and that is identified with at least one content prioritization parameter specified in the particular recommendation configuration, may generate a set of models of one or more model types that model relevance between the set of content and a different event specified in the particular recommendation configuration, and may compute a score for each content in each model based on the modeled relevance. The system may present recommended content in a different order than the set of content based on aggregate scores compiled for each content from the set of models.

Systems and methods for generating scalable personalized recommendations based on dynamic and customized content selections and modeling of the content selections

A method, a device, and a non-transitory storage medium are described, which provide a natural language-based content system with corrective feedback and training service. The natural language-based content system with corrective feedback and training service may collect data based on interaction with search results from users. The natural language understanding model may generate feedback data based on the collected data, and use the feedback data to further train the natural language understanding model and update search and discovery logic for searching and discovering contents. The feedback data may categorize errors based on the interaction, and identify differences between search queries received during a search session with a user.

Natural language-based content system with corrective feedback and training

A method, a device, and a non-transitory storage medium are described, which provide for calculating a first relevance score for each content item of a set of content items, wherein the first relevance scores correspond to a relevance of each content item with respect to a query term according to a term-weighting scheme; calculating, for each content item, a program title relevance score; a media personality relevance score; a media network relevance score; and a live programming event relevance score; ranking each content item based on the program title relevance scores, the media personality relevance scores, the media network relevance scores, and the live event relevance scores; receiving a user input search term; generating, based on the search term, a user interface including multiple graphic icons corresponding to a number of the ranked content items; and presenting, via the user interface, the multiple graphic icons for selection by a user.

Relevance-based search and discovery for media content delivery

A method, a device, and a non-transitory storage medium are described, which provide a natural language-based content search and discovery service. The natural language-based content search and discovery service may use query object types as a basis for interpreting a vocalized search query from a user. The natural language-based content search and discovery service may use a multi-interpretative procedure that includes use of a probabilistic grammar parser, parts of speech, and query object type identification that are configured for a media domain. The natural language-based content search and discovery service may merge different interpretations of the search query based on probability values associated with each interpretation.

Natural language-based search and discovery of content service

A method, a device, and a non-transitory storage medium for determining, based on media content selection activity of a user for a media content inventory, the user's sensitivity to a cost and to a relevance of the media content; assigning a cost value to respective media content items based on user-specific content cost information from media content providers; assigning a relevance value to the respective media content items based on user-specific content relevancy information associated with the respective media content items; ranking the media content items based on: the user's sensitivity to the cost of the media content relative to the cost values for the respective media content items, and the user's sensitivity to the relevance of the media content relative to the relevance values for the respective media content items; and presenting, via a personalized media content recommendation interface, an ordering of the media content items based on the ranking.

Media content recommendation and user interface generation

A method, a device, and a non-transitory storage medium are described in which a personalized content recommendation system determines a content-offering value (COV) for each title of content identified in a content catalog, wherein the COVs indicate terms of offerings to a user for consuming each title of content; calculate, for each title of content, a content-relevance value (CRV), wherein the CRV indicates respective relevancies of each title of content to the user; calculate, for each title of content, a cost-content sensitivity index (CCSI) value indicative of the user's relative cost and content sensitivities, wherein the CCSI value is calculated for a time-of-day parameter or a content-genre parameter for consuming each title of content; calculate, for each title of content, a cost-content tradeoff score (CCTS) based on the COV, CRV, and CCSI value; and identify k number of titles of content having the highest CCTS.

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Irving, TX, United States of America

Praveen Kumar Bandaru