Verizon Patent and Licensing Inc.

Anand N Sankaran

Jubish C Parambath

Lisa A Caputo

Nicky Joshi

Venket Kandanala

Rajesh Narayanan

Mousumi Dasgupta

Karthik Srinivasan

Anil Krishna Guntupalli

Vijay Baskaran

Sudharsan Selvakumar

Nishanth Madichetti

Krithika Swayambhu

Govinda R Yenda

Aveek Mukhopadhyay

Suman Mukherjee

Siddhartha X Gunda

Amitkumar Singh

Mareeswari Muthusamy

Jagadish B Nagam

TamilKumaran Palaniappan

Rajesh Narayan

Sumithra Jayaraman

VijayPrabhu Baskaran

Anierutha X Chandhiramowuli

SyedTalat Iqbal

Varughese P Mathew

Pankul X Mahajan

Paranjothi X Ramalingam

Sundarajan Varadharajan

Nilanjan Roy

Srimathi Saravanan

Muhil Rajamanickam

A method including identifying a customer when the customer uses a television service; storing contents that are each tagged with a Myers-Briggs Type Indicator or one or more personality characteristics pertaining to a Myers-Briggs Type Indicator; selecting contents from the stored contents; providing a user interface to permit the customer to choose from a selection of the stored contents to rate; receiving ratings of a chosen contents from the customer; identifying the customer's Myers-Briggs Type Indicator based on the received ratings; and providing the customer's Myers-Briggs Type Indicator to the customer.

Content personality classifier

A method, performed by a computer device, may include obtaining a viewing history, associated with a customer, from a set-top box associated with the customer, wherein the viewing history relates a particular television channel to a duration time associated with the particular television channel. The method may further include determining a keyword, associated with the customer, based on the obtained viewing history; calculating a keyword score for the keyword based on the obtained viewing history; selecting an advertisement, associated with the keyword, based on the calculated keyword score; and providing the selected advertisement to the set-top box associated with the customer, based on selecting the advertisement.

Behavioral keyword identification based on thematic channel viewing

A method may include obtaining customer information regarding the user and marketing rules regarding a plurality of store offerings. A number of store offerings from the plurality of store offerings may be identified based on the customer information and the marketing rules. One or more of the identified store offerings may be provided to the user. A user request to purchase a selected one of the provided store offerings may be received. The content or services associated with the selected store offering may be provisioned based on the user request.

Multi-service store

A method and system that provides a television service; receives a customer input to access a graphical user interface that includes a main menu pertaining to the television service; retrieves the graphical user interface; and displays the graphical user interface via the customer device, wherein the graphical user interface includes a main menu body and an interactive masthead having an interactive element that permits a customer to access content via the interactive element.

Interactive masthead

A method may include receiving a user request to view a channel to which the user does not currently subscribe. Customer information regarding the user and marketing rules regarding a plurality of combination offers that include the channel may be obtained. A number of combination offers from the plurality of combination offers may be determined based on the customer information and the marketing rules. One or more of the determined combination offers may be provided to the user. A user request to purchase a selected one of the provided combination offers may be received. The channels included within the selected combination offer may be provisioned based on the user request.

Intelligent offer generation

A method and system that provides customers a pay-per-view portal, accessible via a television service, a mobile service, or an Internet service. The pay-per-view portal permits customers to search for pay-per-view programs and provide search results that include related content to pay-per-view programs including related videos, articles, biographical summaries, stats on players or fighters, and images. The pay-per-view portal provides personalized pay-per-view program recommendations, pay-per-view calendars, and sub-portals directed to categories or sub-categories of pay-per-view programs. The pay-per-view portal also provides the ordering and purchasing of pay-per-view programs days, weeks, or months in advance of the airing of the pay-per-view programs.

Pay-per-view portal

There is an approach for generating a pattern-based database. The approach includes accessing a log specifying one or more strings representing data having a dynamic portion and a static portion. The approach also may include generating a pattern-based database, including one or more records representing compression of the data, by determining the dynamic portions and the static portions of the strings. The approach also may include assigning pattern values to the strings based on the determined dynamic portions and the static portions. The pattern values may be configured to provide compression of the strings within the records of the pattern-based database.

Method and system for pattern-based compression

A method storing information that indicates similarity between contents based on content metadata associated with each content, wherein a degree of similarity between contents is based on a semantic interpretation of plot metadata included in content metadata and pertaining to each plot associated with each content; receiving a search query from a user; retrieving one or more content metadata pertaining to one or more contents based on the search query; retrieving one or more recommended content metadata pertaining to one or more recommended contents that are similar to the one or more content metadata pertaining to the one or more contents based on the stored information; and providing a search result that includes the one or more content metadata and the one or more recommended content metadata via a user interface.

Semantic-based search engine for content

A method including providing at least one of a phone service, an Internet access service, or a television service to customers; identifying a customer when the customer uses the at least one of the phone service, the Internet access service, or the television service; selecting products or services for which the customer qualifies based on the identifying; gathering customer-specific data for the customer; generating recommendations that pertain to the at least one of the phone service, the Internet access service, or the television service for the customer based on the products or the services for which the customer qualifies, the customer-specific data, and other factors, wherein the other factors include at least one of a location of the customer or a time in which the recommendations is to be provided to the customer; and providing the recommendations to the customer via the at least service.

Artificial intelligence-based recommender and self-provisioner

A method including providing a television service to customers; identifying a customer when the customer uses the television service; tracking content viewed by the customer; calculating probabilities based on the customer's viewing of contents, wherein the probabilities pertain to features applicable to the viewed contents and the features include genres of the viewed contents; selecting one or more upcoming contents, available at a future date to the customer, to recommend to the customer based on one or more features applicable to the upcoming content that correspond to one or more features applicable to the viewed contents; and providing a user interface to permit the customer to select from the one or more upcoming contents recommended to the customer.

Probabilistic-based content recommender

Growing community of inventors

MGR Nagar, India

Anand N Sankaran