Verizon Patent and Licensing Inc.

Accenture Global Solutions Limited

Vijay Baskaran

Jubish C Parambath

Anand N Sankaran

Aveek Mukhopadhyay

Krithika Swayambhu

Suman Mukherjee

Anil Kv Kumar

Nitin Madhukar Sawant

Manoharan Ramasamy

Rajesh Pappu

Siddhartha X Gunda

Rakesh Singh Kanyal

Jeevak Balasubramaniam

Surender Subramanian

Ganesh Dadasaheb Waghmale

Abhishek Kumar Pandey

Nilanjan Roy

Balasubramanyam Besta

Muhil Rajamanickam

A device may receive an application for transforming legacy applications into low-code/no-code applications to be managed by a low-code/no-code platform, and may execute the application for a legacy application of the legacy applications. The device may process the legacy application, with a machine learning model, to identify one or more components of the legacy application to be managed by the low-code/no-code platform, and may transform the one or more components into one or more transformed components to be managed by the low-code/no-code platform. The device may implement the one or more transformed components in the legacy application to generate a transformed legacy application, and may perform one or more actions based on the transformed legacy application.

Utilizing a machine learning model to transform a legacy application to a low-code/no-code application

A system for generating an architecture diagram includes an input processor, a machine learning processor, and an advice generator. The input processor is configured to receive, from a terminal, entity data associated with a plurality of entities of an architecture and path data associated with a plurality of paths that correspond to interconnections between the plurality of entities. The machine learning processor utilizes a training dataset to assess whether the entities defined by the entity data are correctly interconnected as defined by the path data. The advice generator receives the assessment from the machine learning processor, prepares a recommendation based on the assessment, and communicates the recommendation to the terminal. User feedback is represented in the training data to improve the relevancy of the recommendation.

System for generating an architecture diagram

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 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 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

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Chennai, India

Vijay Baskaran