Media Object Association Graph for Content Discovery
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Solution Overview
Problem
Users in the entertainment industry face difficulty in discovering new content and connections between artists and media due to the vastness of the industry and limited exposure to diverse viewing options, leading to a need for a tool that establishes interrelationships among people, works, and tags to manage viewing alternatives and provide intriguing connections.
Innovation Solution
A system and process that generates interrelationships among people, works, and tags using media content data, interacting with users to determine their interests and producing correlations, recommended programs, and alternative media, with a graphical user interface to display and enhance user engagement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If users manually research and discover interconnections in the entertainment industry, then they can find interesting connections between artists and content, but the vastness of the industry makes it difficult and time-consuming to identify movies and TV shows outside their viewing patterns
Solution Approach 1:
The patent introduces an automated system that acts as an intermediary between users and the vast entertainment industry database. The system automatically generates interrelationship graphs, identifies connections between artists and content, and presents relevant information to users, eliminating the need for manual research while maximizing exposure to new content outside viewing patterns
Solution Approach 2:
The patent replaces the mechanical manual research process with an automated computational system. The system uses algorithms to automatically traverse the entertainment industry database, generate interrelationship graphs, and identify connections, substituting human effort with automated information processing to reduce time loss while maintaining comprehensive content discovery
2Ease of operation
If users stick to familiar networks and series, then viewing becomes convenient and predictable, but users limit their exposure to new content and alternative media
Solution Approach 1:
The patent implements dynamic recommendation systems that adapt to user preferences while actively introducing diversity. The system generates interrelationship graphs that balance familiar content with new discoveries, dynamically adjusting recommendations to maintain viewing convenience while expanding exposure to alternative media and content outside established patterns
Solution Approach 2:
The patent adds a new dimension to content discovery by generating multi-dimensional interrelationship graphs that connect artists, content, and contextual information. This dimensional expansion allows the system to present content recommendations from multiple angles, balancing familiar preferences with novel discoveries across different viewing dimensions
3Reliability
If the system generates comprehensive interrelationships among all people, works, and tags, then it provides complete viewing alternatives, but the system complexity and data processing requirements increase significantly
Solution Approach 1:
The patent segments the comprehensive interrelationship generation into manageable components: generating graphs for individual artists, works, and tags separately, then integrating them. This segmentation allows the system to maintain completeness of viewing alternatives while reducing overall system complexity through modular processing of discrete entertainment industry entities
Solution Approach 2:
The patent creates a universal interrelationship graph generation system that handles multiple types of entertainment industry entities (artists, works, tags) using the same computational framework. This multi-functional approach provides complete viewing alternatives across different entity types while avoiding the complexity of separate specialized systems for each entity type
Data Source
AI summary
A system for correlating a user's interests to media content, said system comprising: (a) at least one data store comprising media content data relating to people, works, and tags; (b) a relationship generator configured to generate direct relationships among said people, works and tags; (c) a connection module to generate connections between a primary person, work or tag and a first set of said people, works, and tags, wherein each person, work and tag of said first set has a direct relationship with said primary person, work or tag; and (d) a display module for causing the display of at least a portion of said first set of said people, works and tags.