Attribute-Based Content Menu for Mood-Driven AV Discovery

The patent transitions from traditional keyword-based linear searching to a multi-dimensional content space where content is positioned based on multiple attributes simultaneously (genre, mood, era, etc.). This dimensional transformation enables users to explore content by navigating through attribute-based dimensions rather than relying solely on keyword matching, thus facilitating mood-based and heuristic content discovery.

The system changes the parameters used for content identification from simple keywords to multiple continuous attributes (genre ratings, mood scores, temporal era, etc.). By representing content in terms of these adjustable parameters and allowing users to filter and explore based on parameter ranges, the system enables flexible content exploration and similarity-based discovery without requiring specific keyword knowledge.

The patent introduces additional dimensions beyond traditional channel and time-of-day organization. Content is positioned in a multi-dimensional space defined by attributes such as genre, mood, era, and other characteristics. This dimensional expansion allows users to explore content by navigating through these new attribute dimensions while the system manages the complexity through computational positioning algorithms.

The patent introduces an intermediary computational layer that automatically positions content items in the multi-dimensional attribute space based on their characteristics. This intermediary system handles the complexity of multi-dimensional organization, translating content attributes into spatial positions without requiring users to directly manage or understand the complex menu structure, thus enabling attribute-based exploration while shielding users from structural complexity.

The system transforms content identification from discrete keyword matching to continuous parameter-based positioning. Content is represented by multiple continuous attributes (genre ratings, mood scores, etc.), allowing users to explore content by navigating through parameter spaces. This enables both precise matching (when users know what they want) and easy discovery (when users are exploring), as the same parameter-based system supports both exact and approximate queries.

By adding multiple attribute dimensions beyond simple keyword matching, the system enables users to precisely locate content by combining multiple attribute constraints while simultaneously facilitating easy exploration by allowing navigation through individual dimensions. The multi-dimensional space provides both precision (through combined attribute filtering) and ease of discovery (through dimensional navigation).