Video Classification System Using Multi-Dimensional Affinity Rankings
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Solution Overview
Problem
Users face inefficiencies in finding specific content, such as videos, within large data stores due to unstructured search interfaces, leading to tedious navigation through numerous search results without adequate refinement options.
Innovation Solution
Implementing a user interface that classifies and organizes content based on user reviews and feedback, using machine-learning classifiers to assign labels and categorize videos by mood, theme, and genre, allowing for personalized and refined search results by displaying videos with multiple classifications based on their affinity rankings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If search interfaces provide unstructured browsing through large data stores, then users can access a wide variety of content, but users must sift through a large and unmanageable number of search results
Solution Approach 1:
The patent segments the unstructured search results into structured categories and classifications. Video content is divided into multiple classification categories (e.g., genre, mood, theme) with hierarchical structures, allowing users to navigate through organized groups rather than a flat, unmanageable list of results.
Solution Approach 2:
The system changes the parameter of content organization from unstructured to structured by applying classification schemes. Content items are assigned multiple classification values across different categories, transforming the search space into a multi-dimensional classified structure that improves navigability while preserving content accessibility.
2Measurement precision
If multiple classification categories are assigned to content items, then search accuracy and user preferences are better matched, but the system complexity increases
Solution Approach 1:
The classification system serves multiple functions simultaneously: it organizes content, enables precise searching, supports personalized recommendations, and provides navigational structure. A single classification framework handles diverse content types and user needs, reducing the need for separate specialized systems.
Solution Approach 2:
The system automatically assigns classification categories to content items using machine learning models, eliminating the need for manual classification of each item. The classification process is self-executing, analyzing content metadata and characteristics to assign appropriate categories without human intervention.
3Productivity
If content is organized with hierarchical classification structures, then navigation becomes more efficient, but the interface complexity increases
Solution Approach 1:
The classification system implements nested hierarchical structures where categories contain subcategories, and items can belong to multiple nested levels simultaneously. This nested organization allows users to drill down from broad categories to specific content while maintaining a manageable interface through progressive disclosure.
Solution Approach 2:
The system adds multiple classification dimensions (genre, mood, theme, etc.) rather than relying on a single hierarchical level. This multi-dimensional classification allows users to navigate content through different axes simultaneously, improving navigation efficiency by providing multiple pathways to discover content without increasing vertical interface complexity.
Data Source
AI summary
The present technology may identify item category affinities by identifying a plurality of classifications of an item. An accuracy of the plurality of classifications relative to one another for the item may be identified. A category affinity of the item may be determined based on the accuracy of the plurality of classifications relative to one another.