Digital Video Content Mapping Using Cognitive Analysis
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Solution Overview
Problem
Users face inefficiencies in selecting relevant digital video content due to the lack of personalized recommendations, often spending time watching irrelevant content as existing methods do not account for individual viewing preferences.
Innovation Solution
A system and method for digital video content mapping using cognitive analysis to identify and tag content tokens, creating a relationship map that indicates the strength of similarity between digital video content instances, allowing for personalized content suggestions based on user preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If arbitrary content suggestion methods are used, then content delivery speed is improved, but content relevance to user preferences deteriorates
Solution Approach 1:
The system performs preliminary cognitive analysis of digital video content to extract meaningful tokens and characteristics before content is requested by users. This pre-processing creates a relationship map that enables rapid retrieval of relevant content while maintaining high relevance to user preferences, resolving the contradiction between speed and reliability.
Solution Approach 2:
The patent replaces arbitrary or manual content selection methods with cognitive analysis technology that automatically understands and processes digital video content. This substitution enables the system to quickly analyze content semantics and match it with user preferences, achieving both fast content delivery and high relevance simultaneously.
2Ease of operation
If trial and error content selection is used, then user autonomy is improved, but time consumption deteriorates
Solution Approach 1:
The system implements feedback mechanisms that learn from user viewing behavior and preferences. By analyzing what content users watch and how they interact with it, the system refines its recommendations over time, allowing users to quickly find relevant content without trial and error while maintaining user autonomy through personalized suggestions.
Solution Approach 2:
The cognitive analysis system automatically performs content understanding, token extraction, and recommendation generation without requiring user intervention. This self-service approach eliminates the need for users to manually search or evaluate content, significantly reducing time consumption while preserving user choice through personalized recommendations.
3Measurement precision
If cognitive analysis with relationship mapping is implemented, then content recommendation accuracy is improved, but system complexity deteriorates
Solution Approach 1:
The patent segments the complex cognitive analysis process into distinct modules: content parsing, token definition, relationship mapping, and recommendation generation. This segmentation allows each component to be optimized independently and managed separately, reducing overall system complexity while maintaining high recommendation accuracy through specialized processing at each stage.
Solution Approach 2:
The system introduces content tokens as intermediary representations that simplify the mapping between digital video content and user preferences. These tokens act as a bridge that translates complex content characteristics into manageable data structures, enabling accurate recommendations without requiring the entire system to handle full content complexity simultaneously.
Data Source
AI summary
A method and system for digital video content mapping includes receiving and parsing digital video content. Content tokens are defined for tagging content in the digital video content. A cognitive analysis is used to identify content tokens and tag content tokens in the digital video content. A relationship map is created between related tagged content tokens between instances of digital video content using a cognitive analysis. The relationship map indicates the strength of a relationship between the tagged plurality of content tokens and thereby the plurality of instances of digital video content.