Context-Based Content Stitching via Hierarchical Causality
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Solution Overview
Problem
The abundance of content and the lack of effective classification methods make it difficult for consumers to find relevant content, as existing classification systems are laborious, inexact, and do not account for consumer context, leading to inefficient content consumption and measurement of its impact.
Innovation Solution
A computer-implemented method for hierarchical causality-based content stitching, which tracks and measures consumer interactions with existing content, correlates meta-tags to determine relevance, and automatically collates relevant content items to form output content, serving it to consumers based on pre-defined rules and thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple classifiers are employed for content classification, then classification coverage is improved, but system complexity and labor requirements increase significantly
Solution Approach 1:
The patent combines multiple classification approaches (system-driven static classification and consumer-driven dynamic classification) into a unified hybrid system. The content classification module integrates expert-defined taxonomies with consumer-generated tags and ratings, allowing the system to leverage the strengths of both approaches while maintaining manageable complexity through automated processing.
Solution Approach 2:
The system enables consumers to actively participate in content classification by generating their own tags, ratings, and reviews. This self-service classification mechanism allows users to contribute to the organization and categorization of content based on their personal context and preferences, reducing the burden on system administrators while improving classification relevance.
2Stability of the object's composition
If content is classified based on classifier rules, then classification consistency is improved, but context relevance to consumers deteriorates
Solution Approach 1:
The patent implements a dynamic classification system where content tags and categories evolve based on consumer interactions and feedback. The system continuously updates content metadata based on consumer ratings, reviews, and behavioral data, allowing classification to adapt to changing consumer preferences and contexts while maintaining a stable underlying taxonomy structure.
Solution Approach 2:
The system applies different classification strategies to different content types and consumer segments. Expert-defined classifications provide consistent structural organization, while consumer-driven classifications add localized context and relevance specific to individual users or user groups, allowing both consistency and adaptability to coexist at different levels of the classification hierarchy.
3Adaptability or versatility
If consumers search for content manually, then search freedom is improved, but time efficiency deteriorates
Solution Approach 1:
The system pre-processes and organizes content based on multiple classification criteria before consumers arrive. Content is pre-tagged with both expert-defined categories and consumer-generated metadata, and recommendation algorithms pre-compute relevant content based on consumer profiles and historical data, allowing consumers to quickly find relevant content without manual searching.
Solution Approach 2:
The system continuously learns from consumer search behavior, ratings, and interactions to refine content recommendations and classification. Consumer feedback loops allow the system to adapt to individual preferences over time, improving the accuracy and relevance of content delivery while reducing the time consumers need to spend searching for relevant material.
4Measurement precision
If content impact measurement is implemented, then content effectiveness is improved, but system complexity increases
Solution Approach 1:
The system employs a multi-functional measurement framework that tracks multiple impact metrics (engagement, conversion, retention, satisfaction) through a unified analytics platform. This universal measurement system serves multiple purposes including content optimization, consumer profiling, and business intelligence, reducing overall system complexity by consolidating measurement functions rather than implementing separate specialized systems.
Data Source
AI summary
This invention discloses a computer-implemented method, caused by a server, for hierarchical causality-based stitching of content and for serving said stitched content as output content, said method comprising: tracking, and measuring, a first set of markers, for a first content consumer, consuming a first content item; tracking, and measuring, a first set of markers, for a second content consumer, consuming a second content item; receiving, by said first content consumer, a request corresponding to a marker from said first set of markers; computing a “pertinence indicator”; computing a “colliding score”; automatically collating said first content item, correlative to said first user, and a second content item, correlative to said second user, to form at least an output content, if said “pertinence indicator” is within said pre-defined rules of correlation and if said “colliding score” is within said pre-determined threshold; and serving said collated output content.