Content Identification via High-Order Conjunction Clustering
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Solution Overview
Problem
Conventional search systems cannot identify and classify content, such as images, without manual association of descriptive text, limiting their ability to retrieve content based on search queries.
Innovation Solution
A method is developed to generate a high-order conjunction that predicts labels associated with content, allowing for automatic tagging and classification of content, enabling search queries to be satisfied by descriptive labels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If conventional search systems rely on manual association of descriptive text with content, then content can be identified when descriptive text is present, but content cannot be automatically identified or classified without manual entry of descriptive text
Solution Approach 1:
The system enables content to identify and classify itself automatically through machine learning algorithms that analyze content features and generate descriptive labels without human intervention. The classifier processes content independently, performing the identification task that would otherwise require manual descriptive text entry.
Solution Approach 2:
The system performs preliminary classification of content by generating descriptive labels in advance, before any search query is executed. This pre-computation of content metadata allows for rapid retrieval during search operations without requiring manual text association at the time of content ingestion.
2Adaptability or versatility
If conventional search systems use simple text matching, then search queries can be processed quickly, but the systems cannot identify content based on visual or structural features
Solution Approach 1:
The content classification system is segmented into distinct functional modules: feature extraction components that analyze different aspects of content (visual, textual, structural), clustering algorithms that group similar features, and label generation components that produce descriptive tags. This modular architecture manages complexity while enabling versatile content identification across multiple content types.
Solution Approach 2:
The system introduces an intermediary classifier layer between raw content and search queries. This classifier acts as a mediator that transforms diverse content types into standardized descriptive labels, enabling the search system to handle complex content identification tasks without requiring direct complex analysis for each search operation.
3Productivity
If manual descriptive text is associated with content, then content can be searched using that text, but new content cannot be automatically tagged without user intervention
Solution Approach 1:
The content tagging system operates autonomously by automatically analyzing new content, extracting relevant features, and generating appropriate descriptive labels without requiring user intervention. The machine learning classifier performs the entire tagging process self-service style, dramatically improving productivity while maintaining ease of operation.
Solution Approach 2:
The system incorporates feedback mechanisms where classification results are continuously refined based on performance metrics and user interactions. This feedback loop enables the system to improve tagging accuracy over time while maintaining automatic operation, balancing productivity gains with operational simplicity.
Data Source
AI summary
Systems, computer program products, and methods can identify a training set of content, and generate one or more clusters from the training set of content, where each of the one or more clusters represent similar features of the training set of content. The one or more clusters can be used to generate a classifier. New content is identified and the classifier is used to associate at least one label with the new content.