Hybrid Content Categorization for Real-Time Multi-Source File Clustering
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Solution Overview
Problem
Existing document categorization systems are limited by static batch preprocessing, costly, and siloed user experiences, failing to provide rapid, comprehensive categorization and characterization of diverse content types, including web pages and chat sessions, and require manual training and reprocessing with new content sources.
Innovation Solution
A hybrid method using recognition samples and heuristic comparisons to identify file clusters, eliminating explicit training steps and providing immediate results, with AI-driven categorization and integrated user interfaces across multiple platforms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If static batch preprocessing is used for document categorization, then processing completeness is improved, but processing time increases significantly (minutes to hours or days)
Solution Approach 1:
The patent segments the batch preprocessing task into incremental updates. Instead of reprocessing all documents whenever new content is added, the system processes only new or changed documents in near-real-time, while maintaining categorization results from previous batches. This divides the large-scale processing problem into smaller, manageable increments that can be handled quickly.
Solution Approach 2:
The system performs preliminary categorization actions during idle periods or as background processes. By pre-processing documents incrementally as they are added or modified, the system prepares categorization results in advance so that when users query the system, the data is already partially or fully processed, eliminating the need for long wait times.
2Measurement precision
If mathematical pairwise comparison is used for document categorization, then categorization accuracy is improved, but computational cost increases significantly
Solution Approach 1:
The patent introduces recognition samples as intermediary references. Instead of directly comparing every document against every other document in the collection, the system compares documents against a curated set of recognition samples that represent different categories. This intermediary approach maintains high categorization accuracy while dramatically reducing the computational complexity from O(n²) to O(n×m) where m is the number of recognition samples.
3Device complexity
If exclusive focus on traditional documents is maintained, then system simplicity is preserved, but content coverage is limited
Solution Approach 1:
The patent implements a universal content analysis architecture that can handle multiple content types (traditional documents, web pages, chat sessions, emails) through the same recognition sample comparison mechanism. The system uses content-agnostic processing steps that work across different formats, with recognition samples that can represent various content types. This allows the system to expand its content coverage without proportionally increasing system complexity.
4Device complexity
If siloed document characterization systems are used, then system complexity is reduced, but user experience is degraded due to workflow interruptions
Solution Approach 1:
The patent merges the document characterization system with the user's existing workflow tools (email clients, web browsers, chat applications). Instead of requiring users to switch to a separate document analysis tool, the system integrates categorization and characterization results directly into the interfaces where users already work. This combination eliminates workflow interruptions while maintaining relatively simple system architecture by leveraging existing UI frameworks.
Data Source
AI summary
Content analysis systems and methods are disclosed. One aspect includes a content analysis system configured to detect a user introducing new content to existing content via a new content source, and analyze one or more file names and message subjects associated with the new content. The content analysis system may generate an initial categorization and characterization of the new content based on the analyzing, and further generate a refined categorization and characterization of the new content using a hybrid analysis based on the initial categorization and characterization. In an aspect, the content analysis system performs a clustering analysis to generate one or more clusters that further refine the refined categorization and characterization of the new content in conjunction with the existing content, and presents results of the further-refined categorization and characterization to the user, including new suggested categories and tags associated with the new content.