Content Analysis Workflow for Incremental Multi-Source Categorization
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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 re-examination for new content sources.
Innovation Solution
A hybrid method using recognition samples and heuristic comparisons to identify file clusters, eliminating explicit training steps, and integrating with user workflows to provide immediate and evolving categorization across various content types, including structured, semi-structured, and unstructured documents, with AI-driven suggestions and incremental analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If batch preprocessing is used for document categorization, then comprehensive analysis can be performed, but the system cannot handle rapidly changing content and requires minutes to hours or even days for processing
Solution Approach 1:
The patent segments the batch preprocessing task into incremental updates. Instead of reprocessing all documents periodically, the system processes individual documents or small batches as they arrive, maintaining categorization accuracy while enabling rapid response to new content.
Solution Approach 2:
The system performs preliminary categorization actions immediately when documents are added or modified, rather than waiting for scheduled batch processing. This allows the system to provide timely categorization results for dynamic content while maintaining comprehensive analysis capabilities.
2Measurement precision
If mathematical pairwise comparison is used to form similarity groups, then document relationships can be identified, but the approach becomes costly and ambiguous
Solution Approach 1:
The patent introduces pre-computed document embeddings and feature vectors as intermediaries between raw documents and similarity comparisons. These compressed representations enable efficient pairwise comparisons while preserving the essential semantic information needed for accurate similarity detection.
Solution Approach 2:
The system dynamically adjusts the comparison strategy based on document characteristics and query requirements. For frequently accessed documents, pre-computed embeddings are used for rapid comparison, while less common documents undergo more thorough analysis, optimizing the balance between accuracy and computational cost.
3Productivity
If existing knowledge samples are used for document categorization, then categorization can be performed, but the system is limited to the existing knowledge represented by the samples
Solution Approach 1:
The system incorporates feedback loops where user interactions, corrections, and new document patterns continuously refine the categorization models. This allows the system to expand its knowledge base beyond initial samples while maintaining efficient categorization performance through incremental learning.
Solution Approach 2:
The system automatically expands its knowledge base by analyzing new document patterns and user behavior without requiring manual retraining. It self-adapts to new content types and categories by learning from incoming documents, maintaining efficiency while increasing versatility.
4Stability of the object's composition
If document characterization systems focus on static content, then batch processing works well, but the systems are ill-suited for dynamic, rapidly changing content
Solution Approach 1:
The patent implements dynamic content handling by transitioning from static batch processing to incremental real-time processing. The system continuously monitors and processes content changes as they occur, adapting categorization and characterization to reflect the current state of dynamic content while maintaining stability through consistent processing rules.
Data Source
AI summary
Content analysis systems and methods are described. One aspect includes receiving a user search request associated with any of an email search, an online web search and a chat prompt. The search request may be routed to a search processing engine, and to a content analysis system. In an aspect, the search processing engine performs a first search based on the search request to retrieve a first content set from a first data storage, and the content analysis system performs a second search based on the search request to retrieve a second content set from a second data storage. The first content set and second content set may be merged to generate a merged content set, and this merged content set may be presented to the user.