Document Topic Color Coding for Faster Corpus Navigation
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Solution Overview
Problem
Navigating and identifying relevant documents within large corporate document collections is challenging, leading to inefficiencies and increased operational costs due to time-consuming searches and reviews.
Innovation Solution
A document analysis and processing (DAP) system that generates document color codes, providing a visual representation of topical information, enabling improved document comparison and similarity clustering through trained AI models for topic classification and image similarity search.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users manually search and review documents in a large corpus, then they can identify topics of interest, but it consumes inordinate amounts of time and increases operational costs
Solution Approach 1:
The patent replaces manual mechanical document review with an automated image-based analysis system. The system converts document pages to images, applies deep learning models for topic classification, and generates visual topic representations. This substitution of manual inspection with automated image processing and AI analysis dramatically reduces search time while maintaining or improving topic identification accuracy.
Solution Approach 2:
The patent employs color-coded visual representations to indicate different topics within documents. Each topic category is represented by specific colors, allowing users to quickly identify topic distributions and relevant sections at a glance. This visual encoding transforms abstract topic data into intuitive color-based signals, enabling rapid document scanning and topic identification without reading entire documents.
2Loss of information
If users manually review documents to find similar or related documents, then they can identify relevant content, but it leads to inefficiencies and additional operational costs
Solution Approach 1:
The patent replaces manual document comparison with automated image-based similarity analysis. The system processes documents as images, extracts visual and textual features, and uses machine learning models to compute similarity metrics. This automated approach efficiently identifies similar and related documents across large corpora, preventing information loss while dramatically improving processing productivity compared to manual review methods.
3Quantity of substance
If a company manages an enormous number of documents, then it can maintain comprehensive records, but it becomes difficult to navigate and identify topics of interest
Solution Approach 1:
The patent implements color-coded visual summaries that represent topic distributions across documents and document collections. Each color corresponds to a specific topic category, allowing users to quickly assess document content and navigate to relevant sections. This visual encoding system makes it easy to identify topics of interest even within enormous document corpora, transforming unmanageable text volumes into intuitive visual overviews.
Solution Approach 2:
The patent creates visual copies or representations of document content in the form of topic distribution images and color-coded summaries. Instead of requiring users to examine actual document text, the system generates condensed visual representations that capture essential topic information. These visual copies enable rapid navigation and topic identification across large document collections without handling the full text volume.
Data Source
AI summary
A document analysis and processing (DAP) system is disclosed that includes at least one memory configured to store a corpus of documents and a topic classifier having a first trained artificial intelligence (AI) model and at least one processor configured to execute stored instructions to perform actions. The actions include, for each document of the corpus of documents: using the first trained AI model of the topic classifier to identify topics of each page of the document; mapping each of the identified topics of each page of the document to respective topic colors; combining the respective topic colors of each page of the document to yield a respective page color code for each page of the document; and combining the respective page color code of each page of the document to yield a respective document color code of the document.