Recurrent Categorization Engine for Electronic Document Retrieval
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Solution Overview
Problem
Modern document and content management systems face limitations in human search efficiency, particularly when dealing with large numbers of electronic documents, as users often struggle to review thousands of search results, leading to ineffective document retrieval.
Innovation Solution
A method and system utilizing recurrent categorization to group electronic documents into a limited number of balanced categories and sub-categories based on rule-based categorization, dynamically generating user prompts to facilitate more precise searching, and allowing further categorization until a manageable number of documents is reached.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If a search engine returns thousands of search results, then the storage and indexing functionality is good, but the user cannot review all results effectively
Solution Approach 1:
The patent segments the large set of search results into multiple categorized groups based on metadata attributes (e.g., document type, date, author, department). Instead of presenting thousands of individual documents, the system groups them into manageable categories with representative samples, allowing users to navigate through organized sections rather than overwhelming lists.
Solution Approach 2:
The patent introduces an intermediary categorization layer between the search engine and the user. This intermediary process analyzes document metadata, applies categorization rules, and presents grouped results with summaries or representative documents. Users interact with this intermediary representation rather than directly with the raw search results, improving review efficiency.
2Loss of information
If the search engine provides detailed search results, then the information completeness is good, but the time required to review results increases
Solution Approach 1:
The patent extracts key metadata attributes from documents (such as document type, creation date, author, department, keywords) and uses these extracted features for categorization. Instead of requiring users to review entire documents or detailed abstracts, the system presents categorized groups based on these extracted attributes, maintaining information completeness while reducing review time.
Solution Approach 2:
The patent performs preliminary categorization and organization of search results before presenting them to users. By pre-grouping documents into categories based on metadata analysis, the system prepares the information in an easily navigable format in advance, eliminating the need for users to spend time manually sorting or filtering results during review.
3Measurement precision
If the system groups documents into many categories, then the categorization precision is good, but the complexity of the interface increases
Solution Approach 1:
The patent applies local quality by allowing different numbers and types of categories at different levels of the interface. The top-level view presents a manageable number of broad categories, while deeper levels or specific sections can have more granular categorization. This localized approach to categorization density maintains precision where needed while keeping the overall interface simple.
Solution Approach 2:
The patent implements dynamic categorization where the number and structure of displayed categories can change based on user interactions, search context, and document distribution. The system can dynamically adjust the level of categorization detail shown, collapsing or expanding categories as users navigate, thus maintaining interface simplicity while providing precise categorization when needed.
Data Source
AI summary
A content management system categorizes electronic documents stored in a document storage database. The electronic documents include metadata. The content management system includes a categorization rules database of categorization rules. A recurrent categorization engine takes the rules and applies them to the electronic documents to generate a plurality of category groupings. Each category grouping includes groups based on the applicable rule used to create the grouping. The electronic documents are grouped between the groups. The most balanced dataset of groups is selected to generate a user prompt for further categorization of the electronic documents.