Topic Clusters for Unstructured Text Document Organization

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Solution Overview

Problem

Conventional systems for organizing and analyzing electronic text documents are time-consuming, prone to errors, and fail to recognize context, leading to unreliable keyword searches and missed significant topics due to reliance on administrator-defined keywords and predefined topics.

Innovation Solution

A content management system that automatically generates topic clusters by analyzing electronic text documents to identify statistically significant terms and related terms, allowing for the organization and presentation of documents based on contextual relevance, with user input options for customization.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If conventional keyword search systems are used to organize text documents, then the system is simple to operate, but the system fails to recognize context and produces unreliable search results

Engineering Contradiction:

Improvesearch result reliabilityVSAvoidsystem complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent introduces topic clusters as an intermediary layer between keywords and documents. Instead of directly searching documents with keywords, the system first identifies topic clusters that represent contextual groupings of related terms, then uses these clusters to organize and search documents. This intermediary structure enables context-aware searching while maintaining system manageability.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system performs preliminary analysis to automatically generate topic clusters and organize documents into these clusters before the actual search operation. By pre-processing the documents and creating contextual groupings in advance, the system eliminates the need for complex real-time context analysis during searching, thus improving reliability without proportionally increasing operational complexity.

Inventive Principle:

Principle #10Preliminary action

2Loss of information

If administrators manually identify and search for topics in text documents, then the search process is simple, but significant topics are often missed and information is lost

Engineering Contradiction:

Improveinformation lossVSAvoidautomation level

Core Design Contradiction:

Loss of informationVSExtent of automation

Solution Approach 1:

The system performs self-service by automatically analyzing text documents, identifying significant topics, and generating topic clusters without requiring administrator intervention. The automated topic identification and clustering processes enable the system to discover and organize information that would otherwise be missed by manual searching, significantly reducing information loss while operating with minimal human input.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system incorporates feedback mechanisms where topic clusters are continuously refined based on analysis of document contents and user interactions. This feedback loop enables the automated system to improve its topic identification accuracy over time, ensuring comprehensive information recovery while maintaining high automation levels.

Inventive Principle:

Principle #23Feedback

3Loss of information

If automated topic identification systems are implemented, then information completeness improves, but system complexity and error-proneness increase

Engineering Contradiction:

Improvetopic identification completenessVSAvoidsystem error rate

Core Design Contradiction:

Loss of informationVSReliability

Solution Approach 1:

The patent segments the complex task of topic identification into multiple distinct components: term frequency analysis, significance calculation, cluster formation, and document organization. By dividing the automated analysis process into these manageable segments, the system achieves comprehensive topic identification while reducing error rates through modular processing and validation at each stage.

Inventive Principle:

Principle #1Segmentation

4Productivity

If conventional tagging and categorizing methods are used, then the organization process is straightforward, but the process is time-consuming and prone to errors

Engineering Contradiction:

Improvedocument organization speedVSAvoidorganization accuracy

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The patent replaces manual mechanical tagging and categorizing processes with automated computational analysis. The system uses algorithmic topic cluster generation and statistical significance calculation to automatically organize documents, dramatically increasing organization speed while maintaining high accuracy through consistent application of analytical criteria without human error.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS11645317B2Recommending topic clusters for unstructured text documents

Publication Date: 2023.05.09 QUALTRICS LLC

AI summary

Embodiments of the present disclosure generally relate to a content management system that automatically determines and generates topic clusters from a collection of electronic text documents. For example, the content management system analyzes a collection of electronic text documents to identify key terms and terms related to the key terms. Based on the key terms and related terms, the content management system generates a topic cluster that includes the key term and related terms. The content management system then organizes the electronic text documents based on terms within a given text document matching terms within a given topic cluster. Further, the content management system presents the topic clusters and organized electronic text documents to a user.