Electronic Data Cluster Analysis for Latent Topic Discovery
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Solution Overview
Problem
Existing business processes struggle to identify latent themes or secondary topics in large document corpora, leading to missed trends and potential insights.
Innovation Solution
A method for electronic data cluster analysis involving preprocessing, similarity calculation, clustering, and topic extraction to identify latent themes in textual data, using techniques like lemmatization, tokenization, and Latent Dirichlet Allocation (LDA).
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional manual analysis methods are used to identify topics in documents, then analysis precision may be maintained for small datasets, but productivity deteriorates significantly when handling large document corpora
Solution Approach 1:
The patent replaces manual mechanical analysis with automated electronic data processing. The system uses computer algorithms to perform preprocessing, similarity calculation, clustering, and topic extraction operations that would be impossible to perform manually at scale, thereby resolving the contradiction between productivity and information loss.
Solution Approach 2:
The patent introduces intermediate processing steps including data preprocessing, similarity calculation, and clustering as mediators between the raw document corpus and final topic extraction. These intermediaries transform the data into structured formats that enable efficient automated analysis while preserving latent themes, solving the productivity-information loss contradiction.
2Productivity
If automated clustering algorithms are applied to large document corpora, then productivity is improved through rapid processing, but device complexity increases due to multiple processing steps
Solution Approach 1:
The patent segments the complex analysis task into distinct modular components: preprocessing module, similarity calculation module, clustering module, and topic extraction module. Each module handles a specific aspect of the analysis, making the overall system more manageable and maintainable while enabling high-throughput processing of large document corpora.
3Measurement precision
If comprehensive preprocessing and analysis steps are performed on each document, then measurement precision of topic extraction is improved, but loss of time increases due to extensive processing
Solution Approach 1:
The patent performs preliminary preprocessing actions on documents before the main clustering and topic extraction processes. Steps such as tokenization, lemmatization, and stopword removal are executed in advance to transform raw documents into standardized formats, which improves the accuracy of subsequent analysis while reducing the time required for the main processing pipeline.
Solution Approach 2:
The patent applies partial preprocessing selectively to documents based on their characteristics and the analysis requirements. Not all documents undergo the same level of processing intensity, allowing the system to maintain high topic extraction accuracy while optimizing processing time by avoiding unnecessary exhaustive analysis on all documents.
Data Source
AI summary
A computer-implemented method for electronic data cluster analysis may include receiving a plurality of electronic data files including non-human-readable data, calculating a similarity of each pair of electronic data files among the plurality of electronic data files, determining one or more electronic data file clusters among the plurality of electronic data files based on the calculated similarity of each pair of electronic data files among the plurality of electronic data files, and extracting one or more commonalities in each electronic data file cluster among the determined one or more electronic data file clusters