Semantic Topic Identification via Vector Clustering and Frequency Thresholding

The system performs automatic text preprocessing, clustering, and semantic topic identification without requiring manual user input or review. The computer system serves itself by autonomously processing unstructured textual content through algorithmic operations including tokenization, lemmatization, filtering, and frequency-based topic selection, thereby achieving both high productivity and acceptable accuracy without human intervention

The patent replaces the mechanical process of manual text review with an automated computational system. Instead of human users manually analyzing and categorizing text, the system uses algorithmic operations including data vector generation, clustering algorithms, and frequency analysis to automatically determine semantic topics, substituting human cognitive work with automated information processing

The system uses lightweight, computationally inexpensive processing operations instead of resource-intensive neural networks. The approach employs simple algorithmic operations including tokenization, lemmatization, filtering, data vector generation, and frequency analysis that consume minimal computational resources and energy, providing an efficient alternative to heavy machine learning models

The patent extracts and uses only the essential computational operations needed for semantic topic identification, removing unnecessary complexity. By focusing on core operations like frequency analysis and clustering rather than deploying full neural network architectures, the system achieves text interpretation with significantly reduced computational resource requirements and energy consumption