Cryptocurrency Scam Detection via NLP Keyword Extraction

The patent replaces manual mechanical analysis of transaction features with an automated machine learning system. The system uses natural language processing to extract keywords from reports and publicly accessible data, then applies trained machine learning models to automatically detect scam addresses, eliminating the need for manual review while maintaining detection accuracy through algorithmic analysis of transaction patterns and textual information.

The patent introduces an intermediary machine learning system that acts as a bridge between raw transaction data and scam detection outcomes. This intermediary process automatically processes massive volumes of data by extracting relevant features, training models on labeled datasets, and generating detection results, thereby resolving the contradiction between processing speed and accuracy by automating the intermediate analysis steps.

The patent segments the machine learning system into distinct functional modules: a data acquisition unit that collects information from multiple sources (reports, websites, services), a processing unit that extracts keywords and features using natural language processing, and a model training unit that develops detection algorithms. This segmentation reduces overall system complexity by making each component independent and manageable while maintaining high automated processing capability.

The patent creates a universal data acquisition system that can collect information from multiple diverse sources including scam reports, publicly accessible websites, and third-party services. This multi-functional approach consolidates what would otherwise require separate systems into a single unified platform, improving productivity without proportionally increasing complexity through standardized data collection and processing pipelines.

The patent merges multiple data sources including scam reports with descriptive text, publicly accessible websites containing cryptocurrency information, and third-party services providing address tags and reliability scores. By combining these diverse sources into a unified data acquisition system that feeds into a single machine learning model, the patent improves detection reliability through comprehensive data coverage while managing complexity through integrated processing architecture.

The patent creates a composite information structure by integrating data from multiple sources with different characteristics and reliability levels. The system combines structured data (transaction records, tags) with unstructured data (descriptions, reports) and metadata (reliability scores, frequencies) into a composite dataset that enhances detection reliability. This composite approach manages complexity by treating diverse data sources as complementary components of a unified information framework.