Exchangeable Asset Market Analysis to Reduce Crypto False Positives
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Solution Overview
Problem
Traders and investors face difficulties in interpreting vast amounts of blockchain data for crypto markets, leading to high false positives and contradictions in market trend analysis.
Innovation Solution
A system utilizing an AI-based processing engine processes detected events associated with crypto asset transactions, applying predefined categories and signal classification rules to generate an evaluation score indicating market value changes, providing a summary of analysis including scores for various factors and confidence levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If blockchain data is processed using traditional analysis methods, then the analysis can be performed, but the false positives and contradictions in interpreting market atmosphere and owner behavior are high
Solution Approach 1:
The patent replaces traditional mechanical analysis methods with an AI-based processing engine that uses machine learning models to analyze blockchain data. The AI system processes events through multiple layers including event detection, categorization, and evaluation scoring, generating market sentiment predictions that are more accurate and reliable than conventional analysis approaches.
2Ease of operation
If on-chain indicators are implemented to simplify blockchain data analysis, then the analysis process is simplified, but false positives and contradictions in interpreting market atmosphere remain high
Solution Approach 1:
The patent segments the complex blockchain data analysis process into distinct functional modules: event detection module, event categorization module, and evaluation scoring module. Each module handles specific aspects of analysis, with predefined categories for different event types and seasoned factors that are independently evaluated and combined to produce the final market sentiment assessment.
Solution Approach 2:
The patent introduces an AI-based processing engine as an intermediary between raw blockchain data and market interpretation. This intermediary layer processes raw events through multiple analysis layers, applying predefined categories and signal classification rules to transform complex data into actionable market sentiment predictions with reduced false positives.
3Reliability
If AI-based processing is applied to analyze blockchain events, then the accuracy and predictive capability improve, but the system complexity increases
Solution Approach 1:
The AI-based system is segmented into distinct functional modules: event detection module for identifying transactions, event categorization module for classifying events into predefined categories, and evaluation scoring module for generating market sentiment predictions. This modular architecture manages system complexity while maintaining high predictive accuracy through specialized processing at each stage.
Data Source
AI summary
A system and a method for analysing a market of exchangeable assets. The system comprises a detecting module arranged to detect an event associated with generation and/or transaction of an exchangeable asset; and a processing module arranged to process the detected event to obtain an evaluation score of the market indicating a likelihood of a change of market value of the exchangeable assets; wherein the evaluation score is obtained with reference to a database of a plurality of history events categorized by a plurality of predefined categories and a plurality of seasoned factors according to a plurality of signal classification rules, and wherein the plurality of history events are assigned with the evaluation score.