Machine Learning Innovation Prediction System

The patent replaces traditional statistical methods with machine learning techniques including neural networks, support vector machines, and random forests. This substitution enables the system to effectively process large, noisy, and complex patent data while improving prediction accuracy for future innovations, resolving the contradiction between implementation simplicity and prediction reliability.

The patent segments the complex machine learning system into distinct functional modules: data collection module, data preprocessing module, feature extraction module, model training module, and prediction module. This segmentation manages system complexity by organizing multiple machine learning algorithms and processing steps into manageable, modular components while maintaining high prediction accuracy.

The patent extracts only the most relevant features from large volumes of patent data and big data, including patent citation networks, inventor collaboration patterns, and technological field classifications. This feature extraction process reduces the data volume that needs to be processed while maintaining or enhancing prediction capability by focusing on the most informative indicators of future innovation.

The patent implements a universal data processing framework that handles multiple data sources (patent data, financial data, scientific publications) and various feature types (quantitative metrics, textual information, network structures) through standardized preprocessing and feature extraction procedures. This multi-functional approach enables comprehensive prediction modeling while managing data processing difficulty through systematic, reusable processing pipelines.