Generative AI Dataset Encoding for Tabular Feature Correlations
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Solution Overview
Problem
Existing machine learning models struggle with accurate prediction on tabular datasets due to lack of well-known representations, data sparsity, mixed feature types, and unknown dataset structure, which traditional manual encoding methods fail to address effectively.
Innovation Solution
Utilizing generative artificial intelligence to encode tabular datasets by generating embeddings and applying weights to pair comparisons of features, enhancing feature representation and correlation, thereby improving the training dataset's scope and comprehensiveness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional manual encoding methods are used, then the process is simple and controllable, but the model accuracy and prediction reliability are insufficient
Solution Approach 1:
The patent introduces an intermediary encoding system that bridges raw tabular data and machine learning models. This intermediary layer automatically transforms raw features into encoded representations using pre-trained models and embedding layers, resolving the contradiction by providing reliable predictions through automated encoding while maintaining manageable system complexity through modular architecture
Solution Approach 2:
The patent replaces manual mechanical encoding processes with automated AI-based encoding systems. Instead of manual feature engineering, the system uses pre-trained language models and neural networks to automatically generate embeddings and encode features, thereby improving prediction accuracy while reducing the mechanical complexity of manual encoding operations
2Adaptability or versatility
If manual feature encoding is performed, then the process is interpretable and controllable, but it cannot effectively handle data sparsity and mixed feature types
Solution Approach 1:
The patent implements a universal encoding system that can handle multiple feature types (categorical, numerical, temporal) through a single integrated architecture. The system uses a unified embedding layer and pre-trained models that can process different feature types automatically, thereby improving adaptability to mixed data while managing complexity through a single multi-functional codebase rather than separate handlers
Solution Approach 2:
The patent dynamically adjusts encoding parameters based on feature types and data characteristics. The system automatically selects appropriate encoding strategies (one-hot encoding, embedding, normalization) based on feature properties, enabling versatile handling of mixed feature types while avoiding manual complexity by using automated parameter selection based on data statistics
3Quantity of substance
If automated AI encoding is applied, then the training dataset scope and comprehensiveness are enhanced, but the computational resources and processing time increase
Solution Approach 1:
The patent performs preliminary encoding actions by pre-training models and generating embeddings before the actual machine learning training process. The system pre-processes and encodes features in advance, creating a comprehensive training dataset with enriched representations, thereby increasing data comprehensiveness while managing computational resources by performing intensive processing beforehand rather than during model training
Solution Approach 2:
The patent applies partial encoding to the most important features and uses pre-trained models that provide excessive pre-computed representations that can be fine-tuned. This allows the system to enhance training data comprehensiveness by encoding all features with high-quality embeddings while managing computational resources by using efficient pre-trained models rather than training everything from scratch
Data Source
AI summary
Operations may include identifying features corresponding to a dataset. An embedding for each feature may be obtained using a pretrained generative artificial intelligence model. Pair comparisons of the embeddings may be generated. An encoded dataset may be generated by applying, to the pair comparisons, weights computed using the pretrained generative artificial intelligence model. The weights may indicate correlation between features in the pair comparisons.