Structured CNN for Raw Transactional Data Prediction
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Solution Overview
Problem
Existing data modeling techniques, particularly in big data environments, are not optimized for predicting user behavior and outcomes, leading to inefficiencies in resource allocation and productivity in transactional systems.
Innovation Solution
The use of densely connected neural networks and unified models, combined with structured convolutional neural networks, for predicting customer value and its sub-variables directly from raw transactional data, enhances predictive accuracy and resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing data modeling techniques are used, then resource allocation can be performed, but prediction accuracy is insufficient leading to inefficiencies in transactional systems
Solution Approach 1:
The patent replaces traditional mechanical data modeling techniques with convolutional neural networks (CNNs), a sophisticated computational system inspired by biological vision processing. The CNN automatically learns hierarchical feature representations from raw transactional data, substituting manual feature engineering and traditional statistical methods with an adaptive, self-organizing system that achieves superior prediction accuracy while maintaining operational efficiency
Solution Approach 2:
The patent fundamentally changes the parameter representation by processing raw transactional data through multiple convolutional layers that automatically discover optimal feature transformations. Instead of relying on pre-defined parameters from existing models, the system dynamically adjusts and learns parameters (filters, weights, biases) during training, enabling accurate predictions even for users with limited transaction history
2Productivity
If traditional machine learning techniques are used, then modeling can be performed, but the techniques are not well optimized leading to reduced productivity
Solution Approach 1:
The patent replaces conventional machine learning algorithms with convolutional neural networks, which provide superior optimization capabilities through automatic gradient-based learning. The CNN architecture with its convolutional layers, pooling operations, and fully connected layers creates a more reliable and optimized modeling system that automatically adapts to the underlying data patterns, improving both productivity and model reliability simultaneously
Data Source
AI summary
While artificial neural networks can be used to predict particular values in certain contexts, convolutional neural networks are not typically used in these contexts—instead they may be employed for image recognition. However, raw transactional data may be structured to take advantage of convolutional neural network (CNN) techniques by arranging the data such that correlations are increased between nearby other data. In arranging data in this manner, the structured CNN (SCNN) can operate efficiently without having to make use of engineered data features, the generation and maintenance of which can be a time-consuming process.