Neural Network Image Encoding for Subscriber Behavior Classification
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Solution Overview
Problem
Existing data processing applications struggle with recognizing and classifying complex data sets, particularly in subscriber engagement and behavior analysis, due to limitations in pattern recognition techniques and access to subscriber data.
Innovation Solution
The approach involves encoding user behavior into images using an image recognition pipeline, which includes a trained machine learning model, to classify subscriber behavior and create synthetic images for simulated populations, thereby overcoming data access limitations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional pattern recognition techniques are used for classifying complex data sets, then the system is simpler to implement, but classification accuracy is insufficient
Solution Approach 1:
The patent replaces traditional mechanical pattern recognition techniques with a neural network-based image recognition pipeline. The system encodes complex data sets into image representations and uses trained neural networks to classify patterns, achieving superior classification accuracy while maintaining manageable system complexity through automated feature detection.
2Reliability
If direct observation of subscriber behavior data is used, then classification can be performed, but data access becomes restricted due to regulations and privacy concerns
Solution Approach 1:
The patent creates synthetic population data that copies the statistical properties and behavioral patterns of real subscriber data without using actual personal information. This synthetic data can be freely accessed and processed for classification tasks, eliminating data access restrictions while preserving the reliability needed for meaningful analysis.
3Loss of information
If manual feature detection and analysis is performed on subscriber data, then detailed insights can be obtained, but the process requires extensive data science expertise and time
Solution Approach 1:
The patent implements automated feature detection within the image recognition pipeline, where the neural network automatically identifies and extracts relevant features from encoded data representations. This self-service approach eliminates the need for manual feature engineering by data scientists, reducing processing time while maintaining high-quality feature detection through the network's learned feature hierarchies.
Data Source
AI summary
Electronic information is received. Training images are created from the received electronic information. The training images comprise a format having a plurality of pixels arranged in a matrix of rows and columns, each of the rows indicative of a time and each of the columns representing a feature characteristic, the matrix of rows and columns together forming a visual image. Subsequent to the completion of the training of the neural network, production images are applied. The production images having the same format as the training images to the trained neural network, each production image being from a different customer and pictorially presenting behavior of each different customer with respect to the telecommunication or data service. The application of the production images to the trained neural network resulting in the creation of one or more control signals by the trained neural network.