Neural Network for Multi-Level Hierarchical Demographic Classification
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Solution Overview
Problem
Existing methods and apparatus fail to efficiently classify individuals into multi-level hierarchical demographic categories due to the vast amount of data available, making manual classification infeasible and prior solutions unable to properly address inter-relatedness between classification levels.
Innovation Solution
A neural network with multiple output layers and a loss function that includes contributions from multiple hierarchical output layers is used to perform multi-level hierarchical demographic classification, ensuring accurate and interconnected classification decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual classification methods are used for demographic categorization, then classification accuracy can be maintained through human judgment, but the process becomes infeasible due to the vast amount of data available
Solution Approach 1:
The patent replaces manual mechanical classification processes with an automated neural network system. The neural network processes demographic data through multiple hierarchical levels (e.g., age groups, income brackets, geographic regions) simultaneously, achieving both high classification accuracy and efficiency that manual methods cannot attain due to the volume of data involved.
2Productivity
If prior classification solutions are applied, then processing speed can be improved, but they fail to properly address the inter-relatedness between classification levels
Solution Approach 1:
The patent implements a nested hierarchical structure where classification levels are organized in nested categories (e.g., broad demographic categories containing sub-categories containing further sub-categories). The neural network processes these nested levels simultaneously, ensuring that classifications at each level are consistent with parent and child levels, thereby maintaining reliability while achieving fast processing.
Solution Approach 2:
The patent adds a hierarchical dimension to the classification system, organizing demographic data into multiple nested levels rather than treating all categories as flat and independent. This dimensional structure allows the neural network to process inter-related classifications systematically, maintaining consistency across levels while improving processing efficiency.
Data Source
AI summary
Methods and apparatus to perform multi-level hierarchical demographic classification are disclosed. An example apparatus includes a neural network structured to process inputs at an input layer to form first outputs at a first output layer representing first possible classifications of an individual according to a demographic classification system at a first hierarchical level, and to process the first outputs to form second outputs at a second output layer representing possible combined classifications of the individual corresponding to combinations of the first possible classifications and second possible classifications of the individual according to the classification system at a second different hierarchical level; and a selector to select one of the second outputs, and associate with the individual a respective one of the first possible classifications and a respective one of the second possible classifications corresponding to a respective one of the possible combined classifications represented by the selected second output.