Neural Network User Classification System for Anonymous Text Analysis
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Solution Overview
Problem
Existing network content monitoring systems fail to effectively classify anonymous users based on their textual data, leading to inadequate protection against inappropriate communications, particularly between children and adults.
Innovation Solution
A method and system that classify users by age and other attributes using neural networks to analyze textual data, employing sets of reference attributes and pre-defined rules to identify and prevent undesirable communications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If traditional keyword filtering is used to block inappropriate content, then simple implementation is achieved, but classification accuracy and ability to understand contextual meaning deteriorates
Solution Approach 1:
The patent replaces mechanical keyword filtering with neural network-based semantic analysis. The system uses trained neural networks to analyze textual data and classify users based on meaning rather than simple keyword matching, significantly improving classification accuracy while maintaining automated operation.
Solution Approach 2:
The patent changes the parameter of analysis from surface-level keywords to deep semantic features. By extracting multiple attributes from textual data and using neural networks to process these features, the system achieves more accurate user classification while adapting to different classification needs through configurable parameters.
2Measurement precision
If neural network analysis is implemented for user classification, then classification accuracy improves, but computational complexity and processing time increases
Solution Approach 1:
The patent segments the classification process into distinct stages: text preprocessing, attribute extraction, neural network classification, and result application. This segmentation allows each component to be optimized independently and enables parallel processing of different textual features, reducing overall computational complexity.
Solution Approach 2:
The patent performs preliminary actions by pre-training neural networks offline and pre-extracting textual attributes before actual classification. This allows the system to use pre-computed models and features during runtime, significantly reducing real-time computational requirements while maintaining high accuracy.
3Reliability
If comprehensive textual analysis is performed to extract multiple attributes, then classification reliability improves, but processing time and computational resources increase
Solution Approach 1:
The patent applies partial action by selectively extracting only the most relevant textual attributes needed for classification. The system can adjust the depth of analysis based on requirements, extracting essential features like spelling patterns, grammatical structures, and vocabulary usage without performing exhaustive analysis of all possible textual characteristics.
Solution Approach 2:
The patent merges multiple attribute extraction processes into a unified neural network framework. By combining spelling error analysis, grammatical error detection, verb usage patterns, and vocabulary analysis into a single integrated system, the patent reduces redundant processing and improves overall efficiency while maintaining comprehensive classification reliability.
Data Source
AI summary
A method of classifying an anonymous user communicating over a computer network, such as by age, gender, or personal interests, based on the content generated or otherwise communicated by the user. The method includes: (a) providing sets of reference attributes, wherein each set of reference attributes is associated with one of a number of personal profile classes; (b) analyzing the user content in order to extract a set of attributes corresponding to such content; (c) comparing the set of extracted attributes against the plural sets of reference attributes in order to find a close match; and (d) associating the user with the personal profile class of the matched set of reference attributes.