Neural Network User Profile Classification System
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Solution Overview
Problem
Current methods for detecting suspicious or at-risk user profiles in data networks are inefficient and fail to identify potential harm early on, particularly in complex networks with diverse interactions, relying on psychological studies and declarative properties rather than analyzing digital interactions effectively.
Innovation Solution
A computer-implemented method using neural networks to classify user profiles based on characteristic vectors derived from digital content interactions, including age, gender, and semantic analysis, generating a list of profiles with associated probabilities of harmful behavior by analyzing data exchanges and temporal interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If psychological or psychic study methods are used to detect suspicious behaviors, then classification can be performed, but detection is delayed until harm has occurred and effectiveness is low
Solution Approach 1:
The system performs preliminary classification of user profiles by analyzing digital interactions before harmful actions occur. The neural network continuously monitors and classifies user behaviors based on digital footprints, enabling early detection of at-risk profiles before they cause harm to others.
2Adaptability or versatility
If declarative properties (age, gender, center of interest) are used for classification, then individuals can be categorized, but the classification is trivial and does not capture actual digital behavior patterns
Solution Approach 1:
The patent replaces traditional declarative classification methods with a neural network-based system that automatically analyzes digital interactions. The system substitutes manual or rule-based classification with automated machine learning that processes digital footprints, message patterns, and interaction behaviors to generate accurate user profiles.
3Reliability
If automated neural network classification is implemented, then early detection of at-risk profiles is enabled, but system complexity increases
Solution Approach 1:
The system performs self-service by automatically collecting, processing, and classifying digital interactions without requiring manual intervention. The neural network autonomously analyzes digital footprints, extracts features, and updates user profiles based on observed behaviors, reducing the need for complex manual configuration and maintenance.
4Measurement precision
If analysis of digital interactions is performed, then intrinsic user behavior can be captured, but processing complexity and data volume increase
Solution Approach 1:
The system extracts relevant features from digital interactions by using the neural network to identify and isolate meaningful patterns from the vast amount of data. The system extracts characteristic vectors from digital footprints, message contents, and interaction patterns, separating useful information from noise to reduce processing complexity while maintaining analysis accuracy.
Data Source
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AI summary
System comprising: ▪ an electronic terminal (T1) including means of communication, said means of communication enabling it to receive data from a plurality of data resources and being configured to: ▪ select a set of content exchanged (Dk) with a first user (U1) of the terminal (T1), said content being associated with the first user (U1), ▪ extract terms (Tki) from the set of digital content (Dk); ▪ generate messages containing said extracted data; ▪ receive data processed by said remote server, ▪ a remote server (SERV1) configured to receive the data emitted by the electronic terminal and to implement a neural network (CNN1) trained to produce outputs (KF) feeding different classifiers, said server (SERV1) being, in addition, configured to generate a list of user profiles that have exchanged data with the first user (U1).