Neural Network Learning Engine for Dynamic User Profile Security

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Solution Overview

Problem

Current systems fail to effectively optimize user interactions across multiple channels for enhanced security, as they lack the ability to dynamically analyze and adapt to complex patterns in user behavior, leading to vulnerabilities in account access and security.

Innovation Solution

A system utilizing a neural network learning engine and controller that analyzes historical and streaming user interaction data from various channels to build a unified user profile, calculate confidence levels, and generate a threat assessment map, enabling real-time optimization and security enhancements by identifying authorized patterns and preventing unauthorized access.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If traditional systems are used to monitor user interactions, then system complexity remains low, but security reliability deteriorates due to inability to detect complex unauthorized access patterns

Engineering Contradiction:

Improvesecurity reliabilityVSAvoidsystem complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

A neural network learning engine is introduced as an intermediary component between data collection and security decision-making. This intermediary processes complex interaction patterns across multiple channels and generates unified user profiles, enabling the system to detect sophisticated unauthorized access patterns while maintaining manageable architectural complexity through modular design

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

Traditional rule-based security systems are replaced with a machine learning-based system that uses neural networks to analyze user interaction patterns. This substitution enables the system to adapt to evolving attack patterns and detect complex unauthorized access behaviors that would be impossible to capture with static security rules

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Difficulty of detecting and measuring

If multi-channel user interaction data is collected and analyzed, then security detection capability improves, but data processing complexity increases

Engineering Contradiction:

Improvesecurity detection capabilityVSAvoiddata processing complexity

Core Design Contradiction:

Difficulty of detecting and measuringVSDevice complexity

Solution Approach 1:

The system merges data from multiple communication channels (email, SMS, voice, chat) into a unified user profile through the neural network learning engine. This consolidation approach consolidates complex multi-channel data into a single structured representation, making security analysis more manageable while preserving the comprehensive detection capability across all channels

Inventive Principle:

Principle #5Merging (Combining)

Solution Approach 2:

The neural network learning engine serves multiple functions simultaneously: it processes data from diverse channels, extracts interaction patterns, generates user profiles, and identifies security threats. This multi-functional design reduces overall system complexity by consolidating multiple specialized components into a single versatile processing unit

Inventive Principle:

Principle #6Universality (Multi-functionality)

3Speed

If real-time analysis of streaming user interaction data is performed, then response speed to security threats improves, but computational resource consumption increases

Engineering Contradiction:

Improveresponse speedVSAvoidcomputational resource consumption

Core Design Contradiction:

SpeedVSUse of energy by moving object

Solution Approach 1:

The system continuously processes streaming user interaction data in real-time through the neural network learning engine, maintaining constant monitoring without requiring intensive batch processing. This continuous action enables immediate detection and response to security threats while distributing computational workload over time rather than concentrating it in resource-intensive periodic analyses

Inventive Principle:

Principle #20Continuity of useful action

Data Source

PatentUS10992763B2Dynamic interaction optimization and cross channel profile determination through online machine learning

Publication Date: 2021.04.27 BANK OF AMERICA CORP

AI summary

A system and method for dynamically optimizing channel interactions and account security are provided. A controller configured for analyzing user interactions is configured to determine an interaction pattern of a user during an interaction with the user over a communication channel; calculate a weighted confidence function for subsequent interactions with the user based on the determined interaction pattern, wherein the weighted confidence function defines authentication and response procedures for the subsequent interactions with the user; and merge the weighted confidence function into a custom user profile for the user across a plurality of communication channels, wherein the user profile maps account vulnerabilities based on the weighted confidence function.