AI Communication Engine for Proactive Training and Fraud Detection

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Solution Overview

Problem

Current chatbots are limited in their ability to deliver bespoke training materials and detect malicious activities during user interactions, failing to predict user needs and respond proactively.

Innovation Solution

An AI/ML communication engine monitors user interactions, determines fraudulent activity, and provides proactive training by analyzing user habits and historical data to deliver information and training materials before they are requested.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If current chatbots are used for user interaction, then basic communication is achieved, but the ability to deliver bespoke training materials and detect malicious activities is limited

Engineering Contradiction:

Improveability to deliver bespoke training materials and detect malicious activitiesVSAvoiddetection of fraudulent activity

Core Design Contradiction:

Adaptability or versatilityVSReliability

Solution Approach 1:

The AI/ML communication engine performs preliminary analysis of user interactions, device information, and behavior patterns before malicious activities occur or before training is needed. The system proactively identifies potential fraud risks and delivers training materials in advance, rather than reacting after problems arise.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system continuously monitors user interactions and uses machine learning to analyze feedback from device information, interaction patterns, and behavior data. This feedback loop enables the system to adaptively improve its detection accuracy and personalize training materials based on observed user needs and risk patterns.

Inventive Principle:

Principle #23Feedback

2Reliability

If AI/ML communication engine monitors all user interactions, then fraudulent activity detection and training needs identification are improved, but system complexity increases

Engineering Contradiction:

Improvefraudulent activity detection accuracyVSAvoidcommunication engine structure

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The AI/ML communication engine is designed as a multi-functional system that simultaneously performs multiple tasks: monitoring user interactions, analyzing device information, detecting fraudulent activities, identifying training needs, and delivering personalized content. This universal approach consolidates what would otherwise require separate systems into one integrated engine.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The communication engine autonomously performs analysis and decision-making using embedded AI/ML algorithms. It self-manages the monitoring, detection, and training delivery processes without requiring constant external intervention, reducing the operational complexity despite the sophisticated functionality.

Inventive Principle:

Principle #25Self-service

3Ease of operation

If AI/ML algorithms analyze user behavior patterns and historical data, then proactive communication and personalized training are achieved, but data processing requirements increase

Engineering Contradiction:

Improvepersonalized training deliveryVSAvoiddata processing energy consumption

Core Design Contradiction:

Ease of operationVSUse of energy by moving object

Solution Approach 1:

The system applies different levels of analysis intensity to different users and interactions based on local characteristics. High-risk users or complex interactions receive more intensive AI/ML analysis, while routine interactions use lighter processing. This localized approach personalizes training delivery while optimizing energy consumption by avoiding uniform high-intensity processing for all users.

Inventive Principle:

Principle #3Local quality

Data Source

PatentUS20250363400A1Apparatus and methods for proactive communication

Publication Date: 2025.11.27 BANK OF AMERICA CORP

AI summary

Apparatus and methods for proactively and preemptively communicating with a user interacting with a software application are provided. The apparatus and methods may include an artificial intelligence/machine learning communication engine monitoring and tracking a user's interactions. The apparatus and methods may include the communication engine determining if the user requires further training, if the interaction is fraudulent, and pre-empting requests for information the user may commence. The apparatus and methods may include the communication engine creating and displaying training materials for the user to complete, revoking access if fraud is present, and proactively providing information before the user requests the information.