Multi-Model AI Framework for Personalized User Interaction

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Solution Overview

Problem

Current customer interaction systems lack the ability to recognize individual differences in customers and adapt communication styles to maximize engagement, leading to varying levels of satisfaction and engagement across different demographics.

Innovation Solution

A system utilizing a multi-model artificial intelligence framework that generates recommendations for modifying user interactions by training neural network classification models on historical data to predict communication features that maximize user engagement, adapting communication styles in real-time based on predicted outcomes.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If a one-size-fits-all communication paradigm is used, then system complexity is reduced and ease of operation is improved, but user engagement and satisfaction deteriorate due to inability to accommodate individual customer preferences

Engineering Contradiction:

Improveease of operationVSAvoidadaptability

Core Design Contradiction:

Ease of operationVSAdaptability or versatility

Solution Approach 1:

The patent segments customers into different groups based on demographic characteristics, communication preferences, and engagement patterns. By dividing the customer base into segments, the system can apply different communication strategies to each segment, achieving adaptability without requiring complete customization for every individual customer.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system dynamically adjusts communication characteristics in real-time based on customer responses and engagement metrics. Communication parameters such as tone, speed, and complexity are continuously adapted during interactions, allowing the system to respond to individual customer needs while maintaining operational efficiency through automated adjustment.

Inventive Principle:

Principle #15Dynamics

2Adaptability or versatility

If multiple AI classification models are trained and executed to predict communication features, then user engagement is maximized through personalized interactions, but device complexity and computational resources increase

Engineering Contradiction:

ImproveadaptabilityVSAvoiddevice complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system employs multiple classification models, but not all models are executed for every customer interaction. Instead, models are selectively applied based on customer segment, interaction type, and confidence thresholds. This partial execution reduces computational overhead while maintaining the benefits of multiple perspectives in predicting communication features.

Inventive Principle:

Principle #16Partial or excessive action

Solution Approach 2:

The patent introduces an intermediary layer that manages the multiple AI classification models. This intermediary selects which models to execute, aggregates their predictions, and translates them into actionable communication adjustments. The intermediary simplifies the complexity by providing a unified interface between the multiple models and the communication system.

Inventive Principle:

Principle #24Intermediary (Mediator)

3Productivity

If communication interactions are automated using AI systems, then productivity increases and human resources are reduced, but the ability to provide truly customized interactions deteriorates without advanced personalization capabilities

Engineering Contradiction:

ImproveproductivityVSAvoidadaptability

Core Design Contradiction:

ProductivityVSAdaptability or versatility

Solution Approach 1:

The AI system performs self-learning and self-adjustment by continuously analyzing interaction outcomes and refining its prediction models. The system automatically updates communication strategies based on observed customer responses, eliminating the need for manual programming of each interaction scenario while maintaining high adaptability to individual customer preferences.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system implements continuous feedback loops where customer engagement metrics, satisfaction signals, and interaction outcomes are fed back into the AI models. This feedback mechanism enables the automated system to learn from past interactions and improve its personalization capabilities over time, bridging the gap between automation and customized service.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS11210607B2Automated predictive analysis and modification of user interaction features using multiple classification models

Publication Date: 2021.12.28 FMR CORP

AI summary

Methods and apparatuses are described for automated predictive analysis of user interactions to determine a modification based upon competing classification models. A server computing device receives first encoded text for prior user interactions and trains a plurality of classification models using the first text. The server determines a prediction cost for each of the models based upon the training. The server receives second encoded text for a current user interaction and executes the trained models using the second text to generate a prediction vector for each model that maximizes user engagement. The server selects one of the models based upon the prediction vectors, identifies a communication feature of the model, generates a user interaction modification, and transmits the user interaction modification to a client computing device.