Personalized Financial Recommendation AI With UI-Transaction Mapping

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Solution Overview

Problem

Existing financial recommendation systems lack the ability to unify user experience across different interfaces and do not effectively incorporate user-specific optimizations based on interface interactions and financial behaviors, leading to suboptimal decision-making support.

Innovation Solution

A computer system and method utilizing two AI models, a generative code effectiveness learning model and a goal-oriented financial transaction optimization network, that communicate and mutually update each other to optimize user interfaces and transactions, providing personalized recommendations through a functional mapping.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If multiple AI models are integrated to optimize both UI effectiveness and financial transactions, then personalization and adaptability improve, but system complexity increases

Engineering Contradiction:

Improvepersonalization capabilityVSAvoidsystem complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system segments the recommendation functionality into two distinct AI models: a first AI model specialized in UI effectiveness optimization and a second AI model specialized in financial transaction optimization. Each model independently processes its domain and communicates through structured data exchange, allowing specialized optimization without monolithic complexity

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

A model mapping module serves as an intermediary between the two AI models, establishing functional mappings that enable communication and mutual updates. This intermediary layer coordinates data flow and synchronization without requiring direct complex integration between the specialized models

Inventive Principle:

Principle #24Intermediary (Mediator)

2Measurement precision

If AI models continuously learn from user interactions in real-time, then recommendation accuracy improves, but computational resource consumption increases

Engineering Contradiction:

Improverecommendation accuracyVSAvoidcomputational resource consumption

Core Design Contradiction:

Measurement precisionVSUse of energy by moving object

Solution Approach 1:

The system performs preliminary actions by pre-establishing functional mappings between AI models and pre-structuring the data exchange protocols. User feedback data is collected and prepared in advance, allowing the AI models to process information more efficiently during real-time operations without redundant computational overhead

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system implements continuous feedback loops where user feedback data from interactions with generated UIs is fed back to the first AI model for retraining. This feedback mechanism enables progressive accuracy improvement while maintaining computational efficiency through targeted learning from actual user behavior patterns

Inventive Principle:

Principle #23Feedback

3Ease of operation

If the system generates and tests multiple UI variations to optimize effectiveness, then user interface quality improves, but development time and cost increase

Engineering Contradiction:

Improveuser interface qualityVSAvoiddevelopment time

Core Design Contradiction:

Ease of operationVSLoss of time

Solution Approach 1:

The first AI model autonomously generates multiple UI element variations and autonomously evaluates their effectiveness based on learned patterns from user feedback data. The system self-optimizes interface quality without requiring manual design iterations, automatically selecting and implementing the most effective UI configurations

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system replaces manual UI design and testing mechanics with an automated AI-driven process. The first AI model, trained on user feedback data, automatically generates, evaluates, and optimizes UI elements, substituting the traditional mechanical process of manual design iterations with intelligent automated generation and selection

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS20260037810A1Artificial intelligence-based personalized financial recommendation assistant system and method

Publication Date: 2026.02.05 ELPIS TECH INC

AI summary

Provided are a computer system and method for generating and providing intelligent recommendations using artificial intelligence (“AI”). The system includes a memory for storing user feedback data, user resource data, and user goal data, and a processor in communication with the memory. The processor is configured to execute a first AI model for user interface (“UI”) effectiveness optimization, a second AI model for transaction optimization, a model mapping module configured to implement a functional mapping between the first AI model and the second AI model through which the first AI model and second AI model communicate and mutually update each other, and a user interface generator module for generating a user interface for outputting the intelligent recommendations and receiving the user feedback data.