AI Output Vector Constraint Validation With Specialized Meta-Models

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Solution Overview

Problem

Traditional methods for ensuring compliance of AI applications with vector constraints, such as preventing bias, harmful language, and IP violations, are labor-intensive, error-prone, and lack scalability, leading to inconsistent and inefficient content moderation.

Innovation Solution

A systematic and automated approach using meta-models to analyze AI-generated content, including models trained to identify patterns of bias, harmful language, and IP violations, with mechanisms for real-time compliance monitoring and automated corrective actions.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If manual interpretation methods are used to validate vector constraints, then compliance assessment can be performed, but the process becomes labor-intensive and error-prone

Engineering Contradiction:

Improvecompliance assessment accuracyVSAvoidmanual interpretation requirement

Core Design Contradiction:

ReliabilityVSExtent of automation

Solution Approach 1:

The patent replaces manual mechanical interpretation processes with automated machine learning models and validation systems. These systems use trained models to automatically assess whether AI outputs satisfy vector constraints, eliminating the need for human reviewers to manually interpret and validate compliance, thereby reducing errors and labor while maintaining or improving accuracy

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The validation system enables the AI model to self-validate its outputs through automated compliance checking. The system includes mechanisms where the AI model generates outputs and the validation system automatically checks compliance with vector constraints, allowing the system to serve itself without requiring external manual intervention for compliance assessment

Inventive Principle:

Principle #25Self-service

2Productivity

If manual interpretation methods are used to validate vector constraints, then compliance can be checked, but scalability is limited and efficiency decreases

Engineering Contradiction:

Improvecompliance assessment efficiencyVSAvoidmanual process dependency

Core Design Contradiction:

ProductivityVSExtent of automation

Solution Approach 1:

The patent substitutes manual compliance checking mechanisms with automated machine learning-based validation systems. These systems can process and validate multiple AI outputs simultaneously and continuously, dramatically increasing productivity and scalability compared to manual methods while eliminating dependency on human reviewers

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The validation system operates continuously to assess compliance of AI model outputs. The automated system can validate outputs in real-time as they are generated, providing continuous compliance monitoring rather than intermittent manual review, thereby maintaining high productivity and efficiency across all AI operations

Inventive Principle:

Principle #20Continuity of useful action

3Reliability

If traditional validation approaches are used, then some compliance checking is possible, but consistency and scalability are compromised

Engineering Contradiction:

Improvecompliance consistencyVSAvoidvalidation scalability

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The patent creates a universal validation system that can assess multiple types of vector constraints (bias, harmful language, IP violations) using the same automated machine learning infrastructure. This multi-functional system maintains consistent compliance standards across different constraint types while scaling to handle various AI model outputs, achieving both reliability and productivity

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS12361334B1Validating vector constraints of outputs generated by machine learning models

Publication Date: 2025.07.15 CITIBANK N A

AI summary

The technology evaluates the compliance of an AI application with predefined vector constraints. The technology employs multiple specialized models trained to identify specific types of non-compliance with the vector constraints within AI-generated responses. One or more models evaluate the existence of certain patterns within responses generated by an AI model by analyzing the representation of the attributes within the responses. Additionally, one or more models can identify vector representations of alphanumeric characters in the AI model's response by assessing the alphanumeric character's proximate locations, frequency, and/or associations with other alphanumeric characters. Moreover, one or more models can determine indicators of vector alignment between the vector representations of the AI model's response and the vector representations of the predetermined characters by measuring differences in the direction or magnitude of the vector representations.