Machine Learning Output Vector Validation for Bias and IP Compliance

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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 inefficiencies and inconsistencies in content moderation.

Innovation Solution

A systematic and automated approach using a meta-model that includes machine learning models to analyze AI-generated content, detect subtle biases and IP violations, and generate validation actions to ensure compliance, with mechanisms for real-time monitoring and automated corrections.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If manual interpretation of AI model outputs is used to enforce vector constraints, then compliance can be assessed, but the process becomes labor-intensive and error-prone

Engineering Contradiction:

Improvecompliance assessment accuracyVSAvoidmanual process complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent replaces manual mechanical interpretation processes with an automated ML-based validation system. The validation model automatically processes AI model outputs, compares them against vector constraints, and generates compliance assessments without human intervention, thereby eliminating labor-intensive manual review while maintaining or improving accuracy through systematic automated validation.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The validation system enables the AI model to self-validate its outputs against predefined vector constraints. The automated validation process allows the system to independently assess compliance without requiring external manual interpretation, reducing human involvement and enabling scalable self-monitoring of constraint adherence.

Inventive Principle:

Principle #25Self-service

2Reliability

If manual validation methods are used for content moderation, then compliance can be checked, but scalability is severely limited

Engineering Contradiction:

Improveconstraint adherence verificationVSAvoidvalidation throughput

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The patent substitutes manual validation mechanics with automated computational processes. The validation model processes AI outputs at machine speed, enabling high-volume throughput while maintaining reliable constraint verification. This automation allows the system to scale validation capacity proportionally with computational resources rather than being constrained by human reviewer availability.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Productivity

If traditional content moderation approaches are used, then some compliance issues can be detected, but subtle biases and IP violations are missed

Engineering Contradiction:

Improvemoderation efficiencyVSAvoidbias detection accuracy

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The patent introduces vector representations as an intermediary layer between AI outputs and compliance assessment. By translating text outputs into vector space and comparing them against constraint vectors, the system enables precise detection of subtle biases and IP violations that traditional text-based moderation would miss, while maintaining efficient automated processing through mathematical operations.

Inventive Principle:

Principle #24Intermediary (Mediator)

4Productivity

If automated validation systems are implemented, then scalability improves, but system complexity increases

Engineering Contradiction:

Improvevalidation scalabilityVSAvoidvalidation system architecture

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent segments the validation system into distinct modular components: a validation model that processes outputs, a vector constraint repository that stores constraints, and a comparison mechanism that assesses adherence. This modular architecture enables scalable deployment where each component can be independently optimized and maintained, reducing overall system complexity despite the automated nature of the validation process.

Inventive Principle:

Principle #1Segmentation

Data Source

PatentUS12361335B1Validating 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.