Computerized Brain Models for Faster AI Alignment Feedback

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Solution Overview

Problem

Existing AI alignment processes for generative models are costly, time-consuming, and prone to inconsistencies due to manual human tagging, leading to inaccurate and sometimes offensive outputs.

Innovation Solution

Utilizing a computerized brain model trained on physiological responses to content items, which simulates human feedback for AI alignment, reducing the need for manual intervention and enhancing alignment accuracy and consistency.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If manual human tagging is used for AI alignment, then alignment can be performed with current technology, but the process is costly and time-consuming

Engineering Contradiction:

Improvealignment speedVSAvoidtime required for alignment

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The patent uses computerized brain models that simulate human brain responses to content items, creating virtual copies of human evaluators. These models generate simulated human feedback for AI alignment, replacing the need for actual human taggers and dramatically reducing time and cost requirements while maintaining alignment functionality

Inventive Principle:

Principle #26Copying

Solution Approach 2:

The patent replaces the mechanical process of manual human tagging with automated computerized brain models. The models process content items through simulated neural processing, generating alignment feedback automatically without human intervention, thereby eliminating the time-consuming manual process

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Measurement precision

If manual human tagging is used for AI alignment, then alignment process can be implemented, but it results in errors and inaccuracies due to non-uniformity and inconsistency

Engineering Contradiction:

Improvealignment accuracyVSAvoidconsistency of alignment

Core Design Contradiction:

Measurement precisionVSReliability

Solution Approach 1:

The patent employs computerized brain models that provide uniform and consistent feedback across all content items. Unlike human evaluators who exhibit non-uniformity and inconsistency, the AI models maintain homogeneous processing standards, ensuring reliable and reproducible alignment results through standardized simulated brain responses

Inventive Principle:

Principle #33Homogeneity

Solution Approach 2:

By replacing manual human tagging with automated neural network models, the patent eliminates the variability inherent in human evaluation. The computerized models process all content items through identical algorithmic pathways, ensuring consistent and accurate alignment without the errors and inaccuracies caused by human non-uniformity

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Quantity of substance

If a large number of people are used for manual tagging, then more comprehensive alignment data can be collected, but the cost increases significantly

Engineering Contradiction:

Improveamount of alignment dataVSAvoidcost of alignment process

Core Design Contradiction:

Quantity of substanceVSLoss of energy

Solution Approach 1:

The patent creates multiple instances of computerized brain models that can simultaneously evaluate large quantities of content items. These virtual models replicate human evaluation capabilities without the need for actual human participants, enabling comprehensive data collection at minimal cost by deploying numerous AI models parallelly

Inventive Principle:

Principle #26Copying

Solution Approach 2:

The computerized brain models autonomously generate alignment feedback without requiring human resources. The system serves itself by automatically processing content items through the neural networks, eliminating the need to pay human taggers and significantly reducing the cost of collecting comprehensive alignment data

Inventive Principle:

Principle #25Self-service

4Ease of manufacture

If manual tagging is used for AI alignment, then alignment process can be completed, but it requires significant financial resources

Engineering Contradiction:

Improvefeasibility of alignmentVSAvoidmoney spent on alignment

Core Design Contradiction:

Ease of manufactureVSLoss of energy

Solution Approach 1:

The patent replaces expensive human labor with computationally efficient AI models. The computerized brain models can be deployed at low cost through software processing, eliminating the need to pay human taggers while maintaining the feasibility of conducting comprehensive alignment studies

Inventive Principle:

Principle #26Copying

Solution Approach 2:

The patent substitutes the economic resource of human labor with computational resources. By replacing manual tagging with automated neural network processing, the system achieves alignment feasibility through software-based processing rather than financial investment in human participants

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS20260080223A1Alignment of Artificial Intelligence Models using Models of Physiological Response Patterns

Publication Date: 2026.03.19 BRAINVIVO LTD

AI summary

A method for performing artificial intelligence (AI) alignment on a generative AI model (GAIM), the method includes receiving a content item, which is generated by the GAIM. A computerized brain model, which has been trained to simulate responses of at least one human to content items generated by the GAIM, is applied to the content item. The AI alignment is performed on the GAIM based on a simulated response of the computerized brain model to the content item.