Explainable AI Model Architecture for Domain-Specific Problem Solving

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Solution Overview

Problem

Conventional methods for creating artificial intelligence models for domain-specific problems face challenges such as lack of explainability, high resource requirements, and inability to be validated or improved by human experts.

Innovation Solution

The approach involves identifying an optimal level of description for a domain, generating candidate features relevant to solving domain-specific problems, and training predictive models using these features to produce explainable solutions.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If automated generic AI models are used to solve domain-specific problems, then the model can be applied across multiple domains, but the model becomes unexplainable and requires enormous training data

Engineering Contradiction:

Improvemodel applicability across domainsVSAvoidexplainability of model decisions

Core Design Contradiction:

Adaptability or versatilityVSLoss of information

Solution Approach 1:

The patent segments the AI model into two distinct components: a generic foundation model that provides domain-agnostic capabilities, and a domain-specific adapter layer that incorporates domain knowledge and provides explainable decisions. This segmentation allows the model to maintain versatility while achieving explainability in the specific domain through the adapter's transparent decision-making process.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The adapter acts as an intermediary between the generic foundation model and the domain-specific problem. It receives inputs from the foundation model, processes them through domain-specific logic that is human-interpretable, and produces final domain-specific outputs. This intermediary layer preserves the versatility of the foundation model while adding explainability for the target domain.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Ease of operation

If automated generic AI models are used, then the model can process unstructured observational information, but significant resources are required to generate training data

Engineering Contradiction:

Improveprocessing of unstructured observational informationVSAvoidcomputational resources for training

Core Design Contradiction:

Ease of operationVSUse of energy by moving object

Solution Approach 1:

The foundation model performs preliminary action by learning general patterns from large-scale unstructured observational data during pre-training. This pre-computed knowledge is then leveraged by the adapter without requiring re-training on the same data, significantly reducing the computational resources needed for domain-specific applications while maintaining the ability to process unstructured observational information.

Inventive Principle:

Principle #10Preliminary action

3Loss of information

If manual domain-specific AI models are created based on human expertise, then the model is explainable and domain-specific, but the development is costly and time-consuming

Engineering Contradiction:

Improveexplainability of model decisionsVSAvoidtime for model construction

Core Design Contradiction:

Loss of informationVSLoss of time

Solution Approach 1:

The foundation model serves as a universal base that can be adapted to multiple different domains through the adapter layer. Instead of manually creating separate models for each domain, the same foundation model can be reused with different adapters, significantly reducing the time and expertise required for model development while maintaining explainability through the adapter's domain-specific logic.

Inventive Principle:

Principle #6Universality (Multi-functionality)

4Reliability

If manual domain-specific AI models are created, then the model can be validated and improved by human experts, but the model cannot be repurposed for other problems

Engineering Contradiction:

Improvevalidation by human expertsVSAvoidrepurposability of the model

Core Design Contradiction:

ReliabilityVSAdaptability or versatility

Solution Approach 1:

The model architecture is designed dynamically, allowing the adapter to be reconfigured for different domains and problems while the foundation model remains static. This dynamic reconfigurability enables the system to maintain validation capabilities through human experts for each specific domain application while also allowing repurposing for different problems by simply changing the adapter rather than recreating the entire model.

Inventive Principle:

Principle #15Dynamics

Data Source

PatentUS20250028986A1Artificial intelligence for solving domain-specific problems

Publication Date: 2025.01.23 ARTIFICIAL GENIUS INC

AI summary

Aspects herein describe new methods of providing explainable solutions to domain-specific problems using artificial intelligence. A domain-specific problem, for example, a biological problem, is identified by a computational unit. The computational unit determines an initial feature subset comprising various features for observable characteristics of the problem. The computational unit optimizes the initial feature subset to improve the accuracy of the subset in generating a solution to the problem. The computational unit trains one or more predictive artificial intelligence models, based on the optimized subset, to output solutions to the problem. Subsequently, the computational unit utilizes the trained predictive model to output solutions to one or more additional domain-specific problems. The computational unit may additionally provide explainable solutions to the domain-specific problems by outputting a representation of steps performed by the predictive model to output the solutions.