AI Model Determination Platform for Cross-Domain Adaptation
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Solution Overview
Problem
Existing AI models are often non-robust and non-applicable to domains outside of the specific domain used for training, making it difficult to transfer or apply them across different domains effectively.
Innovation Solution
A model determination platform that receives source and target data from different domains, generates features and differentiators, identifies mappings, determines clusters, and generates AI models to perform a target task, using external data to refine the process and select the best AI model for the task.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing AI models are trained on domain-specific data, then they achieve high performance within that domain, but they become non-robust and non-applicable to other domains
Solution Approach 1:
The patent segments the training process into two distinct phases: (1) pre-training on source domain data to learn general patterns, and (2) fine-tuning or adaptation on target domain data to achieve domain-specific performance. This segmentation allows the model to maintain generalizability while adapting to specific domains, resolving the contradiction between reliability within a domain and adaptability across domains.
Solution Approach 2:
The patent implements dynamic model adaptation mechanisms that allow the AI model to adjust its parameters and structure based on the target domain characteristics. The system dynamically selects and applies different adaptation strategies (e.g., feature alignment, parameter fine-tuning) depending on the degree of domain difference, enabling the model to maintain reliability across varying domain conditions.
2Measurement precision
If AI models are highly specialized for a specific domain, then they achieve accurate performance for that domain, but they cannot be effectively transferred or applied across different domains
Solution Approach 1:
The patent applies preliminary domain adaptation actions before fine-tuning on target domain data. The system performs feature alignment and representation learning on source domain data that prepares the model for subsequent target domain training, enabling smoother domain transfer while maintaining target domain accuracy.
Solution Approach 2:
The patent systematically changes model parameters during domain adaptation, including learning rates, regularization strengths, and architectural hyperparameters, based on the target domain characteristics. This parameter optimization enables the model to achieve high accuracy on target domains while maintaining the flexibility to adapt to different domain requirements.
3Reliability
If manual methods are used to select and transfer AI models across domains, then human expertise can guide the process, but the process is time-consuming and subject to human bias
Solution Approach 1:
The patent implements automated feedback loops that evaluate model performance on target domain validation data and automatically adjust selection and adaptation parameters. This feedback mechanism replaces manual trial-and-error with systematic automated optimization, reducing time loss while maintaining or improving selection accuracy through objective performance metrics.
Solution Approach 2:
The system performs self-service model adaptation by automatically selecting source domains, determining adaptation strategies, and optimizing parameters without human intervention. The automated pipeline includes domain similarity assessment, model selection, and fine-tuning, eliminating human subjectivity and significantly reducing the time required for cross-domain model deployment.
Data Source
AI summary
A device receives source data, target data, external data, and a target task, and generates features of and differentiators between the source data and the target data. The device identifies a set of mappings between the source data and the target data based on the features and the differentiators, and determines different clusters of the source data based on the external data, the features, and the differentiators. The device generates, based on the external data, a set of artificial intelligence (AI) models as candidates to perform the target task, and generates a performance measure for the set of AI models based on the features, the differentiators, and the external data. The device refines the set of mappings, and identifies an AI model, from the set of AI models, to perform the target task based on the different clusters of the source data and based on the performance measure.