AI Decision Framework With DSL and Explainable Multi-Agent Control
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Solution Overview
Problem
Existing AI-assisted decision-making systems lack a straightforward domain-specific language (DSL) for non-technical stakeholders, struggle with model change and feedback loops, and fail to align pre-trained models with specific objectives, leading to limited transparency and robustness in intellectual property innovation and strategy.
Innovation Solution
A multi-agent system comprising a Problem-Definition Agent (PD-Agent), Universal Boundary Model Construction Agent (UBMC-Agent), Solution Agent (SOLVE-Agent), and Explanation Agent (EXPLAIN-Agent) that separates problem definition, model construction, and solution phases, leveraging generative AI models in a controlled manner to ensure transparency and alignment with human objectives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If pre-trained generative AI language models are used for decision-making, then creativity and solution generation are improved, but transparency and control over spurious elements deteriorate
Solution Approach 1:
The patent introduces an intermediary layer between the pre-trained generative AI model and the decision-making process. This intermediary consists of a domain-specific language model and a structured decision framework that filters, interprets, and validates the outputs of the pre-trained model, ensuring transparency and control while preserving creativity.
Solution Approach 2:
The patent segments the decision-making system into distinct components: a pre-trained generative model for creative solution generation, a domain-specific language layer for structured interpretation, and a validation layer for transparency and control. This segmentation allows each component to specialize while maintaining overall system reliability.
2Ease of operation
If a domain-specific language system is implemented for non-technical stakeholders, then ease of operation is improved, but device complexity increases
Solution Approach 1:
The domain-specific language acts as an intermediary layer that translates between natural language queries from non-technical stakeholders and the complex computational operations of the AI system. This intermediary simplifies user interaction while managing system complexity through structured abstractions.
Solution Approach 2:
The domain-specific language is designed to be universal across different decision-making contexts within intellectual property, allowing non-technical stakeholders to interact with various complex functions through a consistent, simplified interface without requiring domain expertise.
3Productivity
If automated decision-making systems are deployed without feedback loops, then productivity is improved, but adaptability and robustness deteriorate
Solution Approach 1:
The patent implements feedback loops that allow the system to learn from decision outcomes and stakeholder responses. The domain-specific language model captures feedback systematically, enabling the system to adapt and improve over time while maintaining high decision-making productivity through automated processes.
Data Source
AI summary
The present invention is an AI-driven system for automating and accelerating optimal, transparent creative decision-making and problem-solving in intellectual property innovation and strategy. It comprises a multi-media user interface and four software agents: problem-definition (PD-Agent), model-construction (UBMC-Agent), solution-control (SOLVE-Agent), and explanatory (EXPLAIN-Agent). These agents dynamically construct a domain-specific language and ontology, convert it into a computable model, solve the model using automated solution methods, and explain the results to stakeholders. The multi-agent architecture separates problem definition, model construction, solution, and explanation phases while leveraging generative AI language and foundation models in a controlled manner. This enables large-scale quantitative decision-making with objective alignment, transparency, and interactivity, suitable for intellectual property innovation and strategy applications.