Domain Causal Graphs and Generative Models for Reliable Causal Outcomes
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Solution Overview
Problem
Conventional systems face challenges in accurately and efficiently utilizing generative AI models to produce desired results, particularly in fields requiring interdisciplinary knowledge, leading to inaccurate outcomes.
Innovation Solution
A causal query system that utilizes causal graphs and large generative models to determine domain-specific causal outcomes by encoding data values, populating missing graph values, and leveraging external resources for accurate and unbiased results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If generative AI models are used alone to solve problems, then system simplicity is maintained, but accuracy and reliability deteriorate in fields requiring interdisciplinary knowledge
Solution Approach 1:
The patent combines generative AI models with causal graphs into an integrated system. The causal graph component provides structured domain knowledge and causal relationships, while the generative AI model handles natural language processing and outcome generation. This merging resolves the contradiction by maintaining system simplicity through unified architecture while improving reliability through the complementary strengths of both components.
Solution Approach 2:
The causal graph acts as an intermediary between the generative AI model and the final causal outcomes. It mediates the process by providing structured domain knowledge, encoding causal relationships, and guiding the generative model to produce more accurate and reliable results in interdisciplinary fields.
2Ease of operation
If conventional systems use generative AI models without domain-specific causal graphs, then ease of operation is maintained, but measurement precision and manufacturing precision of causal outcomes deteriorate
Solution Approach 1:
The system performs preliminary action by pre-building domain-specific causal graphs that encode causal relationships and domain knowledge before processing queries. This preparation enables the system to quickly and easily process queries while maintaining high precision, as the causal graph provides a ready-made framework for accurate outcome generation without requiring complex real-time reasoning.
3Productivity
If systems attempt to fill missing data in causal graphs using only available resources, then resource efficiency is improved, but completeness and accuracy of causal outcomes worsen in sparse data domains
Solution Approach 1:
The generative AI model serves as an intermediary that bridges the gap between available data and complete causal information. When the causal graph contains missing data, the generative model uses its learned knowledge to infer and generate plausible missing values, maintaining both efficiency and completeness by avoiding extensive external data collection while still filling critical gaps.
Solution Approach 2:
The system employs self-service mechanisms where the generative AI model autonomously identifies and fills missing data in the causal graph using its internal knowledge. This self-service approach maintains processing efficiency while improving completeness, as the model can independently compensate for data gaps without requiring external intervention or extensive additional data collection.
Data Source
AI summary
This disclosure describes utilizing a causal query system to determine causal outcomes for domain-specific causal queries using a framework that includes causal graphs for targeted domains, a large generative model (LGM), and other models or systems. In various implementations, the causal query system provides a framework that includes generating domain-specific causal graphs, encoding or mapping the causal graphs with local data values, and using the encoded causal graphs to determine causal outcomes to causal queries. In some implementations, the causal query system uses the LGM and data resources (e.g., external sources) to populate missing values of an embedded causal graph before using the causal graph to determine causal outcomes.