Triangular Data Structure for AI Reasoning
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Solution Overview
Problem
Current AI systems based on deep learning neural networks face challenges in generalizing across domains and require large amounts of data for training, limiting their ability to perform linear, logical reasoning.
Innovation Solution
A data processing method using a triangular basic data structure with nodes for primary, context, and resultant nodes, assigning attributes for abductive, inductive, and deductive reasoning, allowing for natural language input and query processing without the need for extensive domain-specific training data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If AI systems use deep learning neural networks, then they show superior functions in specific domains, but they require large amounts of training data and have difficulties generalizing to various domains
Solution Approach 1:
The patent segments the AI system into multiple specialized neural network modules, each trained for a specific domain or task. These modular segments can be independently trained with domain-specific data and then combined to handle diverse tasks, thereby improving generalization while maintaining specialized performance.
Solution Approach 2:
The patent implements a universal AI architecture that can perform multiple functions across different domains. By designing a core neural network framework capable of adapting to various tasks through configurable input/output layers and transfer learning, the system achieves both specialized performance and broad generalization capability.
2Reliability
If AI systems use deep learning neural networks, then they show superior functions in specific domains, but they require large amounts of data for training
Solution Approach 1:
The patent applies pre-training on large-scale general datasets followed by fine-tuning on smaller domain-specific datasets. This preliminary action of pre-training enables the model to learn general patterns and representations that transfer to specific domains, reducing the amount of domain-specific training data needed while maintaining high performance.
Solution Approach 2:
The patent implements a nested architecture where general-purpose neural network components are embedded within domain-specific processing modules. The outer layers handle domain-specific tasks while inner shared layers capture general patterns, allowing efficient data utilization across multiple domains through hierarchical feature reuse.
Data Source
AI summary
This application relates to a new paradigm of data reasoning based on natural language inputs. A data structure based on the new paradigm is a canonical of a basic data structure in the shape of a triangle. The basic data structure includes a first node being a primary node, a second node being a context node, a third node being a resultant node. The basic data structure also includes a first link connecting the first and second nodes and configured to assign an attribute of abductive reasoning between the first and second nodes and a second link connecting the second and third nodes and configured to assign an attribute of inductive reasoning between the second and third nodes. The basic data structure further includes a third link connecting the first and third nodes and configured to assign an attribute of deductive reasoning between the first and third nodes.