Artificial Knowledge Object System for Natural Language Comprehension
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Solution Overview
Problem
Current AI technologies lack the capacity for true comprehension of the world, relying on information processing rather than understanding, and struggle to scale knowledge representation effectively, leading to limited practical applications and frustration in natural language interactions.
Innovation Solution
The development of the Artificial Knowledge Object System (AKOS) with a Core World Model (CWM) that creates a compact, conceptual model of the real world, enabling machines to comprehend and interact intelligently by processing and extending a sophisticated information structure that models external reality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If knowledge is represented as a large unstructured collection of assertions to enable comprehensive world modeling, then the system can handle more complex natural language queries, but the device complexity and computational resources required increase significantly
Solution Approach 1:
The patent segments the knowledge representation into a hierarchical structure with a Core World Model containing fundamental concepts and relationships, and an extended model that builds upon it. This segmentation allows the system to manage complexity by organizing knowledge into manageable layers, where the core model provides a compact foundation and extensions add specific domain knowledge as needed.
Solution Approach 2:
The patent introduces a new dimension to knowledge representation by creating a hierarchical, multi-layered model structure. Instead of a flat unstructured collection, the knowledge is organized in dimensions of abstraction levels (core vs. extended model) and relationship types, enabling efficient querying without requiring the entire knowledge base to be processed.
2Productivity
If a compact Core World Model with only a few thousand elements is used, then the system achieves efficiency and scalability, but the measurement precision and completeness of world knowledge representation may be reduced
Solution Approach 1:
The patent performs preliminary action by pre-defining a compact Core World Model with essential concepts and relationships that capture the fundamental structure of the world. This core model is designed in advance to provide a efficient foundation that can be quickly processed, while still maintaining the ability to represent complex knowledge through structured relationships and extensions.
Solution Approach 2:
The Core World Model acts as an intermediary between the compact efficiency requirements and the comprehensive knowledge representation needs. It provides a simplified intermediate layer that maps essential real-world concepts and relationships, enabling the system to achieve both efficiency in processing and sufficient completeness for practical applications through this mediating structure.
3Adaptability or versatility
If sophisticated information processing algorithms are developed to emulate human intelligence, then the system can perform complex cognitive tasks, but the loss of information occurs as the system processes rather than truly comprehends data
Solution Approach 1:
The patent creates a computational copy of the world model that mirrors the structure of reality. By representing objects, concepts, and relationships as software objects with defined properties and interconnections, the system maintains a faithful copy of world knowledge that preserves meaning and context, reducing information loss during processing.
Solution Approach 2:
The Core World Model serves as an intermediary layer between raw data input and cognitive task processing. It provides a structured representation that preserves semantic meaning and contextual relationships, acting as a bridge that maintains information integrity while enabling complex cognitive operations through the organized knowledge structure.
4Ease of operation
If existing AI systems process information without true comprehension, then they can operate with simple data structures, but they fail to provide accurate and meaningful responses in natural language interactions
Solution Approach 1:
The patent segments the processing system into distinct components: a Core World Model for knowledge representation, a natural language processing layer for interpretation, and a response generation layer. This segmentation allows each component to operate with appropriate complexity - the core model maintains simple structured data while the processing layers handle the complexity of language understanding and generation, improving both ease of operation and response accuracy.
Data Source
AI summary
The AKOS (Artificial Knowledge Object System) of the invention is a software processing engine that relates incoming information to pre-existing stored knowledge in the form of a world model and, through a process analogous to human learning and comprehension, updates or extends the knowledge contained in the model, based on the content of the new information. Incoming information can come from sensors, computer to computer communication, or natural human language in the form of text messages. The software creates as an output. Intelligent action is defined as an output to the real-world accompanied by an alteration to the internal world model which accurately reflects an expected, specified outcome from the action. These actions may be control signals across any standard electronic computer interface or may be direct communications to a human in natural language.