Cyber-Secure Natural Language Learning System
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Solution Overview
Problem
Conventional computer-based systems and neural networks struggle to learn and represent natural language effectively, leading to knowledge representation issues that are not easily understandable or extendable, and they fail to handle exceptions and metaphorical explanations.
Innovation Solution
A computing device maps input natural language sentential forms non-deterministically to matching semantics associated with computer language function components, allowing for translation and acquisition of natural language knowledge through interaction with domain experts, minimizing knowledge acquisition costs and enabling intelligent conversational discourse.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If rule-based approaches are used for natural language processing, then the system can provide literal explanations, but it cannot learn exceptions or handle metaphorical explanations
Solution Approach 1:
The system segments natural language processing into multiple components: a rule-based module for literal explanations and a case-based module for exceptions and metaphors. This segmentation allows each module to specialize in its strength while working together to handle the full range of language understanding tasks.
Solution Approach 2:
The patent merges rule-based systems and case-based systems into a unified hybrid architecture. The rule-based component handles systematic literal interpretations while the case-based component manages exceptions and metaphorical meanings, combining their capabilities to overcome individual limitations.
2Extent of automation
If expert systems use segmented if-then rules, then decision-making can be emulated, but knowledge representation is not easy to understand or extend
Solution Approach 1:
The system introduces natural language as an intermediary layer between the segmented if-then rules and the user. By representing knowledge in natural language forms, the system maintains automated decision-making capabilities while making the knowledge representation more understandable and extendable for human users.
Solution Approach 2:
The patent adds a natural language dimension to the traditional rule-based knowledge representation. This allows the system to maintain the structured logic of if-then rules internally while presenting knowledge in a more human-understandable natural language format, effectively operating in multiple representational dimensions.
3Productivity
If case-based systems model events using situation-action codes, then new problems can be solved based on past solutions, but all acquired knowledge is mutually random
Solution Approach 1:
The system creates a universal knowledge framework that integrates both rule-based and case-based representations. This unified structure allows the system to leverage past solutions efficiently while maintaining coherent organizational principles that connect different types of knowledge, making the overall knowledge base more stable and systematic.
4Adaptability or versatility
If brute force hand-coding is used to overcome rule limitations, then more exceptions can be handled, but domain transference is not benefited and the system cannot pass the Turing Test
Solution Approach 1:
The system implements dynamic knowledge acquisition through interaction with domain experts. Rather than statically hand-coding all exceptions, the system can learn and adapt to new exceptions and domain-specific knowledge through expert interactions, making the knowledge base both comprehensive and manageable.
Solution Approach 2:
The system enables self-service knowledge acquisition by allowing domain experts to interactively teach and correct the system. This reduces the burden of manual hand-coding while improving exception handling capabilities, as the system can autonomously learn from expert guidance without requiring exhaustive pre-programming.
Data Source
AI summary
A computing device learns a natural language. A mapper included in the computing device non-deterministically maps an input natural language sentential form to a matching semantic that is associated with a computer language function component. A translator included in the computing device translates the matching semantic into a translated natural language sentential form.