Natural Language Concept Builder for Deep Semantic Parsing
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Solution Overview
Problem
Current computer systems lack the ability to understand human thought and natural language at a deep level, as they cannot effectively parse and interpret the meaning of text inputs, relying on manual supervision and lacking the capability to respond appropriately to complex queries.
Innovation Solution
A system utilizing a database configured to store associations between concepts in a text string and associated concept identifiers, with a builder program that constructs tables representing knowledge by combining operands and operators to form higher-order concepts, enabling a computer to parse and respond to natural language inputs as if it understands human thought.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional text processing methods are used, then computers can process text at a high level, but they cannot understand the deep meaning incorporated in text
Solution Approach 1:
The patent segments text into discrete linguistic units (words, phrases, sentences) and processes each through separate database tables (vocabulary, grammar, punctuation) before integrating results. This allows deep semantic analysis without overwhelming system complexity by breaking down the understanding process into manageable components.
Solution Approach 2:
The patent introduces intermediary database tables that act as mediators between raw text and computer understanding. These tables (vocabulary mappings, grammar rules, punctuation conventions) translate human language structures into computer-processable concepts, enabling deep meaning extraction without direct complex processing.
2Ease of operation
If a database system is designed to enable unrestricted natural language communication, then computers can interpret complex text inputs with precision, but the system requires complex database structures and processing mechanisms
Solution Approach 1:
The patent creates a universal database framework that handles multiple language functions (vocabulary lookup, grammar validation, punctuation interpretation) through a single integrated system architecture. This multi-functional approach enables unrestricted natural language communication while managing complexity through standardized processing routines.
Solution Approach 2:
The patent performs preliminary processing of text inputs by pre-loading vocabulary meanings, grammar rules, and punctuation conventions into database tables before actual communication occurs. This preparation work reduces real-time processing complexity while maintaining ease of natural language interaction.
3Reliability
If computers are to respond appropriately to complex queries with human-level understanding, then deep semantic analysis is required, but current systems lack the capability to parse and interpret meaning at that depth
Solution Approach 1:
The patent implements feedback mechanisms where the database system continuously refines its understanding by comparing parsed results against stored linguistic knowledge. This iterative verification process improves response accuracy by ensuring parsed meanings align with established vocabulary, grammar, and contextual conventions.
Solution Approach 2:
The patent enables the system to automatically parse and interpret text meaning through self-contained database queries without requiring external human intervention. The vocabulary, grammar, and punctuation tables work together autonomously to extract semantic meaning, achieving reliable automatic parsing capability.
Data Source
AI summary
A system for knowledge representation in a computer that can enable unrestricted natural language communication with a computer, comprising a database configured to store at least one table, the table configured to store associations between concepts in a text string and associated concept identifiers; and a builder interfaced with the database, the builder configured to construct a table that represents knowledge defined by a text string, the text string comprising (n) operands and (n−1) operators, by performing a process comprising the following steps: combining concepts in the text string into first order sub-concepts according to precedence defined by the operators, combining the first order sub-concepts into higher order concepts according to the precedence defined by the operators, repeating the steps until the higher order sub-concepts join to form a concept that represents the entire text string, and storing the concept, first order sub-concepts, and higher order sub-concepts in the database.