Natural Language Processing Using Logical Reasoning Engine
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current natural language processing systems struggle to comprehend complex queries and contextual nuances, often requiring specific phrasing and programming, which limits their ability to understand human language intuitively and accurately.
Innovation Solution
Combining natural language parsing with a logical reasoning engine to convert natural language queries into computer-interpretable semantic representations and logical syntax, allowing for automated deduction and retrieval of answers from databases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If natural language processing systems use traditional parsing methods, then they can process simple queries, but they fail to comprehend complex queries and contextual nuances
Solution Approach 1:
The patent combines natural language parsing with a logical reasoning engine to create an integrated system. The parser converts natural language into semantic representations, which are then processed by the reasoning engine using formal logic and inference rules. This merging allows the system to handle both simple and complex queries while maintaining accuracy in understanding contextual nuances through structured logical analysis.
Solution Approach 2:
The patent introduces semantic representations as an intermediary layer between natural language input and logical processing. This intermediary converts human language into a formal structure that the reasoning engine can process, enabling accurate comprehension of complex queries and contextual relationships without losing the nuances of the original language.
2Measurement precision
If natural language processing systems require specific phrasing and programming, then they can provide accurate responses, but they limit their ability to understand human language intuitively
Solution Approach 1:
The system uses automated deduction and logical inference to generate responses without requiring human intervention or programming for each specific query. The reasoning engine autonomously processes semantic representations, applies inference rules, and derives answers, enabling the system to understand and respond to diverse human language inputs intuitively while maintaining accuracy.
Solution Approach 2:
The logical reasoning engine is designed to handle multiple types of queries and reasoning tasks through a unified framework. It can process various logical operations, apply different inference rules, and retrieve information from multiple databases, making the system universally applicable to different natural language queries without requiring specific programming for each case.
3Adaptability or versatility
If natural language processing systems use automated deduction and multiple databases, then they improve the breadth and authenticity of responses, but they increase system complexity
Solution Approach 1:
The patent divides the system into distinct functional modules: a natural language parser that converts input into semantic representations, a reasoning engine that performs logical deduction, and database components that store structured knowledge. This segmentation allows each module to specialize in specific tasks, managing complexity while enabling the system to access and integrate information from multiple databases to provide comprehensive and authentic responses.
Data Source
AI summary
Systems and methods are presented for performing natural language processing and reasoning. In some embodiments, a computer-implemented methods is presented. The method may include accessing a natural language query from a user, parsing the natural language query into a computer-interpretable semantic representation, converting the semantic representation into a computer-interpretable logical syntax, determining a solution to the computer-interpretable logical syntax using a reasoning engine and at least one data source, and outputting an answer to the natural language query using the solution to the computer-interpretable logical syntax.