Metadata-Based Natural Language Query Translation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Large-scale managed networks face challenges in efficiently and consistently parsing user queries and retrieving desired data due to varying data types, amounts, and search methods, leading to inefficiencies in data management and service provision.
Innovation Solution
A remote network management platform is configured to enable natural language processing of user requests using dynamically adaptable metadata, allowing for the parsing of natural language queries into database language queries, with dedicated server devices handling the parsing to improve performance and responsiveness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If natural language queries are parsed using traditional database query languages, then data retrieval can be achieved, but the system becomes difficult to use and requires complex query syntax that users must learn
Solution Approach 1:
The patent introduces a natural language processing intermediary layer that translates user-friendly natural language queries into database query language. This mediator handles the complexity of query syntax internally while presenting a simple interface to users, resolving the contradiction between ease of operation and system complexity.
Solution Approach 2:
The patent replaces the mechanical requirement for users to learn and type complex database query syntax with an automated natural language processing system. Users simply speak or type natural language, and the system automatically converts it to the required database queries, eliminating the need for users to master complex query languages.
2Productivity
If the remote network management platform handles parsing operations locally at each computational instance, then responsiveness may be maintained, but parsing consistency and performance across multiple instances deteriorates
Solution Approach 1:
The patent merges the natural language parsing functionality into a centralized service that can be shared across multiple computational instances. This centralization ensures that all instances use the same parsing logic and metadata, guaranteeing consistency while maintaining high throughput for data retrieval operations.
Solution Approach 2:
The patent creates a universal parsing service that serves multiple computational instances with different data needs. This single parsing infrastructure handles queries across various databases and instances, ensuring consistent behavior while improving overall system productivity through resource sharing and optimized query processing.
3Adaptability or versatility
If the grammar for parsing natural language is fixed and static, then parsing performance is efficient, but the system cannot adapt to changing user vocabulary or database schemas
Solution Approach 1:
The patent implements a dynamic grammar system where the parsing rules and metadata are not fixed but can be updated and adapted over time. The system automatically adjusts to new vocabulary, database schemas, and user preferences, maintaining high adaptability while the underlying parsing engine remains efficient through incremental updates rather than complete rewrites.
Solution Approach 2:
The patent performs preliminary actions by pre-processing and storing metadata about database schemas, column names, and acceptable vocabulary in a structured format. This preparation work is done in advance, allowing the parsing system to quickly adapt to changes by referencing pre-compiled metadata rather than requiring complex real-time analysis, thus balancing adaptability with parsing efficiency.
4Productivity
If dedicated server devices are used for parsing operations, then parsing performance and consistency improve, but system complexity and infrastructure requirements increase
Solution Approach 1:
The patent segments the system into distinct functional components: dedicated parsing servers for natural language processing, computational instances for data storage and retrieval, and client interfaces for user interaction. This segmentation allows each component to be optimized independently, with parsing servers focusing on translation efficiency while computational instances focus on data management, thereby improving overall productivity without overwhelming complexity.
Data Source
AI summary
A system may include: a database table, a grammar containing metadata-referencing non-terminals, and metadata mapping the metadata-referencing non-terminals into alternative terms for the table and a column therein. The system may also include a server device configured to: (i) receive a natural language query, the grammar, and the metadata, wherein the natural language query contains a first alternative term for the table and a second alternative term for the column; (ii) parse, using the grammar, the natural language query into a series of terminals and non-terminals including a first metadata-referencing non-terminal that the metadata maps to the first alternative term and a second metadata-referencing non-terminal that the metadata maps to the second alternative term; (iii) identify, based on the first metadata-referencing non-terminal, the table; (iv) identify, based on the second metadata-referencing non-terminal, the column; and (v) generate, based on the table and the column as identified, a database language query.