AI Search Query Visualization from Natural Language Input
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Solution Overview
Problem
Many users lack the knowledge of programming languages and their syntax, making it difficult for them to perform desired operations on databases, such as with Search Processing Language (SPL), which has a steep learning curve and requires complex syntax understanding.
Innovation Solution
A system utilizing artificial intelligence, specifically generative AI, translates natural language descriptions of search queries into executable search query statements, such as SPL, through a machine learning model trained to predict text outputs, allowing users to generate and execute queries without needing programming expertise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If users directly write computer software code to perform operations on databases, then the operations can be performed with precision and control, but the complexity and difficulty increase significantly requiring programming language knowledge and syntax understanding
Solution Approach 1:
The patent introduces an intermediary system comprising natural language processing modules and code generation algorithms that translate user-friendly natural language queries into executable database code. This intermediary layer allows users to interact with databases using simple language while the system automatically generates the complex syntax required for precise query execution, thereby resolving the contradiction between execution precision and syntax complexity.
Solution Approach 2:
The patent replaces the mechanical process of manually writing and debugging programming code with an automated intelligent system that generates executable queries. The system uses machine learning models and natural language processing to substitute the manual coding mechanism with an automated code generation mechanism, maintaining query precision while eliminating the need for users to understand complex programming syntax.
2Productivity
If programming languages with complex syntax are used to query databases, then powerful and precise operations can be performed, but the ease of operation decreases making it inaccessible to users without programming knowledge
Solution Approach 1:
The patent employs an intermediary natural language processing interface that sits between the user and the database system. Users interact with this intermediary using simple, English-like language, and the intermediary automatically translates these queries into powerful database operations. This maintains high data retrieval capability while dramatically improving ease of operation by eliminating the need for programming knowledge.
Solution Approach 2:
The patent creates a simplified copy or representation of the database query interface that uses natural language instead of programming syntax. The system maintains the full power of complex database operations by copying the functionality into an accessible natural language format, allowing users to perform sophisticated data retrieval without learning complex syntax.
3Ease of operation
If automated query generation using AI is implemented, then the ease of operation and accessibility improve for users without programming knowledge, but the device complexity and computational resources required increase
Solution Approach 1:
The patent segments the AI-powered query generation system into distinct modular components including natural language processing modules, code generation algorithms, and machine learning models. Each module performs a specific function in the query translation pipeline. This segmentation makes the complex system more manageable, allows for independent optimization of each component, and facilitates easier maintenance and updates while maintaining the overall simplicity of the user interface.
Data Source
AI summary
Implementations of this disclosure provide a computer-implemented method that includes operations of receiving user input being a natural language description of a search query through a graphical user interface (GUI) displayed on a display screen of a network device and generating an executable search query statement from the natural language description including providing the natural language description as input to a trained machine learning model, wherein the trained machine learning model, following processing of the natural language description, provides the executable search query statement as an output. Further operations include obtaining results of execution of the executable search query statement, automatically determining a graphical visualization configured to display the results of the execution of the executable search query statement, and updating the GUI by generating the graphical visualization populated with the results of the execution of the executable search query statement.