AI Query Translation for Executable Search Statements
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Solution Overview
Problem
Users without programming knowledge face challenges in storing, processing, or retrieving digital data due to the complexity of programming languages like SPL, which require a steep learning curve and specific syntax, limiting their ability to perform desired operations correctly.
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 on natural language descriptions and corresponding programming language syntax, allowing users to generate and execute queries without deep programming knowledge.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If programming languages like SPL are used for data storage, processing, and retrieval, then operational capability and precision are improved, but the learning curve and syntax complexity increase, making it difficult for users without programming knowledge to perform operations
Solution Approach 1:
The patent introduces a natural language processing intermediary that translates between user-friendly natural language queries and the required programming language syntax (SPL). This mediator layer allows users to interact with complex data operations using simple English sentences while the system automatically handles the translation to precise SPL commands, resolving the contradiction between operational capability and ease of use.
Solution Approach 2:
The patent replaces the mechanical requirement of learning programming syntax and writing code with an automated natural language processing system. Instead of users manually constructing complex SPL queries with proper syntax, the system uses AI/ML models to automatically generate the required programming language commands from natural language input, eliminating the need for users to learn the programming mechanics.
2Manufacturing precision
If programming languages with specific syntax requirements are used, then manufacturing precision and measurement accuracy are improved, but device complexity and difficulty of operation increase
Solution Approach 1:
The natural language processing system acts as an intermediary that handles the complexity of syntax translation. Users provide simple natural language queries, and the system's translation layer automatically converts these into syntactically correct SPL commands, maintaining query accuracy while hiding the syntax complexity from users.
Solution Approach 2:
The patent uses training data that contains pairs of natural language descriptions and corresponding SPL queries. The machine learning model learns from these copied examples to generate accurate SPL queries from natural language input, capturing the precise syntax requirements through pattern recognition rather than requiring users to learn the syntax directly.
3Ease of operation
If natural language processing and machine learning models are used to translate queries, then ease of operation and accessibility are improved, but system complexity and computational resources increase
Solution Approach 1:
The patent implements preliminary action by pre-training the machine learning model with extensive training data containing natural language descriptions and corresponding SPL queries. This pre-training phase captures the complexity and syntax rules in advance, so that during actual operation, the model can quickly translate user queries without requiring users to understand the underlying complexity or the system to perform complex real-time analysis.
4Extent of automation
If machine learning models are trained on natural language descriptions and programming syntax, then automation capability is improved, but training time and computational resources increase
Solution Approach 1:
The patent performs the computationally intensive training of the machine learning model in advance, before the system is deployed for actual use. By completing the training phase beforehand with comprehensive training data, the system achieves high automation capability for query generation while the time cost is incurred during the initial setup rather than during ongoing operations, making the automation efficient for end users.
Data Source
AI summary
Implementations of this disclosure provide a search query statement generation system that receives user input being a natural language description of a search query and, through the use of artificial intelligence, translates the natural language description into an executable search query statement. The executable search query statement may be in the form of a pipelined search query statement. The use of artificial intelligence may include a trained machine learning model that transforms the natural language description to an alternative text format. In some instances, the trained machine learning model is a generative pre-trained transformer. In such instances, the natural language description may be provided to a chatbox, which may return one or more search query statements automatically generated by the generative pre-trained transformer. Further, one of the automatically generated search query statements may be executed such that the results are displayed within the chatbox.