Domain-General AI Platform for Natural Language Data Analysis
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Solution Overview
Problem
Current data science tools are inaccessible to non-technical individuals, leading to inefficient, time-consuming, and inaccurate decision-making processes, as they require coding expertise and are not user-friendly for everyday applications.
Innovation Solution
A domain-general artificial intelligence platform that enables data-informed decision-making through a natural language interface, converting user queries into executable code without requiring coding knowledge, and providing multimodal outputs for user understanding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current data science tools are used, then decision-making accuracy can be improved, but the tools are inaccessible to non-technical individuals requiring coding expertise
Solution Approach 1:
The patent introduces a natural language processing interface as an intermediary between users and data science tools. This mediator translates user-friendly natural language queries into executable code, eliminating the need for users to learn programming languages while maintaining access to powerful data analysis capabilities.
Solution Approach 2:
The patent replaces the mechanical system of manual coding and technical操作流程 with an automated natural language processing system. Users simply type or speak their questions in natural language, and the system automatically generates, executes, and interprets the necessary code, substituting the complex mechanical process of programming with a simplified linguistic interface.
2Reliability
If traditional data science pipelines are used, then analytical capabilities can be improved, but the process becomes time-consuming and inefficient
Solution Approach 1:
The patent performs preliminary actions by pre-compiling and storing executable code templates for common data science operations. When a user submits a natural language query, the system retrieves and combines these pre-prepared code segments rather than generating code from scratch, significantly reducing the time required to execute analytical tasks.
Solution Approach 2:
The patent enables continuous useful action by implementing an iterative refinement process where the system learns from each interaction. The natural language processing model continuously improves by incorporating feedback from user queries and corrections, making the system progressively more efficient and accurate with each use, thereby maintaining high productivity while improving reliability.
3Manufacturing precision
If coding-based interfaces are used, then technical precision can be maintained, but the complexity of the system increases
Solution Approach 1:
The patent segments the complex data science system into distinct modular components: a natural language processing layer, a code generation layer, an execution layer, and a results interpretation layer. Each module handles a specific aspect of the process, allowing the system to maintain technical precision through specialized components while reducing overall complexity by organizing functions into manageable, independent units that can be developed and maintained separately.
Data Source
AI summary
A domain-general artificial intelligence platform or system and methods that enable data-informed decision making for anyone without the need for any coding ability are disclosed. This artificial intelligence platform has domain-generality, interoperability across heterogeneous sources of data, and controllability by tracking provenance. The artificial intelligence platform works by receiving a natural language query, converts the natural language query into executable code grounded in the deep semantic understanding of the underlying data, using a natural language artificial intelligence engine, runs the executable code on a distributed runtime engine to generate data output, and augments the data with a generated natural language report which becomes the ultimate output to the user.