Fine-Tuned ML Model for Natural Language to Enterprise Query Translation
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Solution Overview
Problem
The current process of performing enterprise analytics is time-consuming, costly, and prone to errors due to the need for manual translation of user requests into specialized query languages, which becomes impractical with multiple users or requests.
Innovation Solution
A system utilizing a Machine Learning model data store and fine-tuning data store to create a fine-tuned ML model that automatically converts natural language user requests into enterprise analytics queries, enabling efficient and accurate data retrieval and chart generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual translation of natural language requests into specialized query languages is performed, then accuracy of query generation can be maintained, but time consumption and operational complexity increase significantly
Solution Approach 1:
The system performs preliminary action by pre-training the machine learning model on extensive query language syntax and semantics before deployment. The model learns query generation patterns in advance through fine-tuning with labeled training data, enabling it to accurately translate natural language to query language without requiring manual intervention during actual query execution.
Solution Approach 2:
The machine learning model serves as an intermediary between natural language requests and specialized query languages. Instead of direct manual translation, the model acts as a mediator that automatically converts user-friendly natural language into precise query language, maintaining accuracy while eliminating time-consuming manual processes.
2Measurement precision
If manual query translation is performed by business intelligence engineers, then query accuracy is maintained, but operational cost and complexity increase
Solution Approach 1:
The system implements self-service by enabling users to directly input natural language requests without requiring business intelligence engineers or data analysts for translation. The machine learning model autonomously handles the translation process, allowing users to serve themselves and eliminating the need for specialized human intervention in query generation.
Solution Approach 2:
The patent replaces the mechanical system of manual human translation with an automated machine learning-based translation system. The ML model substitutes the human analyst's cognitive process with an automated computational process that maintains query accuracy while dramatically reducing operational complexity and cost.
3Productivity
If automated query generation is implemented without fine-tuning, then processing speed increases, but query accuracy and reliability decrease
Solution Approach 1:
The system performs preliminary fine-tuning of the machine learning model using labeled training data that includes examples of natural language requests and their corresponding accurate query language translations. This pre-processing step ensures the model learns the specific syntax and semantics of the target query language before deployment, guaranteeing both speed and reliability during actual operation.
Solution Approach 2:
The system incorporates feedback mechanisms during the fine-tuning process, where the model learns from corrected and validated query translations. Training data includes feedback loops that refine the model's understanding of query language requirements, ensuring high reliability while maintaining automated processing speed.
Data Source
AI summary
A system for enterprise analytics may include a Machine Learning (“ML”) model data store containing at least one generic ML model and a fine-tuning data store containing prior natural language user requests and associated enterprise database queries generated by analysts. The system may also include an enterprise data store containing enterprise business data. A transformation framework may retrieve the generic ML model and fine-tune the model using the prior user requests and associated enterprise database queries to create a fine-tuned ML model. The framework may then receive a new natural language request from a user and use the fine-tuned ML model and new natural language request to automatically create a new enterprise analytics query. The new enterprise analytics query may then be executed to fetch enterprise analytics data from the enterprise data store. In some embodiments, an analytics chart may be automatically created and provided to the user.