Natural Language BI Query Trees for Faster Insight Generation
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Solution Overview
Problem
Current business intelligence tools require significant manual effort and expertise for data preparation, optimization, and dashboard creation, leading to long learning curves, high costs, and inefficiencies due to siloed data and app platforms, which are not optimized for each other.
Innovation Solution
A system that interprets natural language queries, constructs structured query trees, and generates responses by executing these trees on data sources, automating data preparation and dashboard creation, and optimizing workflows to provide efficient and accurate business intelligence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual data preparation and dashboard creation methods are used, then expertise and control are maintained, but time consumption and costs increase significantly
Solution Approach 1:
The system enables self-service by automatically generating dashboards and business insights from natural language queries without requiring manual data preparation or expert intervention. The AI agent autonomously processes queries, retrieves data, and creates visualizations, allowing users to obtain insights independently.
Solution Approach 2:
The patent replaces manual mechanical processes of data preparation and dashboard creation with an AI-based automated system. The AI agent substitutes human analysts in performing data retrieval, processing, and visualization tasks, dramatically reducing time and expertise requirements.
2Ease of operation
If traditional BI tools are used, then data analysis capability is maintained, but learning curve and operational complexity increase
Solution Approach 1:
The AI agent acts as an intermediary between users and the complex data infrastructure. Users interact with the system through simple natural language queries, and the AI agent handles the complexity of data retrieval, processing, and visualization generation, shielding users from underlying system complexity.
Solution Approach 2:
The system provides universal access to advanced BI capabilities through a single natural language interface. The AI agent performs multiple functions including data querying, analysis, visualization generation, and interpretation, consolidating previously separate tools and processes into one unified system.
3Productivity
If data and app platforms operate in silos, then platform independence is maintained, but integration efficiency and workflow optimization decrease
Solution Approach 1:
The system merges previously siloed data platforms and application platforms into an integrated workflow. The AI agent bridges the gap between data sources and end applications, enabling seamless data retrieval, processing, and presentation within a unified system that optimizes overall workflow efficiency.
Data Source
AI summary
Implementations described herein relate to systems and methods to generate a response to a business intelligence question received from a user. In some implementations, a computer-implemented method may include receiving the question as a natural language string, determining one or more fragments based on the natural language string, identifying one or more query operators based on the one or more fragments, constructing a structured query tree based on the one or more query operators, executing at least a portion of the structured query tree on a data source, receiving, from the data source, an output result based on the execution, generating the response based on the output result, and providing the response to the user.