Interactive Dashboard Natural Language Query Processing
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Solution Overview
Problem
Conventional dashboards are limited to one-way communication, making it difficult for users to ask questions and receive actionable insights, leading to inefficient decision-making processes.
Innovation Solution
An interactive dashboard system utilizing ChatGPT-like Large Language Model (LLM) AI-based technology for natural language input and immediate intelligent responses, enabling two-way communication between users and the system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional dashboards are designed as static presentation tools, then data presentation capability is improved, but user interaction and question-answering capability deteriorate
Solution Approach 1:
The dashboard transitions from a static presentation tool to a dynamic interactive system that can process natural language queries and provide intelligent responses. The system dynamically adapts to user questions and modifies its output based on the interaction, resolving the contradiction between maintaining strong data presentation capabilities while enabling user interaction.
Solution Approach 2:
The system implements a feedback loop where user questions are processed through natural language interpretation, analytical reasoning, and the system responds with informed answers. This feedback mechanism enables two-way communication while preserving the dashboard's core data presentation functionality.
2Device complexity
If conventional dashboards use one-way communication, then system simplicity is improved, but decision-making efficiency deteriorates
Solution Approach 1:
The system introduces an intermediary natural language processing layer that translates user questions into analytical queries and converts system responses into natural language answers. This intermediary mechanism enables efficient two-way communication without requiring complex system restructuring, thus maintaining relative simplicity while improving decision-making efficiency.
Solution Approach 2:
The system replaces traditional mechanical interaction methods (buttons, menus, filters) with natural language processing. This substitution allows users to interact with the dashboard using conversational language, significantly improving decision-making efficiency while keeping the underlying system architecture relatively simple.
3Loss of information
If users resort to manual data analysis or support team assistance, then question-answering capability is improved, but time consumption increases
Solution Approach 1:
The dashboard enables self-service by automatically processing user questions through natural language interpretation and analytical reasoning. The system independently generates informed responses without requiring manual data analysis by users or intervention from support teams, thus maintaining strong question-answering capability while minimizing time consumption.
Solution Approach 2:
The system changes the parameter of response generation from manual processes to automated AI-driven processes. This parameter change enables the system to quickly process and answer user questions directly, eliminating the time-consuming manual analysis and support team assistance while maintaining high question-answering capability.
Data Source
AI summary
The present invention provides a method for providing analytics and intelligent question-answering via an interactive dashboard, which includes the following steps: receiving user input data via the interactive dashboard; identifying user intent from the user input data; converting the user intent into an application programming interface (API) of a local system for the local system to proceed; translating a response of the API into a natural language response; generating a revised interactive dashboard based on the user intent dynamically according to the response of the API; and presenting the natural language response via the revised interactive dashboard.