Natural Language Dashboard System for Automated Query Generation
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Solution Overview
Problem
Existing systems for generating and visualizing database queries in enterprise environments are inefficient, requiring significant time for data collection and preparation, and lack social networking capabilities, leading to slower development cycles and suboptimal use of computing resources.
Innovation Solution
A dynamic dashboard system that uses natural language conversations to generate structured database queries, provides interactive and adaptive visualization, and facilitates feedback loops among users to expedite data visualization and optimization, incorporating features like personalized insights and crowdsourced data representation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional dashboard generation processes are used with sequential iterative phases, then data visualization can be achieved, but development time is excessively long and BI analysts spend 80% of time on data collection and preparation
Solution Approach 1:
The patent replaces the manual mechanical process of data collection and query formulation with an automated AI system. The AI assistant automatically collects data, generates SQL queries, and creates visualizations based on natural language inputs, eliminating the need for analysts to manually perform these repetitive tasks and reducing development time significantly
Solution Approach 2:
The system enables self-service data visualization where users can directly interact with the AI assistant using natural language to generate dashboards without requiring extensive data preparation or technical expertise. The AI automatically handles data collection, processing, and visualization generation, allowing users to focus on insights rather than technical implementation
2Adaptability or versatility
If predefined KPIs and user-defined criteria are used for queries, then data retrieval can be performed, but the system lacks adaptability to user needs and does not provide social networking capability
Solution Approach 1:
The patent implements feedback mechanisms where user interactions with the AI assistant, including corrections to generated queries and preferences for visualizations, are continuously learned and used to improve future query generation. The system adapts to user needs over time by incorporating feedback loops that refine its understanding of user intentions and data requirements
Solution Approach 2:
The system provides multi-functionality by combining natural language processing, automated data collection, SQL query generation, and social networking capabilities into a single platform. Users can collaborate through shared dashboards, comment on visualizations, and benefit from collective insights, making the system versatile for both individual and team-based data analysis
3Productivity
If manual query generation and dashboard creation processes are used, then data visualization can be achieved, but computing resources are not optimized and development cycles are slow
Solution Approach 1:
The AI assistant performs preliminary actions by proactively collecting and preprocessing data before users request visualizations. The system continuously monitors data sources, updates datasets, and prepares queries in advance, so when users submit natural language requests, the heavy computational work has already been completed or is readily available, optimizing resource usage and reducing response time
Data Source
AI summary
Systems, apparatus, methods, and articles of manufacture provide for generation, execution, and visualization of data queries (e.g., SQL statements) and their results, based on natural language input from a user. In one example implementation, a dashboard system provides a voting lounge and personalized and crowdsourced dashboards.