Natural Language GUI for Multi-Cloud Data Management
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Solution Overview
Problem
Managing complex data across multiple cloud platforms is challenging due to proprietary tools, steep learning curves, and the need for specialized queries, leading to inefficient data analysis and visualization.
Innovation Solution
An interactive graphical user interface (GUI) driven by natural language processing that standardizes data formats and allows users to query and visualize data from multiple cloud sources using intuitive controls and follow-up queries, enabling easy exploration and comparison of data sets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If proprietary vendor tools are used for cloud data management, then data can be accessed from specific cloud platforms, but the system complexity increases and requires specialized knowledge
Solution Approach 1:
The patent introduces a natural language processing intermediary layer that sits between the user and multiple cloud vendor tools. This intermediary translates natural language queries into vendor-specific queries, allowing users to access data from AWS, Azure, GCP, and other cloud platforms through a single unified interface without needing to learn each vendor's proprietary tools and syntax
Solution Approach 2:
The system provides universal data access capabilities across multiple cloud vendors through a single platform. The natural language processing engine can handle queries against data from different cloud sources (AWS S3, Azure Blob Storage, GCP Cloud Storage, etc.) using consistent interaction patterns, making the system adaptable to various cloud environments without requiring separate specialized tools for each vendor
2Measurement precision
If specialized query languages are used for data retrieval, then precise data can be obtained, but the ease of operation decreases
Solution Approach 1:
The patent replaces the mechanical system of learning and typing specialized query languages (SQL, vendor-specific query syntax) with a natural language processing system. Users can ask questions in plain English or other natural languages, and the system automatically translates these into precise vendor-specific queries, maintaining data retrieval precision while dramatically improving ease of operation
Solution Approach 2:
The natural language processing engine serves as an intermediary that translates between natural language (easy to use) and specialized query languages (precise). This mediator layer preserves the precision of specialized queries while eliminating the need for users to learn complex syntax, allowing precise data retrieval through simple natural language interactions
3Adaptability or versatility
If multiple cloud vendors are integrated, then data versatility increases, but the difficulty of detecting and measuring increases
Solution Approach 1:
The system introduces a natural language processing intermediary that handles the complexity of multiple cloud vendor data formats. This intermediary automatically adapts queries and results across different vendors (AWS, Azure, GCP, etc.), translating between their proprietary formats and a unified representation, thereby maintaining data versatility while reducing the difficulty of detecting and measuring through consistent natural language interactions
Solution Approach 2:
The system creates homogeneity in the user experience and data representation layer by using natural language as the universal interface. While underlying data formats from different cloud vendors remain heterogeneous, the natural language processing layer presents a homogeneous, consistent interface for querying and analyzing data from any vendor, making it easier to detect and measure across diverse sources
4Reliability
If traditional analytical tools are used, then data analysis can be performed, but productivity decreases due to fragmented workflows
Solution Approach 1:
The patent merges multiple separate analytical tools and workflows into a single unified natural language interface. Instead of requiring users to switch between different cloud vendor tools, charting tools, and analysis platforms, the system combines these functions into one integrated system that handles data retrieval, analysis, and visualization through natural language interactions, thereby improving productivity while maintaining analytical capability
Solution Approach 2:
The system provides universal data analysis capabilities that work across multiple cloud vendors through a single platform. The natural language processing engine can perform various analytical tasks (data retrieval, filtering, aggregation, visualization) regardless of the underlying data source, creating a multi-functional system that improves workflow efficiency while maintaining reliable data analysis capability
Data Source
AI summary
The technology disclosed provides an interactive GUI driven by natural language questions and intuitive controls that support follow-up queries. One features a table-graph that links responsive series of data to graph elements. Individual rows of data in the table can be selected or deselected for display. The rows can be displayed in a single graph for individual graphs. Averages and other statistical measures can be calculated and graphed responsive to selectable controls, without formulas for series calculations. Another feature is so-called Liveboards that include multiple natural language questions and data views produced from executing queries derived from the questions, adapted to data available to a particular user, especially when the user's organization is different from an origin organization that generated the Liveboard.