Automatic Data Insight Recognition System
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Solution Overview
Problem
Manual configuration of data analysis tools is inadequate for large or complex data sets, requiring users to have extensive knowledge and effort to identify insights such as trends, correlations, and patterns, especially when dealing with data from external sources.
Innovation Solution
Implementing a system that automatically recognizes and presents insights through statistical, heuristic, and comparative analyses, using machine learning algorithms to optimize visualizations and data formatting, allowing users to explore further without pre-selecting data or defining analysis parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual configuration of data analysis tools is used, then users can analyze data with existing tools, but users require extensive knowledge and effort to identify insights
Solution Approach 1:
The system performs self-service by automatically analyzing data without requiring user configuration. The patent implements automatic data set identification, automatic selection of analysis tools, and automatic generation of visualizations, allowing the system to serve itself rather than requiring manual user intervention for each analysis task.
Solution Approach 2:
The system performs preliminary actions by pre-configuring analysis parameters and automatically selecting appropriate analysis tools before the user initiates the analysis. The patent describes automatic inference of analysis parameters based on data characteristics, which prepares the analysis environment in advance without requiring user setup time.
2Quantity of substance
If manual configuration based tools are used for large data sets, then tools may be adequate for small data, but tools become inadequate or unusable for large data sets
Solution Approach 1:
The system automatically adjusts analysis parameters based on the characteristics and size of the data set. The patent implements automatic inference of analysis parameters that adapt to different data volumes and types, allowing the same tool to effectively handle both small and large data sets without manual reconfiguration.
Solution Approach 2:
The system dynamically adapts its behavior based on data characteristics. The patent describes automatic selection of analysis tools and methods based on the inferred nature of the data, making the system flexible and adaptable rather than static and rigid, which enables it to handle varying data sizes effectively.
3Extent of automation
If users pre-select data and define analysis parameters, then analysis can be performed with existing tools, but users need general understanding of data and extensive knowledge
Solution Approach 1:
The patent introduces an automatic data set identification system as an intermediary between the raw data and the analysis tools. This intermediary layer automatically infers data set boundaries and characteristics, translating raw data into a format suitable for analysis without requiring user interpretation or manual configuration.
Solution Approach 2:
The system replaces manual mechanical processes of data selection and parameter configuration with automated computational processes. The patent implements machine learning and automatic inference algorithms that substitute for the manual cognitive and operational processes previously required, reducing user burden while maintaining analysis quality.
4Measurement precision
If manual analysis is performed on small amounts of data, then configuration is not daunting, but results provide less accurate snapshot of overall story
Solution Approach 1:
The system implements a universal analysis framework that can handle both small and large data sets with the same automated process. The patent describes a unified system that automatically adapts to different data volumes, providing consistently accurate results whether analyzing small or large data sets, eliminating the need for different approaches for different data sizes.
Data Source
AI summary
Automatic recognition and presentation of insights of data is provided through analysis of overall data to infer locations of a user's data. Statistical, heuristic, and comparable analysis on the user's data sets is used to determine insights such as trends, correlations, outliers, comparisons, and patterns. The insights are then presented to the user through automatically optimized visualizations (highlighting determined insights), emphasis on presented raw data, data formatting suggestions, and similar ones with the capability to explore further.