Automated Data Clustering System for Non-Expert Analysis

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Solution Overview

Problem

Current tools for understanding large datasets require significant linguistic background and training, making it difficult for users to analyze and leverage data effectively without extensive expertise.

Innovation Solution

A system and method for data clustering and organization that allows users to analyze large datasets by extracting features, removing irrelevant data, adding contextual features, and using pattern recognition to group data into meaningful buckets, enabling users to identify patterns and insights without extensive training.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If traditional data analysis tools are used, then data understanding capability is improved, but user expertise requirement increases

Engineering Contradiction:

Improvedata understanding capabilityVSAvoiduser expertise requirement

Core Design Contradiction:

Measurement precisionVSEase of operation

Solution Approach 1:

The patent introduces automated classification models and natural language processing tools as intermediaries between the user and the complex data analysis process. These tools translate user-friendly queries into sophisticated data operations, enabling non-experts to perform advanced data analysis without needing to understand the underlying complex algorithms and processes.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Measurement precision

If manual data analysis is performed, then analysis accuracy is improved, but time consumption increases

Engineering Contradiction:

Improveanalysis accuracyVSAvoidtime consumption

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent implements automated classification models that are pre-trained and ready to perform data analysis tasks. These models automatically process and categorize data before user review, performing preliminary analysis actions that would otherwise require manual effort. This preliminary automated processing maintains accuracy while significantly reducing the time users need to spend on data analysis.

Inventive Principle:

Principle #10Preliminary action

3Loss of information

If comprehensive data processing is applied, then data insight quality is improved, but computational complexity increases

Engineering Contradiction:

Improvedata insight qualityVSAvoidcomputational complexity

Core Design Contradiction:

Loss of informationVSDevice complexity

Solution Approach 1:

The patent segments the comprehensive data processing task into multiple automated classification stages and models. Each model handles specific aspects of data analysis, breaking down the complex computational process into manageable segments. This segmentation maintains thorough data processing and high-quality insights while reducing the apparent computational complexity for the user by automating each segment.

Inventive Principle:

Principle #1Segmentation

Data Source

PatentUS11537820B2Method and system for generating and correcting classification models

Publication Date: 2022.12.27 VERINT AMERICAS INC

AI summary

Data having some similarities and some dissimilarities may be clustered or grouped according to the similarities and dissimilarities. The data may be clustered using agglomerative clustering techniques. The clusters may be used as suggestions for generating groups where a user may demonstrate certain criteria for grouping. The system may learn from the criteria and extrapolate the groupings to readily sort data into appropriate groups. The system may be easily refined as the user gains an understanding of the data.