Data Segmentation Using Machine Learning Linear Regression
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Solution Overview
Problem
Current data segmentation processes are manual, slow, inefficient, and dependent on user judgment, lacking automation and predictive capabilities.
Innovation Solution
A data segmentation system using machine learning that employs linear regression to analyze parameter values, ranks parameters by signal strength, and applies weights to automatically segment data into categories, enabling predictive and automated data categorization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual data segmentation is performed by users, then the process allows for human judgment and flexibility, but the process is slow and inefficient
Solution Approach 1:
The system performs data segmentation automatically without requiring manual user intervention. The machine learning model independently analyzes data, selects parameters, and generates segments, enabling the system to serve itself rather than relying on human operators for each segmentation task.
Solution Approach 2:
The patent replaces the manual mechanical process of data segmentation with an automated machine learning system. The ML model substitutes human judgment and manual operations with algorithmic processing, dramatically increasing segmentation speed while maintaining intelligent decision-making capabilities.
2Reliability
If manual data segmentation is performed, then user judgment can be applied, but the process is dependent on individual user expertise
Solution Approach 1:
The machine learning model transforms the segmentation process by changing from manual parameter selection to automated parameter analysis. The system evaluates multiple parameters simultaneously using algorithms, converting subjective user judgment into objective, reproducible computational assessments that are not dependent on individual expertise.
Solution Approach 2:
The patent segments the complex segmentation process into distinct computational components: data preprocessing, parameter selection, model training, and segment generation. This modular approach simplifies the overall process while maintaining high reliability through specialized algorithms for each component.
3Productivity
If automated machine learning segmentation is implemented, then productivity and efficiency are enhanced, but the system complexity increases
Solution Approach 1:
The machine learning system performs multiple functions within a single unified framework: data preprocessing, parameter selection, model training, validation, and segment generation. This multi-functionality increases productivity while managing complexity by consolidating operations into an integrated system rather than separate manual processes.
Solution Approach 2:
The system incorporates feedback mechanisms where the ML model is trained on labeled data, generates predictions, and is validated against actual outcomes. This feedback loop continuously improves segmentation accuracy and maintains system reliability despite the increased complexity of automated processing.
Data Source
AI summary
Disclosed are systems, methods, and non-transitory computer-readable media for data segmentation using machine learning. A data segmentation system prioritizes parameters used for segmenting data into predetermined categories. For example, the data segmentation system uses linear regression to determine signal strength values for the individual parameters. The signal strength values can be used to automatically select a set of parameters for segmenting data, determine weights for the parameters and/or determine threshold segmentation values.