Data Composition Optimization via Machine Learning Analysis
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Solution Overview
Problem
Existing methods for optimizing user perception of combined data in the internet and e-commerce spaces are subjective and lack an automated, objective technical approach, making it difficult to determine and enhance the impact of individual data subsets on user perception.
Innovation Solution
A system and method that utilize machine learning techniques to analyze user activity and behavior data across multiple nodes, combining data subsets to create optimized compositions by measuring interactions, time spent on data, and purchasing triggers, with AI training for unsupervised processing and feature importance calculation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional subjective methods (surveys, interviews) are used to optimize data composition, then user perception can be analyzed, but the process becomes tedious and expensive while still failing to accurately target appropriate users and obtain accurate information
Solution Approach 1:
The patent replaces manual, subjective analysis methods (surveys, interviews) with an automated machine learning system that objectively analyzes user interaction data. The system uses algorithms to process user behavior data from node systems, automatically determining which data subsets positively influence user perception without requiring human analysts to manually evaluate each combination.
Solution Approach 2:
The system enables self-service optimization by automatically analyzing user interaction data and generating optimized data compositions without requiring external human intervention. The machine learning model continuously learns from user behavior patterns and autonomously determines optimal data subset combinations, eliminating the need for repeated manual surveys and interviews.
2Loss of information
If multiple data subsets are combined to create composed data, then more comprehensive information is provided to users, but it becomes impossible or difficult to determine and optimize the influence of each individual data subset on user perception
Solution Approach 1:
The patent segments the composed data into individual data subsets and evaluates each subset's contribution separately. The system analyzes user interaction data to determine which specific subsets have positive influence on user perception, allowing identification of impactful individual components within the larger composed data structure.
Solution Approach 2:
The system changes parameters by evaluating different combinations and variations of data subsets systematically. By adjusting which subsets are included or excluded and analyzing user responses, the system identifies the optimal configuration that maximizes positive user perception while maintaining information completeness.
3Extent of automation
If automated machine learning methods are used to optimize data composition, then objective and robust optimization is achieved, but complex computational processing and training data requirements are introduced
Solution Approach 1:
The patent introduces a machine learning model as an intermediary between raw user interaction data and optimized data composition decisions. This intermediary processes the complex computational tasks of analyzing user behavior patterns and determining optimal data subset combinations, managing the system complexity while enabling high-level automation.
Data Source
AI summary
A method and a system for automatically optimizing a data composition can be configured to provide a plurality of data subsets, combine the data subsets to combined data, and analyze the combined data by variations of at least one data subset. In some aspects, the system and method include a plurality of nodes, wherein the analyzing of variations is performed at at least one node of a node system.