ML Data Correlation for Side-by-Side Service Comparison
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Solution Overview
Problem
The increase in variables among product and service offerings has led to confusion in the marketplace, making it difficult to make apples-to-apples comparisons due to differences in data formats, measurements, and field names across disparate data sources.
Innovation Solution
Utilizing a machine learning engine to identify correlations between data from different services by analyzing metadata and programming nomenclature, generating a graphical user interface for side-by-side comparison, and providing reasons for deviations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data from disparate sources with different formats, measurements, and field names are collected to provide custom-tailored service offerings, then the variety and customization of product/service offerings increase, but the difficulty of making accurate comparisons between products and services increases
Solution Approach 1:
The patent introduces a data normalization layer as an intermediary between disparate data sources and the comparison interface. This layer standardizes different data formats, measurements, and field names into a common structure, enabling accurate comparisons while preserving the ability to collect diverse customized offerings from multiple sources
Solution Approach 2:
The system dynamically transforms data parameters by mapping various data formats, measurements, and field names to standardized parameters. This parameter transformation enables consistent comparison across different data sources while maintaining the ability to accommodate diverse service offerings with varying characteristics
2Speed
If real-time data processing and comparison is implemented to provide immediate insights, then the responsiveness and user experience improve, but the computational load and system complexity increase
Solution Approach 1:
The patent implements preliminary data normalization and validation steps that prepare data in advance for comparison operations. By pre-processing and standardizing data before it reaches the comparison engine, the system reduces computational complexity during real-time operations while maintaining fast responsiveness
Solution Approach 2:
The system segments the data processing pipeline into distinct modular components: data collection, normalization, validation, and comparison. This segmentation allows each component to be optimized independently and processed in parallel, reducing overall system complexity while enabling real-time performance
3Reliability
If comprehensive data validation and correlation checks are performed to ensure data accuracy, then the reliability of comparison results improves, but the processing time and computational resources increase
Solution Approach 1:
The patent implements selective validation that performs comprehensive correlation checks only on critical data fields and basic validation on all fields. This partial action approach ensures reliability for the most important comparison criteria while reducing overall processing time by avoiding exhaustive validation on every data point
Data Source
AI summary
A data correlation and presentation system is provided herein. Machine learning is used to identify correlations in data received from disparate electronic services. Correlated data objects are generated based upon the identified correlations and the correlated data objects are provided in a combined characteristics output, enabling downstream reporting systems to report the correlations with very little processing resource utilization.