ML Ensemble Data Digitization for Heterogeneous Layout Mapping
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Heterogeneous computing systems face challenges in integrating data from various sources without excessive data transformations, leading to errors and high computational resource usage due to the large volume and variety of data formats.
Innovation Solution
A system utilizing trained ensembles of machine learning models to identify, extract, and map data sets into a compatible format for electronic transaction systems, reducing computational resources and errors through adaptive formatting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional data formatting methods are used to transform heterogeneous data sets, then data compatibility is improved, but computational resource usage increases and error rates increase
Solution Approach 1:
The patent replaces traditional mechanical data transformation methods (manual mapping, rule-based conversion) with machine learning ensembles that automatically learn and apply transformation patterns. The ML models analyze source data characteristics and generate appropriate transformation rules dynamically, substituting rigid mechanical formatting processes with adaptive intelligent systems that reduce computational overhead.
Solution Approach 2:
The system performs preliminary actions by pre-processing data sets through the ML ensembles before actual transformation. The ensembles analyze and categorize source data formats, identify transformation requirements, and prepare mapping strategies in advance, which streamlines the subsequent data conversion process and reduces overall computational resource consumption during production transformations.
2Adaptability or versatility
If traditional data formatting methods are used to transform heterogeneous data sets, then data compatibility is improved, but error rates increase
Solution Approach 1:
The patent implements feedback mechanisms where the ML ensembles continuously learn from transformation outcomes and error patterns. The system monitors transformation results, identifies errors, and uses this feedback to refine and update transformation models, thereby reducing error rates while maintaining adaptability to different data formats and sources.
Solution Approach 2:
The system dynamically adjusts transformation parameters based on the specific characteristics of each data set being processed. Rather than applying fixed transformation rules, the ML ensembles analyze source data parameters and automatically optimize transformation settings, which reduces errors caused by inappropriate or rigid parameter application across diverse data formats.
3Adaptability or versatility
If manual data formatting processes are used, then adaptability to new data types is improved, but processing time increases
Solution Approach 1:
The patent enables self-service through ML ensembles that automatically detect new data types, learn their characteristics, and generate appropriate transformation strategies without manual intervention. The system self-adapts to new data formats by analyzing samples and updating its transformation models autonomously, eliminating the need for manual configuration and significantly reducing onboarding time for new data sources.
Solution Approach 2:
The system performs preliminary learning and adaptation actions when new data types are encountered. The ML ensembles quickly analyze new data formats, extract patterns, and prepare transformation rules in advance, enabling rapid onboarding of new data sources without lengthy manual configuration processes.
4Adaptability or versatility
If extensive data transformations are applied to integrate heterogeneous systems, then data compatibility is improved, but system complexity increases
Solution Approach 1:
The patent introduces ML ensembles as intermediary components between heterogeneous data sources and the target system. These ensembles act as intelligent mediators that automatically analyze source data, determine appropriate transformations, and execute conversions, thereby reducing the complexity of direct system-to-system integration and simplifying the overall architecture.
Solution Approach 2:
The ML ensemble system provides universal transformation capabilities that can handle multiple data formats and sources through a single unified platform. Rather than implementing separate transformation logic for each data source, the ensembles offer multi-functional adaptation that reduces system complexity by consolidating diverse transformation requirements into one versatile system.
Data Source
AI summary
Data digitization via custom integrated machine learning ensembles is provided. For example, a system integrates multiple trained machine learning ensembles to identify, extract, and map data. The system receives a data set from sources. The system identifies ensembles can include machine learning models that can determine an outcome. The system filters a subset of data from the data set. The system identifies a layout for the data set based on a vendor type, data type, and the data set. The system executes a block detection module to identify blocks of the layout. The system executes a header detection module. The system executes a policy detection module to identify the headers as policies. The system transforms, based on the headers, the layout, the blocks, and the policies, the data set into a second file type, and presents the transformed data set for integration into a capital management system.