Predictive Mapped Formatting for Structured Data Inconsistency

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Solution Overview

Problem

Traditional data processing engines face challenges in handling disparate data formats, leading to inefficient data aggregation, resource-intensive formatting, and inaccurate data ingestion due to the need for manual conversion and repeated external calls, which hinder collaboration and computing resources.

Innovation Solution

An intelligent data processing pipeline using machine learning to cleanse and format data, identifying format inconsistencies, and generating predictive mapped formats for improved data quality in downstream systems.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If traditional data processing engines enforce common data schemes, then data aggregation is achieved, but error rates increase and computing resources are consumed

Engineering Contradiction:

Improvedata aggregation efficiencyVSAvoiddata formatting accuracy

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The system enables self-service data formatting by allowing the data processing engine to automatically learn and adapt to different data formats from external platforms. The engine ingests sample data, learns format patterns independently, and applies learned formats to subsequent data ingestion tasks without requiring manual intervention or enforcement of common schemes, thereby reducing errors while maintaining aggregation capability

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system dynamically changes formatting parameters by learning from historical data formats. Instead of using fixed common data schemes, the engine adjusts formatting parameters based on learned patterns from multiple external platforms, enabling adaptive formatting that reduces errors while maintaining productivity in data aggregation

Inventive Principle:

Principle #35Parameter changes

2Reliability

If manual data conversion operations are performed, then data format consistency is achieved, but time consumption and resource usage increase

Engineering Contradiction:

Improvedata format consistencyVSAvoiddata ingestion time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system performs preliminary learning actions by ingesting sample data from external platforms before actual data aggregation tasks. The engine learns format patterns in advance and stores them for future use, so that when real data needs to be aggregated, the formatting is already prepared and can be applied automatically, eliminating time-consuming manual conversion operations

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system replaces manual mechanical data conversion operations with an automated machine learning-based formatting system. The engine automatically learns and applies data formats without human intervention, substituting the manual conversion process with an intelligent automated system that maintains format consistency while significantly reducing time consumption

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Productivity

If repeated calls to external platforms are made for data extraction, then data aggregation is achieved, but computing resources are consumed

Engineering Contradiction:

Improvedata aggregation capabilityVSAvoidcomputing resource consumption

Core Design Contradiction:

ProductivityVSUse of energy by moving object

Solution Approach 1:

The system creates local copies of external platform data formats by learning and storing format patterns from sample data. Instead of repeatedly calling external platforms to extract and re-format data, the engine uses learned format copies to automatically format incoming data, maintaining data aggregation capability while significantly reducing computing resource consumption from repeated external calls

Inventive Principle:

Principle #26Copying

4Manufacturing precision

If user input is required for data formatting, then formatting accuracy is improved, but operational complexity increases

Engineering Contradiction:

Improvedata formatting accuracyVSAvoiddata processing simplicity

Core Design Contradiction:

Manufacturing precisionVSEase of operation

Solution Approach 1:

The system provides self-service data formatting by automatically learning format patterns from external platforms and applying them without requiring user input. The engine independently handles format learning and application, eliminating the need for manual user intervention while maintaining high formatting accuracy through its learned patterns

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system uses feedback from learned data formats to improve formatting accuracy. By continuously learning from external platform data and comparing learned patterns with actual data formats, the engine refines its formatting capabilities automatically, achieving high accuracy without requiring user input or increasing operational complexity

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS12443619B1Predictive mapped formatting for data related to a format inconsistency error via machine learning

Publication Date: 2025.10.14 OPTUM INC

AI summary

Various embodiments of the present disclosure provide predictive mapped formatting for data. The techniques may include receiving an input structured data object, identifying a format inconsistency error for the input structured data object, generating a predictive mapped format data object for an input data format of the input structured data object by using a predictive machine learning model, initiating a presentation of a validation user interface that reflects the predictive mapped format data object, and storing the predictive mapped format data object in response to a confirmation input to the validation user interface.