Cognitive Automation Engine for Cross-System Data Propagation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Repetitive data entry across multiple systems is time-consuming, prone to errors, and inefficient, leading to resource wastage and data inconsistency, as employees manually input data into various systems like CRM and ERP, causing computational and network resource overload.
Innovation Solution
A cognitive automation-based platform utilizing a client-server architecture that parses, normalizes, and validates data for real-time propagation across systems, eliminating the need for manual entry by breaking data into smaller packets and distributing it efficiently through a private network, ensuring data consistency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual data entry is performed across multiple systems, then data can be entered into each system individually, but employee time and productivity are significantly reduced
Solution Approach 1:
The patent segments the data entry process by identifying a single source system and multiple target systems, then automatically propagating data from the source to all targets. This eliminates the need for employees to manually enter data into each system separately, thereby resolving the contradiction between ease of operation and productivity.
Solution Approach 2:
The patent introduces an intermediary mechanism (automatic data propagation system) that mediates between the source system and multiple target systems. This intermediary automatically transfers and reconciles data, eliminating manual intervention and significantly improving employee productivity while maintaining ease of operation.
2Ease of operation
If data is manually entered into multiple systems, then each system can be updated individually, but data inconsistency and errors increase
Solution Approach 1:
The patent segments the data management architecture into a source system and multiple target systems, with automatic propagation mechanisms ensuring that data entered once in the source system is automatically and consistently distributed to all target systems. This segmentation approach maintains data consistency while preserving ease of operation.
Solution Approach 2:
The patent implements feedback mechanisms through data reconciliation processes that verify data consistency across systems. The system automatically detects and resolves conflicts, ensuring data reliability while maintaining the simplicity of the update process.
3Reliability
If computational resources are used to search and reconcile data across disparate systems, then data accuracy can be improved, but resource consumption and processing time increase
Solution Approach 1:
The patent applies preliminary action by establishing data propagation rules and relationships in advance between the source system and target systems. This pre-configuration eliminates the need for exhaustive searches and complex reconciliation processes, thereby maintaining data accuracy while significantly reducing computational resource consumption.
Solution Approach 2:
The patent introduces an intermediary data propagation system that automatically manages data transfer and reconciliation based on pre-defined rules. This intermediary eliminates the need for resource-intensive exhaustive searches across disparate systems, maintaining data accuracy while optimizing resource usage.
4Ease of manufacture
If employees perform repetitive data entry tasks, then data can be populated in multiple systems, but time and business resources are wasted
Solution Approach 1:
The patent segments the data population process by identifying a single source of truth and automatically propagating data to multiple target systems. This segmentation eliminates repetitive manual data entry tasks, significantly reducing time loss while maintaining the ease of populating multiple systems.
Solution Approach 2:
The patent implements self-service through automatic data propagation mechanisms that eliminate the need for employee intervention in data entry tasks. The system automatically populates data across multiple systems, reducing time loss while maintaining ease of operation.
Data Source
AI summary
Aspects of the disclosure relate to cognitive automation-based engine processing to propagate data across multiple systems via a private network to overcome technical system, resource consumption, and architecture limitations. Data to be propagated can be manually input or extracted from a digital file. The data can be parsed by analyzing for correct syntax, normalized into first through sixth normal forms, segmented into packets for efficient data transmission, validated to ensure that the data satisfies defined formats and input criteria, and distributed into a plurality of data stores coupled to the private network, thereby propagating data without repetitive manual entry. The data may also be enriched by, for example, correcting for any errors or linking with other potentially related data. Based on data enrichment, recommendations of additional target(s) for propagation of data can be identified. Reports may also be generated. The cognitive automation may be performed in real-time to expedite processing.