Data Aggregation Automation via Smart Adapters and Rules Engine
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Solution Overview
Problem
Existing systems in industries like healthcare struggle with integration and automation due to divergent business interests, architectures, and protocols, leading to inefficiencies and the need for human intervention.
Innovation Solution
A data aggregation and process automation system (DAPA) that uses smart adapters and a rules engine to facilitate data exchange and automation across different systems, including those on a permissioned blockchain network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If legacy systems with divergent architectures and protocols are used, then each organization can maintain its own tailored internal systems, but integration and cooperation between organizations becomes difficult and inefficient
Solution Approach 1:
The patent introduces a trust-agnostic network as an intermediary layer between legacy systems with divergent architectures. This network acts as a mediator that enables communication and data exchange between organizations without requiring them to modify their underlying legacy systems, thus resolving the integration difficulty while maintaining system complexity at each organization level
Solution Approach 2:
The system segments the integration challenge by creating separate trust-agnostic network interfaces at each organization boundary. Instead of attempting to unify all legacy systems into a single architecture, the patent segments the solution into distributed trust networks that can independently connect to different legacy systems, reducing the overall integration complexity
2Productivity
If legacy systems are translated and consolidated quickly, then benefits from trust-agnostic networks can be realized, but translation and consolidation processes become time-consuming and inefficient
Solution Approach 1:
The patent implements preliminary action by pre-configuring trust-agnostic network interfaces and establishing translation protocols before actual data exchange occurs. Organizations can prepare their legacy system connections in advance, so when data needs to be exchanged, the translation and consolidation processes are already in place, reducing real-time delays
Solution Approach 2:
The trust-agnostic network serves as a pre-established intermediary infrastructure that handles translation and consolidation automatically. Once the network is set up, it can rapidly translate between different legacy system formats without requiring time-consuming manual intervention, thus increasing data exchange speed while minimizing translation time
3Extent of automation
If human intervention is used in process automation, then complex decisions can be made with judgment and flexibility, but human error and inefficiency increase
Solution Approach 1:
The patent implements self-service automation where the trust-agnostic network automatically handles data translation, validation, and exchange between legacy systems without requiring human intervention. The system serves itself by maintaining its own protocols and interfaces, reducing human error while handling complex translation tasks that would otherwise require expert judgment
Data Source
AI summary
A system and method for data aggregation and process automation is disclosed. The method includes receiving a first data object from a first integration point through a first smart adapter, identifying an appropriate rules library from a plurality of rules libraries using a rules engine, the appropriate rules library being identified using the first data object, and applying the appropriate rules library through the rules engine. The rules are applied by instructing a transformation module to transform the first data object into a transformed data object, instructing a validation module to validate at least one of the first data object and the transformed data object, and instructing an aggregation module to perform a statistical analysis on one of the first data object and the transformed data object. Finally, the method includes sending the transformed data object to a second integration point associated with a second smart adapter.