AI Application Integration Subsystem for Automated Data Mapping
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Solution Overview
Problem
Conventional methods for integrating data between applications are time-consuming, error-prone, and require significant human intervention and technical expertise, involving costly software installations and manual programming.
Innovation Solution
An automated system utilizing a hardware processor with memory and multiple subsystems, including user command capturing, application integration, artificial intelligence-based Natural Language Processing for attribute mapping, and machine learning for exception handling, to establish and manage data transmission channels between applications with minimal human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional manual programming methods are used for data integration, then integration accuracy can be maintained, but development time increases significantly and error probability increases
Solution Approach 1:
The system performs self-service by automatically generating integration code through AI/ML algorithms. The automated code generation subsystem analyzes source and target application attributes and autonomously produces integration code without human intervention, eliminating the time-consuming manual programming process while maintaining accuracy through systematic attribute mapping.
Solution Approach 2:
The patent replaces the mechanical manual programming process with an automated AI/ML-based code generation system. The machine learning models substitute human developers' cognitive and manual work, automatically analyzing application attributes and generating integration code, thereby reducing development time while preserving integration accuracy through algorithmic precision.
2Reliability
If expert technical manpower is deployed for integration, then integration quality can be ensured, but cost and time consumption increase
Solution Approach 1:
The system substitutes expert human technicians with an automated AI/ML-based integration platform. The machine learning models perform attribute analysis, code generation, and testing automatically, eliminating the need for expert manual intervention while maintaining integration quality through systematic processing and reducing time consumption through automation.
Solution Approach 2:
The patent introduces an intermediary automated code generation subsystem that mediates between source and target applications. This intermediary system uses AI/ML algorithms to analyze application attributes and generate integration code, replacing the need for expert human mediators while improving productivity through automated processing.
3Adaptability or versatility
If technical software components are installed on local machines and servers, then integration functionality can be achieved, but installation complexity and time consumption increase
Solution Approach 1:
The patent extracts the integration functionality from complex local software installations and consolidates it into a centralized automated code generation platform. The system retrieves necessary application attributes through automated interfaces and generates integration code without requiring technical software components to be installed on local machines or servers, thereby reducing installation complexity while maintaining integration functionality.
Solution Approach 2:
The automated code generation subsystem serves as a universal platform that handles multiple integration scenarios without requiring specific local software installations. The system performs attribute analysis, code generation, and testing through a single multi-functional platform, eliminating the need for separate technical software components on different machines and reducing overall system complexity.
4Manufacturing precision
If manual data transportation methods are used, then data accuracy can be maintained, but time consumption and human intervention requirements increase
Solution Approach 1:
The system performs self-service by automatically generating and executing integration code through AI/ML algorithms. The automated code generation subsystem analyzes application attributes, generates appropriate integration code, and performs testing without human intervention, thereby reducing the extent of automation required from users while maintaining data accuracy through systematic processing.
Data Source
AI summary
A system to integrate data from one application to another application is disclosed. The plurality of subsystems includes a user command capturing subsystem, configured to capture commands from a user. The plurality of subsystems includes an application integration subsystem, configured to analyse the captured commands of the user corresponding to the source application and the target application based on a set of parameters. The application integration subsystem obtains a source response and a target response and extract source application attributes and target application attributes. The plurality of subsystems includes an application attribute mapping subsystem, configured to generate an application attribute map from the source application attributes and the target application attributes by implementation of artificial intelligence-based Natural Language Processing (NLP). The application attribute mapping subsystem establishes a data transmission channel between the source application and the target application and transmit data through the data transmission channel.