Conversation-Based AI Integrator for Heterogeneous Systems
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Solution Overview
Problem
Software integration of heterogeneous systems across different vendors, formats, and languages is complex, time-consuming, and often requires extensive human interaction, leading to inefficiencies and high costs due to repeated discovery and data mapping efforts for each client change.
Innovation Solution
A conversation-based artificial intelligence (AI) integrator facilitates integration by training AI integrators to communicate and map data requests and availability, generating source code to conform to application programming interface specifications, and deploying the necessary code after consensus is reached, reducing the need for human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional software integration methods are used between heterogeneous systems, then integration can be achieved, but the process becomes extremely complex and time-consuming requiring extensive human interaction
Solution Approach 1:
The patent replaces manual mechanical integration processes with an AI-based automated system. The AI integrator uses natural language processing and machine learning to automatically discover data requirements, map data between systems, and generate integration code, substituting the manual mechanical process of human developers with an intelligent automated system that handles complexity internally while providing simple interfaces to users
Solution Approach 2:
The AI integrator serves as an intermediary between heterogeneous systems, acting as a mediator that translates and adapts data between different formats, protocols, and languages. This intermediary automatically negotiates data mappings and generates integration logic, eliminating the need for direct complex point-to-point connections between disparate systems
2Measurement precision
If data discovery and mapping is performed manually for each client change, then accurate integration is achieved, but the process must be repeated time and time again increasing time consumption
Solution Approach 1:
The AI integrator performs preliminary actions by learning and storing data mapping patterns from initial integrations. When faced with new client changes or similar integration scenarios, the system retrieves and adapts previously learned mappings, performing the discovery and mapping work in advance rather than repeating it manually for each change
Solution Approach 2:
The system implements feedback loops where integration results and data mapping outcomes are continuously learned and stored. This feedback mechanism allows the AI to improve its mapping accuracy over time and automatically adjust to client changes by comparing new requirements against learned patterns, reducing the need for repetitive manual mapping
3Reliability
If multiple teams are involved in integration tasks, then comprehensive coverage is achieved, but the number of interactions increases making the process more complex
Solution Approach 1:
The patent merges multiple integration teams and their respective expertise into a single AI integrator system. The AI consolidates the knowledge and capabilities that would otherwise require multiple human teams, combining data discovery, mapping, code generation, and validation functions into one unified automated system that reduces inter-team interactions while maintaining comprehensive coverage
Data Source
AI summary
A method for integrating heterogeneous systems is presented including initiating a conversation between a consumer system having a first artificial intelligence (AI) integrator and a producer system having a second AI integrator, training the first and second AI integrators to enable the conversation between the consumer system and the producer system, upon a data request from a client, enabling the consumer system to receive an application programming interface specification (API Spec) and locate the producer system that provides service in conformance with the API Spec, determining missing data needed to conform to the API Spec to build source code, if the producer system determines that it can support the data request, triggering the producer system to generate a contract, and after consensus is provided, in a deployment phase, generating by the producer system the source code to provide information pertaining to the data request.