Gateway Service API Onboarding Engine
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Solution Overview
Problem
In large enterprise service systems, determining whether existing services or new services are needed to satisfy new requirements is complex and resource-intensive, often leading to inefficient utilization of computer resources and potential duplication of APIs.
Innovation Solution
The implementation of an API onboarding engine within the gateway service that uses machine learning algorithms to determine the necessary downstream services and APIs, optimizing resource utilization and reducing duplication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual determination of service dependencies is performed, then service requirements can be satisfied, but computer resource utilization becomes inefficient and time-consuming
Solution Approach 1:
The patent replaces manual mechanical analysis of service dependencies with automated machine learning algorithms. The ML model automatically analyzes service catalogs, API definitions, and data models to determine downstream service dependencies, eliminating the need for manual resource-intensive dependency determination while significantly improving speed and accuracy.
2Measurement precision
If comprehensive service analysis is performed to determine existing service capabilities, then accurate service matching is achieved, but computer resources are excessively consumed
Solution Approach 1:
The patent performs preliminary analysis by pre-processing and storing service catalog information, API definitions, and data models in structured formats. The machine learning model is pre-trained on service metadata, enabling rapid accurate matching without performing comprehensive real-time analysis of all services, thus reducing computational resource consumption while maintaining high accuracy.
3Adaptability or versatility
If existing services are thoroughly examined to satisfy new requirements, then API duplication is reduced, but the complexity of determining service capabilities increases
Solution Approach 1:
The patent introduces a machine learning model as an intermediary between service requirements and the service catalog. The ML model automatically analyzes service capabilities, API definitions, and data models to determine if existing services can satisfy new requirements, reducing API duplication. This intermediary simplifies the overall process by automating the complex analysis that would otherwise require manual examination of service capabilities.
Data Source
AI summary
Various techniques are disclosed for providing gateway services between a client system and downstream service systems for a service system. The disclosed gateway service system is capable of implementing a new service for a client system in response to a request from the client system. The gateway service system internally determines which downstream services are needed to implement the new service. In various instances, the gateway service system utilizes machine learning algorithms to determine the downstream services suitable for providing the output needed for the new service. The gateway service system is also capable of determining whether an existing application programming interface (API) is able to be used for the new service or whether a new API needs to be created for the new service. By internally determining the downstream services and APIs, the gateway service system has more efficient utilization of its computational resources.