API Parameter Mapping Using NLP and GenAI for Router Integration
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Solution Overview
Problem
Manual processes for configuring API routers in complex environments are inefficient, leading to delays, high error rates, scalability issues, and increased costs due to the need for specialized developers to manually analyze and map disparate parameters, which are exacerbated by the heterogeneity of API designs and frequent updates.
Innovation Solution
A computer-implemented method using natural language processing (NLP) to compute similarity scores between ingress and egress API parameters, generating pseudo code via generative artificial intelligence (GenAI) for automated transformation logic, with manual processing as a fallback, and real-time execution to facilitate seamless interoperability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual processes are used to configure API routers and map parameters, then developers can handle complex heterogeneous API designs, but the process becomes labor-intensive, time-consuming, and error-prone
Solution Approach 1:
The patent replaces manual mechanical processes (developers manually inspecting API specifications and writing transformation rules) with an automated system using NLP models and GenAI. The system automatically computes similarity scores between API parameters, generates transformation logic, and compiles executable code, eliminating the need for manual intervention while maintaining high accuracy in parameter mapping.
Solution Approach 2:
The system enables self-service by allowing the API router to automatically discover and map parameters between ingress and egress APIs without human assistance. The automated pipeline includes similarity computation, transformation logic generation, and code compilation, creating a self-sufficient system that reduces dependency on specialized developer expertise.
2Ease of manufacture
If manual parameter mapping is performed by specialized developers, then complex transformations can be handled accurately, but development costs and operational complexity increase
Solution Approach 1:
The patent introduces an intermediary automated system that mediates between heterogeneous API specifications and the routing logic. This system uses NLP models to compute similarity scores and GenAI to generate transformation rules, acting as a bridge that simplifies the integration process while handling the complexity of parameter mapping automatically, thereby reducing the need for specialized developer expertise.
3Productivity
If automated methods are used for API parameter transformation, then deployment speed increases, but handling heterogeneous API designs and complex transformations becomes more difficult
Solution Approach 1:
The patent transforms the problem of handling diverse API formats by changing the parameters used for comparison. Instead of manually analyzing semantic meanings, the system computes similarity scores based on parameter definitions, data types, and structural characteristics. The GenAI model then generates transformation logic based on these scores, enabling automated handling of heterogeneous APIs while maintaining adaptability to diverse formats.
Data Source
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AI summary
A system and method for automated discovery and transformation of application programming interfaces (APIs) receive descriptions of an ingress API and multiple egress APIs. A natural language processing (NLP) model computes similarity scores (0-1 range) for parameter pairs by comparing definitions. A similarity matrix is constructed from these scores. Pairs exceeding a threshold (e.g., ≥0.5) are selected, and a generative artificial intelligence (GenAI) model generates pseudo code defining transformation logic for data mapping, optionally with regular expressions. Low-score pairs enable manual processing. The system includes processors and a non-transitory storage medium for execution, with an API router for routing using the logic. This hybrid approach reduces manual effort, errors, and time in API integration, enhancing scalability for diverse environments.