API Parameter Mapping Using NLP and GenAI for Router Configuration
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Solution Overview
Problem
Current manual processes for configuring API routers in complex environments are labor-intensive, error-prone, and inefficient, leading to delays, scalability issues, and increased operational costs, particularly in dynamic and heterogeneous API ecosystems.
Innovation Solution
An automated method using natural language processing (NLP) and generative artificial intelligence (GenAI) to compute similarity scores for API parameter pairs, generate pseudo code for transformation logic, and execute it within an API router, with manual intervention for low-score pairs, facilitating hybrid automated-manual handling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual processes are used to configure API routers and map payload parameters, then developers can handle complex transformations with domain knowledge, 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, identifying matches, and defining transformation rules) with an automated system using NLP models and GenAI models. The NLP model computes similarity scores between ingress and egress API parameters, and the GenAI model generates transformation pseudo-code automatically, eliminating the need for manual domain knowledge application while maintaining mapping accuracy.
Solution Approach 2:
The system enables self-service by allowing the automated discovery and transformation logic generation to operate without continuous human intervention. The NLP model autonomously compares parameter definitions and computes similarity scores, while the GenAI model independently generates transformation pseudo-code based on the similarity matrix, making the system self-sufficient for routine API integration tasks.
2Manufacturing precision
If manual transformation of payload parameters is performed, then developers can ensure data accuracy, but human oversight leads to errors such as incorrect data conversions and runtime failures
Solution Approach 1:
The patent replaces human manual transformation processes with automated NLP and GenAI models. The NLP model precisely compares parameter definitions to compute similarity scores, and the GenAI model generates transformation pseudo-code that accurately converts data between different formats (e.g., time duration in seconds to expiry datetime formats), eliminating human oversight errors while maintaining transformation precision.
Solution Approach 2:
The system incorporates feedback mechanisms where the NLP model's similarity scores guide the transformation process, and the generated pseudo-code can be validated against the original API specifications. This feedback loop ensures that transformations maintain data accuracy while the automated process prevents the propagation of human errors.
3Adaptability or versatility
If the volume of APIs scales to hundreds in enterprise settings, then comprehensive coverage is achieved, but manual configuration delays compound and organizational agility is hindered
Solution Approach 1:
The patent implements a universal automated system that can handle diverse API types (RESTful, GraphQL, SOAP) and various parameter transformations simultaneously. The NLP model and GenAI model work together to process multiple ingress and egress API pairs in parallel, generating transformation logic for the entire API ecosystem rather than handling each API individually, thereby maintaining versatility while dramatically improving deployment speed.
Solution Approach 2:
The system performs preliminary action by pre-computing the similarity matrix between all ingress and egress API parameters before actual API integration is needed. The NLP model analyzes parameter definitions in advance to generate similarity scores, and the GenAI model prepares transformation pseudo-code beforehand, so that when APIs need to be integrated, the work is already completed, enabling rapid deployment even as the API volume scales to hundreds.
4Adaptability or versatility
If frequent updates to API specifications are made to adapt to evolving business needs, then system adaptability is improved, but manual reconfiguration is required and performance degrades over time
Solution Approach 1:
The patent implements a dynamic system where the NLP model continuously monitors API specification changes and automatically re-computes similarity scores when updates occur. The GenAI model dynamically generates new transformation pseudo-code based on the updated specifications, allowing the system to adapt to evolving business needs in real-time without manual reconfiguration, maintaining both adaptability and performance over time.
5Adaptability or versatility
If custom adaptations are created for each API integration, then specific requirements are met, but resource strain increases and operational costs rise
Solution Approach 1:
The patent creates a universal transformation engine that handles diverse API integration requirements through a single automated system. The NLP model and GenAI model work together to generate appropriate transformation logic for different API types and parameter formats without requiring separate custom adaptations for each integration, reducing resource consumption while maintaining the ability to meet specific integration requirements through the same unified platform.
Data Source
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.