Natural Language to API Conversion via Modular Neural Decoder
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Solution Overview
Problem
Natural language interfaces struggle to effectively convert user utterances into API calls due to brittleness in rule-based models and the 'black box' problem of statistical approaches, which limits their flexibility and user interaction efficiency.
Innovation Solution
A trained machine learning model using a sequence-to-sequence architecture with specialized decoder modules that break down natural language input into manageable prediction units, allowing for user verification and correction, and enabling interaction with both natural language-enabled and non-enabled programs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If rule-based models are used for natural language to API conversion, then the system provides interpretability and controllability, but the system suffers from brittleness and inability to handle diverse natural language variations
Solution Approach 1:
The patent segments the natural language processing task into multiple specialized decoder modules, each responsible for predicting specific API frame items (e.g., endpoint, parameters, authentication). This segmentation allows the system to handle diverse natural language variations through specialized modules while maintaining overall reliability through structured assembly of predictions into a coherent API frame.
2Adaptability or versatility
If statistical approaches are used for natural language to API conversion, then the system handles natural language variations better, but the system becomes a black box with reduced user interaction efficiency
Solution Approach 1:
The patent introduces an API frame as an intermediary representation between natural language input and API call output. The API frame serves as a structured intermediate format that maintains interpretability while enabling flexible statistical processing. This intermediary structure allows users to verify and understand the conversion process while still benefiting from the adaptability of machine learning models.
3Device complexity
If a unified decoder is used for natural language to API conversion, then the system structure is simpler, but the system lacks user verification and correction capabilities
Solution Approach 1:
The patent divides the decoder into multiple specialized modules, each handling specific API frame items. This segmentation enables user verification and correction by allowing users to interact with and validate individual predictions (such as endpoint or parameters) separately, improving ease of operation while maintaining a manageable system structure through modular design.
4Ease of manufacture
If traditional natural language interfaces are used, then the system is easier to implement, but the system cannot effectively control non-natural language enabled programs
Solution Approach 1:
The patent creates a universal natural language interface that can control both natural language-enabled and non-enabled programs through API integration. The system translates natural language into standardized API frames that can interface with any program having an API, providing multi-functionality and broad adaptability while maintaining relatively simple implementation through the structured conversion process.
Data Source
AI summary
Representative embodiments disclose mechanisms to map natural language input to an application programming interface (API) call. The natural language input is first mapped to an API frame, which is a representation of the API call without any API call formatting. The mapping from natural language input to API frame is performed using a trained sequence to sequence neural model. The sequence to sequence neural model is decomposed into small prediction units called modules. Each module is highly specialized at predicting a pre-defined kind of sequence output. The output of the modules can be displayed in an interactive user interface that allows the user to add, remove, and/or modify the output of the individual modules. The user input can be used as further training data. The API frame is mapped to an API call using a deterministic mapping.