Augmented Language Model API Orchestration for Real-Time Workflows
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Solution Overview
Problem
Natural Language Processing (NLP) systems lack real-time access, leading to suboptimal decision-making and inefficient automation due to reliance on outdated information, hindering their ability to adapt to changing conditions and user needs.
Innovation Solution
Employing a machine learning model with natural language processing capabilities to generate computer instructions based on user input, allowing for real-time task execution and automation by identifying task-specific applications and generating API calls for various computer tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If NLP systems rely on previously learned data without real-time access, then the system structure remains simple, but the decision quality and adaptability deteriorate
Solution Approach 1:
The patent introduces an API gateway as an intermediary component that enables NLP systems to access real-time application data without direct integration. The gateway mediates between the NLP system and external applications, providing structured access to real-time information while maintaining system simplicity. This resolves the contradiction by enabling reliable real-time decisions without requiring complex direct integrations.
Solution Approach 2:
The system architecture is segmented into distinct modular components: NLP processing module, API gateway, and application execution environment. This segmentation allows each component to operate independently with well-defined interfaces, enabling real-time data access while maintaining overall system simplicity. The modular structure resolves the contradiction by organizing complexity into manageable segments.
2Productivity
If NLP systems lack real-time access to applications, then the system remains simple to implement, but automation capability and productivity deteriorate
Solution Approach 1:
The API gateway implements a universal interface that can interact with multiple different applications through standardized protocols. This multi-functionality enables the NLP system to automate tasks across diverse applications without requiring application-specific integration logic for each case, thereby improving productivity while controlling integration complexity.
Solution Approach 2:
The API gateway serves as a universal mediator that handles the complexity of application-specific protocols and interfaces. By centralizing integration logic in the gateway, the system achieves high automation capability across multiple applications while the NLP system itself remains relatively simple to implement.
3Adaptability or versatility
If NLP systems use outdated information, then data processing requirements remain low, but the ability to adapt to changing conditions deteriorates
Solution Approach 1:
The system implements dynamic data access where the NLP system can query applications in real-time based on current conditions and user needs. The API gateway enables dynamic retrieval of up-to-date information, allowing the system to adapt to changing conditions while maintaining a relatively simple core architecture through on-demand data access.
Solution Approach 2:
The system incorporates feedback loops where real-time application data is retrieved, processed by the NLP system, and used to generate updated responses or actions. This feedback mechanism enables adaptability to changing conditions by continuously incorporating current information, while the structured feedback path through the API gateway keeps the architecture manageable.
Data Source
AI summary
A method for performing computer-based tasks using computer applications and a machine learning model (MLM) includes identifying, within input data, a set of requests for performing corresponding computer-based tasks, utilizing the MLM. This identification process includes determining a computer application for executing the request and determining a programming instruction. The programming instruction includes a call to the computer application using an application programming interface (API) associated with the computer application. For the set of requests, the method includes generating computer instructions outlining a sequence for executing a set of programming instructions. Each instruction corresponds to the determined programming instruction and is configured with the associated API to accept input parameters generated from identified information within the input data, output parameters from the execution of any of the set of programming instructions, or a combination thereof. Finally, the method involves processing the computer instructions, leading to the generation of output data.