API Integration Framework for Natural Language Models

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Solution Overview

Problem

Conventional natural language models are limited in their ability to access and utilize external data and applications, leading to high training costs and inefficiencies due to outdated training data, and they struggle with scalability and integration with external tools.

Innovation Solution

Integrating external application programming interfaces (APIs) with natural language models, allowing them to access and interact with external data and applications through a user interface, using a manifest stored with the API that describes its functionality, enabling the model to perform tasks without user intervention.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If natural language models are trained with extensive training data to improve their functionality, then the model's capabilities are enhanced, but the training cost and time increase significantly

Engineering Contradiction:

Improvemodel capabilitiesVSAvoidtraining time

Core Design Contradiction:

Adaptability or versatilityVSLoss of time

Solution Approach 1:

The system performs preliminary actions by pre-defining API schemas, data structures, and integration frameworks before actual model deployment. This allows the model to leverage pre-prepared external tools and data sources, reducing the need for extensive retraining when new functionalities are required.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent introduces an intermediary layer (API integration framework) between the natural language model and external data sources. This mediator handles data retrieval, transformation, and integration tasks, allowing the model to access current external information without requiring retraining, thus resolving the contradiction between model adaptability and training time.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Adaptability or versatility

If natural language models are trained with extensive training data to improve their functionality, then the model's capabilities are enhanced, but the training cost increases

Engineering Contradiction:

Improvemodel capabilitiesVSAvoidtraining cost

Core Design Contradiction:

Adaptability or versatilityVSLoss of energy

Solution Approach 1:

The API integration framework serves as an intermediary that handles complex data processing and integration tasks externally, reducing the computational burden on the natural language model itself. This allows the model to maintain enhanced capabilities while reducing training costs by offloading intensive processing to specialized external systems.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system segments functionality by separating the natural language understanding capabilities from the data processing and integration tasks. Each component is optimized independently, allowing the model to be trained more efficiently while still providing comprehensive functionality through the coordinated work of multiple specialized components.

Inventive Principle:

Principle #1Segmentation

3Adaptability or versatility

If natural language models are designed to access external data sources, then the model's functionality is enhanced, but the system complexity increases

Engineering Contradiction:

Improveexternal data accessVSAvoidsystem complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent implements a universal API integration framework that can handle multiple types of external data sources and applications through a single standardized interface. This multi-functional approach allows the system to access diverse external data without requiring separate integration mechanisms for each source, thereby reducing overall system complexity while maintaining high adaptability.

Inventive Principle:

Principle #6Universality (Multi-functionality)

4Loss of energy

If natural language models use outdated training data, then training costs are reduced, but the accuracy and relevance of the model decrease

Engineering Contradiction:

Improvetraining costVSAvoidmodel accuracy

Core Design Contradiction:

Loss of energyVSMeasurement precision

Solution Approach 1:

The API integration framework acts as an intermediary that bridges the gap between static model training and dynamic external data. The model is trained on foundational data while the framework retrieves current, relevant information from external sources during operation, ensuring both cost-effectiveness and high accuracy without requiring continuous expensive retraining.

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS12124823B2Schema-based integration of external APIs with natural language applications

Publication Date: 2024.10.22 OPENAI OPCO LLC

AI summary

Disclosed herein are methods, systems, and computer-readable media for integrating a particular external application programming interface (API) with a natural language model user interface. In one embodiment, a method includes receiving a first input at the natural language model user interface, determining the first input includes a request to integrate the particular external application programming interface (API) with the natural language model user interface, identifying the particular external API based on the received input, integrating the particular external API with the natural language model user interface, accessing the particular external API based on the first input or a second input at the natural language model user interface, and transmitting, based on the accessing, a response message to the natural language model user interface, the response message including a result of the accessing.