NLP Platform Modular Architecture for Multi-Channel Integration
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional chatbots have a closed architecture, making them difficult to extend or integrate with other natural language processing (NLP) components, resulting in variable user experiences across different interaction channels and limited flexibility in handling multi-channel conversations.
Innovation Solution
An artificial intelligence natural language processing platform with a loosely coupled architecture that allows plug-and-play connectivity with various NLP engines, providing dynamic content integration and holistic context using customer conversation history across channels, enabling design-time configuration of dialogue flows and API connections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a closed architecture is used for chatbots, then the system structure is simple and easy to implement, but the system becomes difficult to extend or integrate with other NLP components
Solution Approach 1:
The chatbot system is divided into independent modular components including NLP engines, dialogue management modules, and integration adapters. Each component can be developed, tested, and deployed independently while maintaining clear interfaces for communication, enabling both simple implementation of individual modules and flexible extension of the overall system.
Solution Approach 2:
The platform implements a universal architecture with standardized interfaces and protocols that allow different NLP components to work together through common communication mechanisms. This enables the system to integrate multiple NLP engines and adapt to various interaction channels while maintaining a consistent core structure.
2Ease of manufacture
If traditional chatbot architecture is used, then the system is simple to deploy, but the user experience varies across different interaction channels
Solution Approach 1:
The platform implements a universal architecture with standardized interfaces and protocols that allow different NLP components to work together through common communication mechanisms. This enables the system to integrate multiple NLP engines and adapt to various interaction channels while maintaining a consistent core structure.
Solution Approach 2:
Integration adapters serve as intermediary components between different interaction channels and the core chatbot system. These adapters translate channel-specific protocols into standardized internal formats, ensuring consistent user experience across websites, IVR systems, SMS, and other channels while maintaining ease of deployment.
3Ease of repair
If a closed chatbot system is used, then the system is easy to maintain, but the flexibility in handling multi-channel conversations is limited
Solution Approach 1:
The chatbot system is divided into independent modular components including NLP engines, dialogue management modules, and integration adapters. Each component can be developed, tested, and deployed independently while maintaining clear interfaces for communication, enabling both simple implementation of individual modules and flexible extension of the overall system.
Solution Approach 2:
Integration adapters serve as intermediary components between different interaction channels and the core chatbot system. These adapters translate channel-specific protocols into standardized internal formats, ensuring consistent user experience across websites, IVR systems, SMS, and other channels while maintaining ease of deployment.
4Productivity
If design-time configuration is implemented, then the processing speed is enhanced, but the initial system setup becomes more complex
Solution Approach 1:
The system performs configuration, compilation, and optimization of dialogue flows and NLP component connections during design time before deployment. This preliminary action prepares the system in advance, enabling faster processing during runtime while the initial setup complexity is confined to the configuration phase rather than affecting operational performance.
Data Source
AI summary
Systems and techniques for an artificial intelligence natural language processing platform are described herein. A first dialogue configuration ruleset may be obtained for a first intent for a first entity. First user input may be received that includes a first text string from a chatbot session. The first intent may be identified from the chatbot session through evaluation of the first text string by a natural language processing engine using the first dialogue configuration ruleset. A dialogue stack may be generated for the chatbot session that includes a first state based on the first dialogue configuration ruleset. A first response to the first user input may be displayed based on the first dialogue configuration ruleset. The dialogue stack may be updated based on the first response.