Multi-tenant Chatbot Configuration via Neural Network Phrase Matching
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Solution Overview
Problem
Conventional multi-tenant systems require separate design and training for chatbots due to differing tenant-specific data schemas and features, leading to inefficiencies and the need for retraining when functionality changes.
Innovation Solution
A multi-tenant system configures tenant-specific chatbots using machine learning models that do not require tenant-specific training, employing a neural network to compare natural language requests with predefined example phrases, allowing for updates without retraining, and reusing the model across different tenants or evolving action sets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If separate chatbots are designed and trained for each tenant, then tenant-specific requirements are met, but development effort and time increase significantly
Solution Approach 1:
A single universal chatbot system is designed to serve multiple tenants with different data schemas and features. The system uses a common machine learning model that can be configured for different tenants without requiring separate training for each tenant, thereby reducing development effort while maintaining tenant-specific customization capabilities
Solution Approach 2:
The chatbot system allows configuration of different parameters and settings for each tenant without retraining the underlying machine learning model. The model remains unchanged while tenant-specific requirements are met through parameter configuration and data schema adaptation, significantly reducing development complexity
2Reliability
If chatbots are trained separately for each tenant, then tenant-specific functionality is optimized, but retraining is required when functionality changes
Solution Approach 1:
The machine learning model is trained once with comprehensive training data that covers various functionalities. This preliminary training enables the model to handle different tenant-specific functionalities without requiring retraining when new features are added or existing ones are modified, as the model already contains the necessary knowledge from the initial comprehensive training
Solution Approach 2:
When functionality changes for a tenant, the system can adapt by changing configuration parameters and data schemas rather than retraining the model. The underlying machine learning model remains stable and reusable, eliminating the need for time-consuming retraining operations while maintaining optimized tenant-specific functionality
3Ease of manufacture
If conventional chatbots use fixed predefined tasks, then implementation is simple, but the chatbot fails when system functionality is extended
Solution Approach 1:
The chatbot system uses a machine learning model that can adapt to new functionalities through parameter changes and configuration updates rather than requiring changes to the core model structure. This allows the system to maintain implementation simplicity while being capable of extending functionality to support new features and tenant requirements
Solution Approach 2:
The system transitions from a static predefined task approach to a dynamic machine learning-based approach. The machine learning model can learn and adapt to new functionalities as they are added to the system, enabling the chatbot to handle extended functionality without failing, while maintaining the simplicity of the underlying implementation architecture
Data Source
AI summary
A multi-tenant system performs custom configuration of a tenant-specific chatbot to process and act upon natural language requests. The multi-tenant system configures the tenant-specific chatbots without requiring tenant-specific training. The multi-tenant system providing a user interface for configuring a tenant-specific set of permitted actions. The multi-tenant system determines a set of example phrases for each of the selected permitted actions. The multi-tenant system receives a natural language request from a user and identifies the action that the user wants to perform. The multi-tenant system uses a neural network to compare the natural language request with example phrases to identify an example phrase that matches the natural language request. The multi-tenant system performs the action corresponding to the matching example phrase.