Customized AI Chatbots Through Configuration Without Model Retraining
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Solution Overview
Problem
Existing AI/ML models, such as large language models, require extensive training data and computing resources and are limited in customization, leading to generalized responses that may be inaccurate or inconsistent, lacking the ability to be tailored for specific contexts or uses.
Innovation Solution
A computer system provides an interface for administrators to customize interactive applications like chatbots by specifying data sources, behavior, and appearance without re-training AI/ML models, combining AI/ML models with non-AI/ML data processing systems to generate reliable and accurate responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If AI/ML models are extensively trained with large amounts of training data and computing power to improve capability, then the model's general capability is improved, but the customization capability for specific uses or contexts deteriorates
Solution Approach 1:
The patent divides the chatbot system into two independent components: a pre-trained foundation model and a configurable application layer. The foundation model handles general language understanding, while the application layer handles customization through configuration files that define data sources, response formats, and behavior parameters. This segmentation allows the model to maintain its general capability while enabling easy customization without retraining.
Solution Approach 2:
The patent enables customization by changing parameters in configuration files rather than modifying the trained model weights. Administrators can adjust parameters such as data source selections, response templates, access control settings, and behavior parameters to tailor the chatbot's behavior for specific uses or contexts, achieving adaptability without affecting the model's core capabilities.
2Device complexity
If standardized AI/ML models are used to reduce complexity and resource requirements, then ease of deployment is improved, but the ability to be customized for particular uses or contexts deteriorates
Solution Approach 1:
The patent uses configuration files as templates that define the behavior and parameters of chatbot applications. These configuration files serve as blueprints that can be copied, modified, and distributed without requiring model training or complex deployment procedures. The configuration files contain all necessary parameters for customization, making it easy to deploy customized chatbots across different environments.
3Adaptability or versatility
If administrators can fully customize chatbot behavior and data sources, then adaptability is improved, but the interface complexity and time required for customization increase
Solution Approach 1:
The patent creates a universal configuration file format that can define multiple aspects of chatbot behavior (data sources, response formats, access control, parameters) in a single standardized structure. This universal configuration approach allows administrators to customize various aspects of the chatbot using the same interface and methodology, reducing the learning curve and simplifying the customization process while maintaining high adaptability.
4Adaptability or versatility
If multiple customized chatbots are deployed for different data sets and users, then adaptability is improved, but the resource consumption and costs increase
Solution Approach 1:
The patent enables a single foundation model to serve multiple customized chatbot applications by loading different configuration files. Each configuration file defines a specific chatbot's behavior, data sources, and parameters. The system can switch between different chatbot configurations without requiring separate model instances, thereby reducing computing resource consumption and costs while maintaining the ability to deploy multiple customized chatbots for different data sets and users.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for creating and distributing customized artificial intelligence chatbots. In some implementations, a system provides an interface for creating or editing an interactive application configured to provide responses generated using one or more artificial intelligence (AI) or machine learning models. The system receives customization data through the interface, where the customization data indicates customizations specified by a user to customize the interactive application. The system stores one or more records specifying configuration settings representing the customizations for the interactive application. The system provides one or more users access to the interactive application with the customizations.