AI Dialog Bot Training System Using Dynamic Tree Generation
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Solution Overview
Problem
Conventional dialog bot systems require manual creation and updating of static dialog trees, lack a standardized framework for reuse across domains, and result in inefficient development efforts due to the need for customizations, leading to reduced productivity and increased costs.
Innovation Solution
An AI-based interactive dialog training and communications system that uses example-based creation and construction, allowing editors to interact with dialog bots dynamically, leveraging natural language processing and machine learning to build, train, and maintain dialog bots, enabling real-time updates and efficient reuse across various domains.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If manual curation of pre-canned scripts and dialog trees is used, then dialog bots can be created, but the scripts become static and cumbersome, requiring deep domain knowledge and increasing development complexity
Solution Approach 1:
The patent replaces the mechanical system of manual dialog tree construction with an AI-based natural language processing system. Instead of editors manually creating static dialog trees, the system uses machine learning models to automatically generate and update dialog structures from conversational data, eliminating the need for deep domain knowledge and reducing complexity
Solution Approach 2:
The patent transforms static dialog trees into dynamic, adaptive conversation structures. The dialog bot continuously learns from interactions and automatically updates its conversation flow, allowing the system to adapt to new scenarios without manual reconfiguration of the entire dialog tree
2Reliability
If custom dialog trees are created for each implementation, then specific domain requirements are met, but development efforts cannot be reused across domains, reducing productivity
Solution Approach 1:
The patent creates a universal dialog bot platform that can serve multiple domains through a single standardized framework. The system uses domain-agnostic natural language processing capabilities that can be applied across different industries and use cases, eliminating the need to create separate custom dialog trees for each implementation
Solution Approach 2:
The patent performs preliminary training of the dialog bot on diverse conversational data from multiple domains during the development phase. This pre-training enables the system to handle various domain-specific requirements without requiring custom dialog tree creation for each new implementation, significantly improving development efficiency
3Device complexity
If static dialog scripts are used, then development is simpler, but the dialog bots cannot adapt to real-world conversational variations, reducing their effectiveness
Solution Approach 1:
The patent enables the dialog bot to self-update and self-improve through continuous learning from conversational interactions. The system automatically analyzes user inputs, identifies new patterns and intents, and updates its conversation handling capabilities without requiring manual script revisions, maintaining both simplicity and adaptability
Data Source
AI summary
A system for training and deploying an artificial conversational entity using an artificial intelligence (AI) based communications system is disclosed. The system may comprise a memory storing machine readable instructions. The system may also comprise a processor to execute the machine readable instructions to receive a request via an artificial conversational entity. The processor may also transmit a response to the request based on a dialog tree generated from at least a model-based action generator and a memory-based action generator. The processor may further provide a training option to a user in the event the response is suboptimal. The processor may additionally receive a selection from the user via the training option. The selection may be associated with an optimal response.