Chatbot Socialization for Cross-Domain Knowledge Sharing
Solution Overview
Problem
Existing chatbots are often specialized and narrowly focused on specific communication channels or domains, limiting their ability to handle interactions outside their trained scope, necessitating improved methods for utilizing and training chatbots across multiple channels and domains.
Innovation Solution
The system allows multiple chatbots from different communication channels and domains to socialize and assist each other during interactions, enabling them to share knowledge and resources for more comprehensive responses and facilitating automated training across channels and domains.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If chatbots are trained using manual inputs or inputs from a common channel to achieve high specialization, then the chatbot becomes highly trained and specialized for that particular channel or domain, but the chatbot becomes too narrowly focused and cannot handle communications outside its trained scope
Solution Approach 1:
The patent creates a universal training framework where chatbots are trained on aggregated data from multiple communication channels and domains. The system collects inputs from various channels (social media, customer service, messaging apps) and domains, then trains a single chatbot to handle diverse communication types, making it multi-functional rather than specialized for one channel.
Solution Approach 2:
The patent merges training data from multiple sources by aggregating inputs from different communication channels and domains into a unified training dataset. This combination allows the chatbot to learn patterns across diverse interactions, resolving the contradiction between specialization and versatility.
2Measurement precision
If multiple chatbots are used to handle different channels and domains, then each chatbot can be highly specialized, but the system complexity increases and coordination between chatbots becomes challenging
Solution Approach 1:
Instead of deploying multiple specialized chatbots, the patent trains a single universal chatbot capable of handling multiple channels and domains. This approach maintains high specialization accuracy while avoiding the complexity of coordinating multiple separate chatbot systems.
Solution Approach 2:
The patent combines the functionality of multiple specialized chatbots into a single multi-functional chatbot by aggregating training data from various sources. This merger eliminates the need for complex inter-chatbot coordination while preserving domain-specific expertise.
3Reliability
If chatbots are trained on channel-specific data to maintain high performance, then the chatbot achieves better performance on that channel, but the training time and resources increase when deploying across multiple channels
Solution Approach 1:
The patent performs preliminary aggregation of training data from multiple channels and domains before training the chatbot. By preparing a comprehensive training dataset in advance that includes diverse communication patterns, the chatbot can be trained once to handle multiple channels, reducing overall training time compared to training separate chatbots for each channel.
Solution Approach 2:
The patent merges training efforts by using a unified training process that incorporates data from multiple channels simultaneously. This approach maintains channel-specific performance while reducing total training time by avoiding repeated training cycles for each individual channel.
Data Source
AI summary
Embodiments of the disclosure provide systems and methods for utilizing chatbots to support interactions with human users and more particularly to using multiple chatbots from different communication channels and/or in different domains to support an interaction with a user on a single communication channel and/or in a single domain. Generally speaking, embodiments of the present disclosure are directed to allowing multiple chatbots that operate on different communication channels and/or in different domains to socialize amongst one another. This socialization of chatbots operating on different communication channels and/or in different domains allows each chatbot to call upon one another to help engage in a transaction with a customer. In addition to facilitating a communication session in which two or more chatbots are socialized together to help prepare coherent responses to user inputs, the socialization of chatbots can also facilitate the automated training of chatbots.