View source for 18242719. Systems and Methods for Creating an Interactive Knowledge Base Using Interactive Chat Machine Learning Models simplified abstract (State Farm Mutual Automobile Insurance Company)

=Systems and Methods for Creating an Interactive Knowledge Base Using Interactive Chat Machine Learning Models=


==Organization Name==

[[:Category:State Farm Mutual Automobile Insurance Company|State Farm Mutual Automobile Insurance Company]]


[[Category:State Farm Mutual Automobile Insurance Company]]

==Inventor(s)==

[[:Category:Matthew Mifflin of Bloomington IL (US)|Matthew Mifflin of Bloomington IL (US)]][[Category:Matthew Mifflin of Bloomington IL (US)]]

==Systems and Methods for Creating an Interactive Knowledge Base Using Interactive Chat Machine Learning Models - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18242719 titled 'Systems and Methods for Creating an Interactive Knowledge Base Using Interactive Chat Machine Learning Models

The abstract describes systems and methods for creating an interactive knowledge base using a machine learning chatbot trained on data from a communication system.

* Training module connects to a communication channel to aggregate data from communications.
* Machine learning chatbot is trained with this data to generate responses to hypothetical prompts.
* Chatbot is then connected to live communication channels to provide predicted responses to live prompts.

Potential Applications:
- Customer service automation
- Knowledge base creation and maintenance
- Interactive virtual assistants

Problems Solved:
- Streamlining communication processes
- Improving response accuracy and efficiency
- Enhancing user experience

Benefits:
- Increased efficiency in handling customer queries
- Consistent and accurate responses
- Scalability for handling large volumes of communications

Commercial Applications:
- Customer support services
- E-commerce platforms
- Online education platforms

Prior Art:
Prior art related to this technology may include research on machine learning chatbots in communication systems and knowledge base management.

Frequently Updated Research:
Stay updated on advancements in machine learning algorithms for chatbots, natural language processing technologies, and communication system integrations.

Questions about Interactive Knowledge Base Systems:
1. How does this technology improve customer service interactions?
2. What are the key features that differentiate this interactive knowledge base system from traditional chatbots?

A relevant generic question not answered by the article could be: How does this technology impact job roles in customer service industries?
This technology can potentially lead to the automation of repetitive tasks, allowing customer service agents to focus on more complex issues and provide higher-quality support to customers.

Another relevant generic question could be: What are the privacy implications of using machine learning chatbots in communication systems?
The use of machine learning chatbots raises concerns about data privacy and security, as sensitive information may be processed and stored by these systems. It is essential to implement robust data protection measures to address these concerns.


==Original Abstract Submitted==

Systems and methods for creating an interactive knowledge base are disclosed herein. An exemplary computer-implemented method may include connecting a training module configured to train a machine learning (ML) chatbot to a communication channel associated with the communication system. The exemplary method further includes aggregating, by executing the training module, a set of data corresponding to the communication system from a plurality of communications utilizing the communication channel. The exemplary method further includes training the ML chatbot with the set of data corresponding to the communication system as inputs to generate a plurality of training responses as outputs, wherein the plurality of training responses represent responses to hypothetical prompts related to the data corresponding to the communication system; and connecting the ML chatbot to one or more live communication channels of the communication system to generate predicted responses to live prompts received across the one or more live communication channels.



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