20240046108. SYSTEMS AND METHODS FOR CONTINUAL UPDATING OF RESPONSE GENERATION BY AN ARTIFICIAL INTELLIGENCE CHATBOT simplified abstract (Cambia Health Solutions, Inc.)

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SYSTEMS AND METHODS FOR CONTINUAL UPDATING OF RESPONSE GENERATION BY AN ARTIFICIAL INTELLIGENCE CHATBOT

Organization Name

Cambia Health Solutions, Inc.

Inventor(s)

Weicheng Ma of Brooklyn NY (US)

Kai Cao of Seattle WA (US)

Bei Pan of Kirkland WA (US)

Lin Chen of Bellevue WA (US)

Xiang Li of Bellevue WA (US)

SYSTEMS AND METHODS FOR CONTINUAL UPDATING OF RESPONSE GENERATION BY AN ARTIFICIAL INTELLIGENCE CHATBOT - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240046108 titled 'SYSTEMS AND METHODS FOR CONTINUAL UPDATING OF RESPONSE GENERATION BY AN ARTIFICIAL INTELLIGENCE CHATBOT

Simplified Explanation

Methods and systems are provided for a variational-sequence-to-sequence dialog generator (VSDG) of a chatbot, which receives an input query and calculates a response using a variational autoencoder (VAE) combined with a generative adversarial network (GAN). The response generated by the VSDG can be in a dialog form. Additionally, the GAN evaluates the response to update the VSDG.

  • The patent application describes a system for generating dialog responses in a chatbot using a VSDG.
  • The VSDG receives an input query and uses a VAE combined with a GAN to calculate a response.
  • The response generated by the VSDG can be in a dialog form, allowing for more natural and interactive conversations.
  • The GAN evaluates the response to provide feedback and improve the performance of the VSDG.
  • The technology combines variational autoencoders and generative adversarial networks to enhance the capabilities of the chatbot.

Potential applications of this technology:

  • Chatbot systems and virtual assistants can benefit from more advanced and natural dialog generation.
  • Customer service chatbots can provide more accurate and helpful responses to user queries.
  • Language learning platforms can use this technology to create interactive conversational agents for practice and learning.
  • Virtual chat agents in gaming and entertainment applications can engage in more realistic and dynamic conversations with users.

Problems solved by this technology:

  • Traditional chatbots often struggle to generate coherent and contextually appropriate responses.
  • Generating dialog responses that mimic human-like conversation is challenging.
  • Feedback and evaluation of chatbot responses can be subjective and difficult to automate.
  • Improving the performance and user experience of chatbot systems requires advanced techniques for response generation.

Benefits of this technology:

  • Enhanced user experience with chatbots that can generate more natural and contextually relevant responses.
  • Improved accuracy and efficiency in providing information and assistance to users.
  • Increased engagement and interaction with chatbot systems, leading to better user satisfaction.
  • Continuous learning and improvement of chatbot performance through the evaluation and updating process.


Original Abstract Submitted

methods and systems are provided for receiving an input query at a variational-sequence-to-sequence dialog generator (vsdg) of a chatbot, and calculating, via a variational autoencoder (vae) combined with a generative adversarial network (gan) of the vsdg, a response to the input query. the response may be in a dialog form. further, in one or more examples, the gan evaluates the response for updating the vsdg.

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