Systems and methods for continual updating of response generation by an artificial intelligence chatbot
Inventors
Ma, Weicheng • Cao, Kai • Pan, Bei • Chen, Lin • Li, Xiang
Assignees
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Publication Number
US-12314676-B2
Publication Date
2025-05-27
Expiration Date
Abstract
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.
Core Innovation
A Variational-sequence-to-sequence dialog generator (VSDG) for a chatbot receives an input query from a user via a user interface and calculates a response to the input query. The VSDG is stored in non-transitory memory of a processor communicatively coupled to the user interface, and the response is output to the input query.
The VSDG uses a variational autoencoder (VAE) combined with a generative adversarial network (GAN) to calculate the response. In the described approach, the response is generated through the neural network after converting the input query to vectors via the VAE.
The generated response is sent to a GAN of the VSDG to be evaluated, and the VSDG is updated based on the evaluation of the GAN. The evaluation may involve comparing the response to a seed answer from a training dataset, and the updating can involve adjusting algorithms used to train the neural network and updating the VAE using backpropagation.
Claims Coverage
The document provides three independent claims, including method and system claims. Across these claims, the coverage centers on a VSDG chatbot that combines a VAE with a GAN to generate and output responses to user input queries, with GAN-based evaluation and VSDG updating in one method claim and a system-level implementation in another independent claim.
Variational-sequence-to-sequence dialog generator with VAE and GAN response calculation
A method in which a VSDG of a chatbot receives an input query from a user via a user interface, calculates a response using a variational autoencoder combined with a generative adversarial network, and outputs the response to the input query.
GAN evaluation and VSDG updating loop for VAE-based response generation
A method in which the input query is converted to vectors via a variational autoencoder to generate a response by a neural network, the response is sent to a GAN of the VSDG to be evaluated, and the VSDG is updated based on the evaluation of the GAN.
Chatbot system with VSDG including VAE combined with GAN
A system for a chatbot that includes a user interface and a processor configured with a VSDG comprising a VAE combined with a GAN stored in non-transitory memory, where executing instructions causes the processor to receive an input query at the VSDG via the user interface, calculate a response via the VAE combined with the GAN of the VSDG, and output the response to the input query.
Overall, the independent claims cover response generation in a VSDG chatbot using a VAE combined with a GAN, including a GAN-based evaluation and VSDG update mechanism and a corresponding processor-based system implementation.
Stated Advantages
Documented Applications
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