Beijing, China

Xiaorui Wang

USPTO Granted Patents = 4 

Average Co-Inventor Count = 4.2

ph-index = 1

Forward Citations = 2(Granted Patents)


Company Filing History:


Years Active: 2022-2023

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4 patents (USPTO):Explore Patents

Title: Innovations of Xiaorui Wang in Automatic Speech Recognition

Introduction

Xiaorui Wang is a prominent inventor based in Beijing, China. He has made significant contributions to the field of automatic speech recognition, holding a total of four patents. His work focuses on enhancing the efficiency and effectiveness of speech recognition systems through innovative methods and technologies.

Latest Patents

Xiaorui Wang's latest patents include systems and methods for automatic speech recognition based on graphics processing units. One of his notable inventions is an automatic speech recognition system that comprises an encoder and a decoder. The encoder consists of multiple layers, with at least one layer featuring several sublayers fused into encoder kernels. This system utilizes a pair of ping-pong buffers to facilitate communication between the encoder kernels. Additionally, another patent describes a conformer encoder that executes a series of layers using graphic processing units, incorporating modules such as feed forward, multi-head self-attention, and convolution.

Career Highlights

Throughout his career, Xiaorui Wang has worked with notable companies, including Kwai Inc. and Beijing Dajia Internet Information Technology Co., Ltd. His experience in these organizations has allowed him to develop and refine his innovative approaches to speech recognition technology.

Collaborations

Xiaorui Wang has collaborated with talented individuals in his field, including Qiang Li and Yongxiong Ren. These partnerships have contributed to the advancement of his research and the successful development of his patented technologies.

Conclusion

Xiaorui Wang's contributions to automatic speech recognition demonstrate his innovative spirit and dedication to advancing technology. His patents reflect a deep understanding of complex systems and a commitment to improving user experiences in speech recognition applications.

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