This inventor holds 1 USPTO granted patent. Top assignee: Microsoft Technology Licensing, LLC. Active years: 2025.
Company Filing History:
Years Active: 2025
Title: Innovations of Shuohang Wang in Natural Language Processing
Introduction
Shuohang Wang is an accomplished inventor based in Bellevue, WA (US). He has made significant contributions to the field of natural language processing, particularly through his innovative patent that enhances the operations of natural language generation systems.
Latest Patents
Shuohang Wang holds a patent titled "Natural language training and/or augmentation with large language models." This patent describes techniques that improve natural language generation systems by utilizing large language models for training and augmentation. In one example, the large language model processes a training dataset to produce a natural language output. The natural language generation system then analyzes both the training dataset and the output to generate a response that mimics the large language model's output. The large language model evaluates the system's output to iteratively enhance the quality of the generated responses. In another example, the large language model augments a smaller language model by retrieving external information to provide context and a language framework, thereby improving overall outputs.
Career Highlights
Shuohang Wang is currently associated with Microsoft Technology Licensing, LLC, where he continues to innovate in the realm of natural language processing. His work has positioned him as a key figure in the development of advanced language models.
Collaborations
Shuohang collaborates with talented individuals such as Nanshan Zeng and Yang Liu, contributing to a dynamic and innovative work environment.
Conclusion
Shuohang Wang's contributions to natural language processing through his patent demonstrate his expertise and commitment to advancing technology in this field. His work at Microsoft Technology Licensing, LLC, along with his collaborations, highlights the importance of innovation in enhancing natural language generation systems.
