Pune, India

Devavrat Shailesh Thosar


Average Co-Inventor Count = 6.0

ph-index = 1


Company Filing History:


Years Active: 2023

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1 patent (USPTO):Explore Patents

Title: Innovations by Devavrat Shailesh Thosar

Introduction

Devavrat Shailesh Thosar is an accomplished inventor based in Pune, India. He has made significant contributions to the field of data extraction through his innovative patent. His work addresses the complexities involved in extracting mentions of complex relation types from documents.

Latest Patents

Devavrat holds a patent titled "Extracting mentions of complex relation types from documents by using joint first and second RNN layers to determine sentence spans which correspond to relation mentions." This patent focuses on the challenges of extracting data from documents, which often vary in structure, content, and style across different geographies and functional areas. The invention utilizes a joint neural network model that includes two sequence labeling layers trained jointly to extract entity mentions and relation mentions effectively. The first RNN layer creates sentence embeddings for each sentence in the document and predicts entity mentions, while the second RNN layer predicts labels for each sentence span corresponding to a relation type. This innovative approach facilitates downstream processing by addressing gaps in the art of data extraction.

Career Highlights

Devavrat is currently employed at Tata Consultancy Services Limited, where he continues to apply his expertise in data extraction and machine learning. His work has been instrumental in advancing the capabilities of data processing technologies.

Collaborations

Some of his notable coworkers include Sachin Sharad Pawar and Nitin Vijaykumar Ramrakhiyani, who contribute to the collaborative environment at Tata Consultancy Services Limited.

Conclusion

Devavrat Shailesh Thosar's innovative patent and contributions to data extraction highlight his role as a significant inventor in the field. His work not only addresses existing challenges but also paves the way for future advancements in data processing technologies.

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