Company Filing History:
Years Active: 2021-2023
Title: Dhiresh Rawal: Innovator in Organizational-Based Language Models
Introduction
Dhiresh Rawal is a prominent inventor based in Redmond, WA (US). He has made significant contributions to the field of language processing through his innovative patents. With a total of 2 patents, Rawal is recognized for his work in developing systems and methods that enhance organizational-based language models.
Latest Patents
Dhiresh Rawal's latest patents focus on organizational-based language model generation. The first patent outlines systems and methods for acquiring training data and building an organizational-based language model based on this data. It describes how organizational data is generated through various applications associated with an organization. The collected data is then aggregated and filtered into training data, which is utilized for training a language model aimed at improving speech processing. The second patent similarly details a system and method for acquiring training data and constructing an organizational-based language model. This method includes collecting organizational data, aggregating it with previously collected data, and training a language model for speech processing based on the aggregated data.
Career Highlights
Dhiresh Rawal is currently employed at Microsoft Technology Licensing, LLC, where he continues to innovate in the field of language processing. His work has been instrumental in advancing the capabilities of organizational-based language models, making significant strides in how organizations can utilize language technology.
Collaborations
Some of Dhiresh Rawal's notable coworkers include Ziad Al Bawab and Anand U Desai. Their collaborative efforts contribute to the innovative environment at Microsoft Technology Licensing, LLC.
Conclusion
Dhiresh Rawal's contributions to organizational-based language models highlight his expertise and commitment to innovation in technology. His patents reflect a deep understanding of the intersection between organizational data and language processing, paving the way for future advancements in the field.