Company Filing History:
Years Active: 2023-2025
Title: Saurabh Sanjay Deshpande: Innovator in Content Retrieval Processing
Introduction
Saurabh Sanjay Deshpande is an accomplished inventor based in Sammamish, WA (US). He has made significant contributions to the field of content retrieval processing, particularly in managing indexed data. With a total of 2 patents, his work focuses on improving the efficiency of data retrieval in large datasets.
Latest Patents
One of Saurabh's latest patents is centered on the management of indexed data to enhance content retrieval processing. This patent outlines processing operations that uniquely utilize indexing to improve content retrieval, especially when dealing with extensive data sets. The techniques described enable efficient content retrieval in scenarios involving multiple tenants of a data storage application or service. This innovation allows for the training of classifiers using relevant samples based on text searches in tenant-specific situations. It ensures accurate searching for content associated with various tenant accounts concurrently, even when millions of documents are involved. The patent also discusses the generation and management of exemplary data shards for scalable content retrieval processing, including the training of artificial intelligence classifiers and real-time query processing.
Career Highlights
Saurabh currently works at Microsoft Technology Licensing, LLC, where he continues to develop innovative solutions in data management and retrieval. His expertise in this area has positioned him as a valuable asset to his team and the broader technology community.
Collaborations
Saurabh has collaborated with notable colleagues, including Mina Mikhail and Matthew Francis Hurst, contributing to a dynamic and innovative work environment.
Conclusion
Saurabh Sanjay Deshpande's contributions to content retrieval processing demonstrate his commitment to innovation and efficiency in data management. His patents reflect a deep understanding of the challenges associated with large datasets and offer practical solutions for improving content retrieval.