Dubai Media City, United Arab Emirates

Saurabh Batra

This inventor holds 1 USPTO granted patent. Top assignee: Sas Institute Inc.. Active years: 2026.

IDiyas Innovation Intelligence. (2026). Inventor Profile: Saurabh Batra. Retrieved from https://idiyas.com/inventor/saurabh-batra-03iy6wu5

Data source: USPTO (United States Patent and Trademark Office) public patent records. Weekly synchronization. How IDiyas builds this profile

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Average Co-Inventor Count = 1.0


Company Filing History:


Years Active: 2026

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1 patent (USPTO):Explore Patents

Title: Saurabh Batra and Innovation in Machine Learning

Introduction

Saurabh Batra is an inventor associated with SAS Institute Inc. His work includes a patent on generating features for machine-learning models from unstructured data. The provided information lists his location as Dubai Media City, AE, and credits him with 1 patent.

Latest Patents

Batra’s listed patent is titled “Computing Devices, Systems, and Graphical User Interfaces for Feature Generation Using Unstructured Data.” It describes a computing system that receives character data stored across multiple documents, with documents relating to different entities. The system converts that character data into structured terms and stores those terms in separate memory elements. It then generates a machine-learning feature using terms from different memory blocks or by manipulating a structured term.

Career Highlights

The patent reflects work at the intersection of data processing and machine learning. Its approach aims to make information in unstructured documents usable as structured features for machine-learning models. Batra is associated with SAS Institute Inc., a company known for analytics and data-management technologies.

Collaborations

The available information connects Batra’s patent work with SAS Institute Inc. No additional collaborators or co-inventors were specified in the supplied details.

Conclusion

Saurabh Batra’s listed invention focuses on transforming unstructured document data into structured features for machine-learning systems, highlighting a practical contribution to data-driven innovation.

Profile summary based on public USPTO records.
Data Sources: USPTO Patent Grant XML, Patent Center, EPO & CIPO • Normalized by IDiyas Innovation Graph. Methodology & provenance architecturePlease report any incorrect information to [email protected]
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