Pune, India

Karishma Agarwal

This inventor holds 1 USPTO granted patent. Top assignee: Integral Ad Science, Inc.. Active years: 2026.

IDiyas Innovation Intelligence. (2026). Inventor Profile: Karishma Agarwal. Retrieved from https://idiyas.com/inventor/karishma-agarwal

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

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Company Filing History:


Years Active: 2026

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1 patent (USPTO):Explore Patents

Title: Karishma Agarwal: Patent Innovation in Digital Advertising Context

Introduction

Karishma Agarwal is an inventor based in Pune, India, whose patent work addresses contextual risk assessment for digital advertising. She is associated with Integral Ad Science, Inc. Her listed patent portfolio includes 1 patent.

Latest Patents

Agarwal is named on “Methods, Systems, and Media for Providing Digital Advertisers with Improved Context for Dynamic Webpages.” The invention describes using machine learning to assess webpages and their associated URLs for advertising risks. Its methods evaluate potential cybersquatting, webpage sentiment and keywords, and sentiment risk scores, then adjust an aggregate risk score according to an advertiser’s risk tolerances. When the score falls within a specified range, the system can add the webpage or its domain to the advertiser’s exclusion list.

Career Highlights

Agarwal’s listed patent reflects work at the intersection of machine learning, webpage analysis, and digital advertising safety. The approach is designed to give advertisers more context for making decisions about where their ads may appear.

Collaborations

The provided information lists Vikram Gupta and Mahesh Nagargoje as Agarwal’s coworkers. The patent’s described system also incorporates advertiser-defined risk tolerances and approval lists to support contextual assessment.

Conclusion

Karishma Agarwal’s patent focuses on helping digital advertisers evaluate webpage context and manage placement risks through machine-learning-based analysis.

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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