Examiner Nupur Debnath has allowed 58 of 89 decided applications (65%) in Computer Architecture, Software, and Information Security.
Nupur Debnath maintains a public record across four art units within Technology Center 2100 (Computer Architecture, Software, and Information Security). Over 89 disposed applications, the allowance rate stands at 65%. This pooled figure reflects both allowed and abandoned applications decided by the examiner across these art units. The allowance rate ranges from 63% to 70% across the individual art units, reflecting variation in the examiner's record within TC 2100. The examiner's public record spans 115 total applications, of which 58 were allowed and 31 abandoned.
This profile aggregates the examiner's record across multiple art units in a single technology center. The pooled allowance rate describes past dispositions and does not predict outcomes in any specific application. When an examiner works across several art units, their aggregate rate can mask meaningful differences in allowance rates unit by unit. For detail on variation across individual art units, refer to the per-art-unit section. Pooled figures are historical measures only.
These are aggregate statistics from this examiner's past public record — not predictions about any specific application. The per-art-unit figures below show how the record varies across art units. Our approach to patent prosecution →
Each section benchmarks this examiner against that art unit's average. Figures are this examiner's own public record within the art unit; the overall rate above pools them.
Primarily examines neural-network / biological-model computing, and machine learning.
Based on 46 applications — too small a sample to characterize the rejection mix reliably; shown for completeness.
Primarily examines computer-aided design (CAD).
Based on 38 applications — too small a sample to characterize the rejection mix reliably; shown for completeness.
Primarily examines computer-aided design (CAD).
Based on 24 applications — too small a sample to characterize the rejection mix reliably; shown for completeness.
Primarily examines neural-network / biological-model computing, and machine learning.
Based on 7 applications — too small a sample to characterize the rejection mix reliably; shown for completeness.
Methodology. This page pools every art unit in which Examiner Nupur Debnath has a public record within Technology Center 2100. Statistics are computed from publicly available USPTO records, refreshed on a recurring schedule. This page's data was last updated June 25, 2026. The overall allowance rate is total allowed divided by total decided applications (allowed plus abandoned) across all art units — not an average of the per-art-unit rates; pending applications are excluded. Figures are rounded for display. Pooled sample: 115 applications.
Lynch LLP is not affiliated with, endorsed by, or sponsored by the United States Patent and Trademark Office. Examiner statistics are derived from publicly available USPTO data.
These statistics describe past examiner behavior and do not predict the outcome of any particular application. Past results do not guarantee future outcomes. Where this page compares an examiner's allowance rate to an art-unit average, that comparison is a factual description of the public record, not a characterization of any individual examiner's conduct or competence.
This page is for general informational purposes and is not legal advice. No attorney-client relationship is formed by viewing it. Full disclaimers →
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