Examiner Kamini S Shah has allowed 64 of 165 decided applications (39%) in Computer Architecture, Software, and Information Security.
Kamini S Shah maintains a public record across six art units within Technology Center 2100 (Computer Architecture, Software, and Information Security). Over 165 disposed applications, the examiner's allowance rate is 39%. The record spans art units 2115, 2123, 2127, 2128, 2142, and 2146. Allowance rates across these art units range from 3% to 90%, reflecting variation in outcomes by art unit. Of 184 total applications in the examiner's record, 64 were allowed and 101 were abandoned.
This record aggregates applications across multiple art units in TC 2100, pooling different technology areas into a single allowance rate. The 39% figure describes past dispositions and is not a prediction about any specific application. The range of 3% to 90% across art units shows that outcomes vary by art unit; the pooled rate reflects the combined history but does not indicate the rate applicable to any particular art unit or case.
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.
Primarily examines neural-network / biological-model computing, and machine learning.
Based on 34 applications — too small a sample to characterize the rejection mix reliably; shown for completeness.
Primarily examines control or regulating systems, and electric power networks.
Based on 29 applications — too small a sample to characterize the rejection mix reliably; shown for completeness.
Primarily examines artificial-intelligence and machine-learning methods.
Based on 29 applications — too small a sample to characterize the rejection mix reliably; shown for completeness.
Primarily examines machine learning, and neural-network / biological-model computing.
Based on 21 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 14 applications — too small a sample to characterize the rejection mix reliably; shown for completeness.
Methodology. This page pools every art unit in which Examiner Kamini S Shah 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: 184 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 →
ATTORNEY ADVERTISING — Sean Lynch, Partner, Lynch LLP