Examiner Sivalingam Sivanesan has allowed 241 of 308 decided applications (78%) in Computer Architecture, Software, and Information Security.
Sivalingam Sivanesan's public record spans 6 art units within Technology Center 2100 (Computer Architecture, Software, and Information Security). Over 308 disposed applications, the examiner allowed 241 and abandoned 67, for an overall allowance rate of 78%. This rate reflects decided cases only and does not include pending applications. The allowance rate across individual art units ranges from 72% to 81%, indicating variation in outcomes by art unit.
This pooled record aggregates data across multiple art units in TC 2100. The 78% allowance rate describes the examiner's historical record across all assigned art units combined and is not a prediction of any specific application's outcome. Aggregated figures mask differences between individual art units; applicants may wish to review the examiner's per-art-unit data for more granular information about their particular technology area.
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.
Allowance rate for applications with an examiner interview versus without one.
A correlation, not proof that interviews cause allowances. Based on 16 decided applications with an interview and 87 without.
Primarily examines neural-network / biological-model computing, and machine learning.
Allowance rate for applications with an examiner interview versus without one.
A correlation, not proof that interviews cause allowances. Based on 17 decided applications with an interview and 59 without.
Primarily examines neural-network / biological-model computing, and machine learning.
Primarily examines neural-network / biological-model computing, and machine learning.
Based on 29 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 19 applications — too small a sample to characterize the rejection mix reliably; shown for completeness.
Primarily examines control or regulating systems.
Based on 8 applications — too small a sample to characterize the rejection mix reliably; shown for completeness.
Methodology. This page pools every art unit in which Examiner Sivalingam Sivanesan 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: 308 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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