Examiner Sivalingam Sivanesan has allowed 241 of 308 decided applications in Computer Architecture, Software, and Information Security.
Examiner Sivalingam Sivanesan maintains a pooled allowance rate of 78% across hundreds of decided applications in Technology Center 2100 (Computer Architecture, Software, and Information Security). His record spans six art units within this technology center. Across these art units, allowance rates range from 72% to 81%, reflecting variation in the outcomes of decided applications by art unit. This pooled figure represents the share of allowed applications among all decided (allowed and abandoned) cases in his record, pooled across all art units served.
This pooled record aggregates examination outcomes across multiple art units within TC 2100. The allowance rate reflects past decisions on allowed and abandoned applications and is not a prediction of outcomes on any specific pending application. The range shown (72% to 81%) indicates variation across individual art units; the overall 78% is a combined figure across all units in the record. Pooled statistics describe historical patterns 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.
Grounds can co-occur, so the four don't sum to 100%. The art-unit figure is the unweighted mean across examiners in the art unit; §102 and §112 carry no art-unit benchmark.
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.
Grounds can co-occur, so the four don't sum to 100%. The art-unit figure is the unweighted mean across examiners in the art unit; §102 and §112 carry no art-unit benchmark.
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.
Grounds can co-occur, so the four don't sum to 100%. The art-unit figure is the unweighted mean across examiners in the art unit; §102 and §112 carry no art-unit benchmark.
Primarily examines neural-network / biological-model computing, and machine learning.
Grounds can co-occur, so the four don't sum to 100%. The art-unit figure is the unweighted mean across examiners in the art unit; §102 and §112 carry no art-unit benchmark.
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.
Grounds can co-occur, so the four don't sum to 100%. The art-unit figure is the unweighted mean across examiners in the art unit; §102 and §112 carry no art-unit benchmark.
Based on 19 applications — too small a sample to characterize the rejection mix reliably; shown for completeness.
Primarily examines control or regulating systems.
Grounds can co-occur, so the four don't sum to 100%. The art-unit figure is the unweighted mean across examiners in the art unit; §102 and §112 carry no art-unit benchmark.
Based on 8 applications — too small a sample to characterize the rejection mix reliably; shown for completeness.
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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 July 14, 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.
Rejection rates. Each §-rate is the share of this examiner's applications that drew at least one office-action rejection in which that statutory ground appears; applications with no rejection on record are excluded, and because grounds can co-occur the four do not sum to 100%. The art-unit figure beside each is the unweighted mean of the per-examiner rates across the art unit, published for §101 and §103 only. Beside the overall allowance rate we show a benchmark: for a single-art-unit examiner it is exactly that art unit's average, labeled “art-unit average”; for an examiner spanning several art units it is the “weighted peer average” — the per-art-unit averages, weighted by this examiner's application count in each — labeled distinctly because it is a blended figure, not any single art unit's average. Both are built from the same per-art-unit averages the panels show.
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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