Examiner David H Tan has allowed 34 of 102 decided applications (33%) in Computer Architecture, Software, and Information Security.
David H Tan maintains a public record across 6 art units within Technology Center 2100 (Computer Architecture, Software, and Information Security). Over 102 disposed applications, the examiner's allowance rate stands at 33%. This reflects 34 allowed applications against 68 abandonments. The examiner's allowance rate ranges from 29% to 34% across these art units, indicating variation in outcomes by subject area within the technology center. This pooled record aggregates activity across multiple art units and does not predict outcomes in any individual application.
A pooled, cross-art-unit record combines statistics from multiple art units into a single aggregate profile. The figures—allowance rate, application counts, and the range across units—describe the examiner's past decisions across different subject areas within TC 2100. The aggregate allowance rate is a historical snapshot, not a prediction for any pending or future application. The range reflects natural variation among different art units and does not indicate performance quality.
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 artificial-intelligence and machine-learning methods.
Based on 48 applications — too small a sample to characterize the rejection mix reliably; shown for completeness.
Based on 11 applications — too small a sample to characterize the rejection mix reliably; shown for completeness.
Primarily examines artificial-intelligence and machine-learning methods.
Based on 9 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 1 applications — too small a sample to characterize the rejection mix reliably; shown for completeness.
Based on 1 applications — too small a sample to characterize the rejection mix reliably; shown for completeness.
Methodology. This page pools every art unit in which Examiner David H Tan 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: 150 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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