Examiner Daniel T Pellett has allowed 405 of 506 decided applications (80%) in Computer Architecture, Software, and Information Security.
Daniel T Pellett maintains a public record across 3 art units within Technology Center 2100 (Computer Architecture, Software, and Information Security). Over 506 decided applications, his allowance rate is 80%, with 405 allowed and 101 abandoned. The allowance rate varies across art units, ranging from 73% to 85%. This pooled figure reflects his aggregate performance across all three art units and does not represent the outcome of any individual application or art unit.
A pooled record aggregates performance across multiple art units, masking variation between them. The overall allowance rate of 80% describes past disposition patterns and is not a prediction about any specific application. Art-unit-level records, available separately, show where rates differ. Pooled figures are most useful for understanding broad patterns; individual art-unit data provides finer detail for applications in particular art units.
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 160 decided applications with an interview and 161 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 68 decided applications with an interview and 110 without.
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 Daniel T Pellett 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: 528 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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