Examiner Daniel E Miller has allowed 24 of 57 decided applications (42%) in Computer Architecture, Software, and Information Security.
Daniel E Miller maintains a public record across 5 art units within Technology Center 2100 (Computer Architecture, Software, and Information Security). Over 57 disposed applications, the examiner's allowance rate is 42%, calculated from 24 allowed and 33 abandoned applications. The remaining 5 applications are pending and do not factor into the allowance rate. This pooled record aggregates outcomes across multiple art units and represents historical dispositions, not predictions about individual future cases.
A pooled record aggregates an examiner's outcomes across multiple art units, producing an overall allowance rate that describes past decisions in the aggregate. This figure reflects the examiner's historical pattern across TC 2100 and is not a prediction of any specific application's outcome. Individual art units may show variation; pooled statistics smooth those differences into a single historical summary.
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.
Based on 36 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 computer-aided design (CAD).
Based on 9 applications — too small a sample to characterize the rejection mix reliably; shown for completeness.
Primarily examines program control and execution.
Based on 5 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 3 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 E Miller 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: 62 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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