Examiner Tan V Mai has allowed 1,872 of 2,086 decided applications (90%) in Computer Architecture, Software, and Information Security.
Examiner Tan V Mai maintains a public record across five art units in Technology Center 2100 (Computer Architecture, Software, and Information Security). Over 2,086 disposed applications, the examiner has allowed 1,872 and abandoned 214, yielding an allowance rate of 90%. The allowance rate ranges from 85% to 95% across the examiner's art units, reflecting variation in the record within the technology center. This pooled figure aggregates outcomes across all five art units and describes the historical record only.
A pooled record combines data across multiple art units into a single aggregate profile. The overall allowance rate of 90% reflects past dispositions across all five art units together and is not a prediction of any specific application's outcome. Similarly, the 85%–95% range shows where individual art units fall but does not indicate which unit will handle a particular case or what that case will receive. Pooled figures are historical summaries, not forecasts.
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 software engineering, and error detection, correction, and monitoring.
Allowance rate for applications with an examiner interview versus without one.
A correlation, not proof that interviews cause allowances. Based on 83 decided applications with an interview and 908 without.
Primarily examines data-processing methods for specific functions, and processing data by its order or content.
Allowance rate for applications with an examiner interview versus without one.
A correlation, not proof that interviews cause allowances. Based on 132 decided applications with an interview and 736 without.
Primarily examines neural-network / biological-model computing, and machine learning.
Primarily examines program control and execution.
Primarily examines neural-network / biological-model computing, and machine learning.
Methodology. This page pools every art unit in which Examiner Tan V Mai 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: 2,086 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.
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