Examiner Tsu-Chang Lee has allowed 362 of 483 decided applications (75%) in Computer Architecture, Software, and Information Security.
Examiner Tsu-Chang Lee has a pooled record of 547 total applications across 4 art units in Technology Center 2100 (Computer Architecture, Software, and Information Security). Of 483 disposed applications, 362 were allowed, yielding an overall allowance rate of 75%. The examiner's allowance rates across these art units range from 31% to 84%, reflecting variation in outcomes across different art-unit assignments. This pooled figure represents the examiner's aggregate historical record and does not constitute a prediction for any specific application.
This pooled record aggregates the examiner's work across multiple art units within TC 2100. The 75% allowance rate describes past decisions on 483 completed applications and reflects the examiner's combined history across all assigned art units. Aggregate figures describe historical outcomes and are not predictions about any individual application. Detailed allowance rates for each specific art unit appear in a separate section.
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 machine learning, and neural-network / biological-model computing.
Allowance rate for applications with an examiner interview versus without one.
A correlation, not proof that interviews cause allowances. Based on 166 decided applications with an interview and 182 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 51 decided applications with an interview and 26 without.
Primarily examines neural-network / biological-model computing, and machine learning.
Based on 35 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 23 applications — too small a sample to characterize the rejection mix reliably; shown for completeness.
Methodology. This page pools every art unit in which Examiner Tsu-Chang Lee 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: 547 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 →
ATTORNEY ADVERTISING — Sean Lynch, Partner, Lynch LLP