Examiner Li Wu Chang has allowed 742 of 862 decided applications (86%) in Computer Architecture, Software, and Information Security.
Li Wu Chang maintains a public record across 4 art units within Technology Center 2100 (Computer Architecture, Software, and Information Security). Over 862 disposed applications, 742 were allowed, yielding an 86% allowance rate. The allowance rate ranges from 84% to 88% across the examiner's art units. This pooled figure represents the aggregate of applications decided across art units 2122, 2124, 2129, and 2142, each of which may carry its own record and allowance rate. The 120 abandoned applications are included in the disposed total used to calculate the allowance rate.
A pooled record aggregates data across multiple art units, presenting an examiner's overall allowance rate rather than unit-by-unit figures. This aggregate allowance rate describes the examiner's historical record across decided applications and is not a prediction of any specific application's outcome. Different art units within the same technology center may have different allowance rates. Per-art-unit data, where available separately, may provide more granular context for applications in specific subject areas.
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 221 decided applications with an interview and 219 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 82 decided applications with an interview and 338 without.
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.
Primarily examines neural-network / biological-model computing, and machine learning.
Methodology. This page pools every art unit in which Examiner Li Wu Chang 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: 862 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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