Examiner Oluwatosin O Alabi has allowed 139 of 226 decided applications (62%) in Computer Architecture, Software, and Information Security.
Oluwatosin O Alabi maintains a public record spanning 4 art units within Technology Center 2100 (Computer Architecture, Software, and Information Security). Across 226 disposed applications, the examiner allowed 139, yielding an overall allowance rate of 62%. The allowance rate varies across the examiner's art units, ranging from 34% to 73%. This aggregate figure reflects decisions rendered across multiple subject areas within TC 2100 and does not describe the record in any single art unit.
A pooled record aggregates data across multiple art units, creating a single allowance-rate figure that represents the examiner's overall history of decisions. That aggregate rate describes past outcomes across different technology areas and is correlational data only—a historical summary. It is not a prediction of any specific application's disposition. Individual art units may show different patterns; a separate section of this page details per-art-unit records.
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 103 decided applications with an interview and 50 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 29 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 9 applications — too small a sample to characterize the rejection mix reliably; shown for completeness.
Methodology. This page pools every art unit in which Examiner Oluwatosin O Alabi 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: 276 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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