Examiner Kidest Worku has allowed 1,408 of 1,643 decided applications (86%) in Computer Architecture, Software, and Information Security.
Examiner Kidest Worku maintains a public record across 6 art units within Technology Center 2100 (Computer Architecture, Software, and Information Security). Of 1,643 disposed applications, 1,408 were allowed, yielding an 86% allowance rate. The examiner's record spans multiple art units (2119, 2121, 2122, 2123, 2125, 2127), with allowance rates ranging from 78% to 91% across those units. This pooled figure represents the overall historical rate across all art units combined and does not reflect performance in any single art unit.
A pooled record aggregates data across multiple art units, masking variation among them. The 86% figure describes past dispositions across all units together and is not a prediction of outcome for any specific application. Individual art units may have different allowance rates; the range of 78% to 91% reflects that variance. Pooled statistics are useful for understanding overall patterns but do not forecast results in particular technologies or art units.
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 electric power networks, supply, and distribution.
Allowance rate for applications with an examiner interview versus without one.
A correlation, not proof that interviews cause allowances. Based on 153 decided applications with an interview and 507 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 36 decided applications with an interview and 440 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 24 decided applications with an interview and 223 without.
Primarily examines neural-network / biological-model computing, and machine learning.
Primarily examines neural-network / biological-model computing, and machine learning.
Based on 48 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 Kidest Worku 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: 1,699 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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