Examiner Sheela S Rao has allowed 452 of 564 decided applications (80%) in Computer Architecture, Software, and Information Security.
Examiner Sheela S Rao maintains a public record across 7 art units within Technology Center 2100 (Computer Architecture, Software, and Information Security). Over 564 decided applications, the examiner's allowance rate stands at 80%, with 452 applications allowed and 112 abandoned. The allowance rate across individual art units ranges from 70% to 86%, reflecting variation in outcomes by art-unit assignment. This pooled figure aggregates dispositions across multiple subject areas and does not predict any individual application's outcome.
A pooled record aggregates an examiner's decisions across multiple art units, presenting an overall allowance rate as historical fact rather than a prediction tool. The range (70% to 86%) shows that allowance rates differ by art unit; the pooled figure (80%) is a weighted average across all units in the record. Aggregate statistics describe past output and are correlational only—they do not forecast the path or result of any specific application.
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 75 decided applications with an interview and 118 without.
Primarily examines neural-network / biological-model computing, and machine learning.
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 28 decided applications with an interview and 106 without.
Primarily examines neural-network / biological-model computing, and machine learning.
Primarily examines neural-network / biological-model computing, and machine learning.
Based on 19 applications — too small a sample to characterize the rejection mix reliably; shown for completeness.
Primarily examines machine learning, and neural-network / biological-model computing.
Primarily examines neural-network / biological-model computing, and machine learning.
Methodology. This page pools every art unit in which Examiner Sheela S Rao 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: 582 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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