Examiner Bijan Mapar has allowed 341 of 494 decided applications (69%) in Computer Architecture, Software, and Information Security.
Examiner Bijan Mapar holds a public record of 537 total applications across Technology Center 2100 (Computer Architecture, Software, and Information Security), spanning four art units. Of 494 disposed applications, 341 were allowed, yielding an overall allowance rate of 69%. The examiner's allowance rate ranges from 55% to 84% across these art units. This pooled figure reflects aggregate outcomes across different subject-matter areas within TC 2100 and does not characterize performance in any single art unit.
A pooled record aggregates the examiner's outcomes across multiple art units within TC 2100. The overall allowance rate of 69% describes past dispositions and is not a prediction of any specific application's outcome. The range (55% to 84%) reflects variation among individual art units; the aggregate figure masks this spread. Historical data describes what occurred, not what will occur in any future case.
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 131 decided applications with an interview and 118 without.
Primarily examines computer-aided design (CAD).
Allowance rate for applications with an examiner interview versus without one.
A correlation, not proof that interviews cause allowances. Based on 43 decided applications with an interview and 52 without.
Primarily examines artificial-intelligence and machine-learning methods.
Allowance rate for applications with an examiner interview versus without one.
A correlation, not proof that interviews cause allowances. Based on 44 decided applications with an interview and 82 without.
Primarily examines neural-network / biological-model computing, and machine learning.
Based on 24 applications — too small a sample to characterize the rejection mix reliably; shown for completeness.
Methodology. This page pools every art unit in which Examiner Bijan Mapar 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: 537 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