Examiner Jason Lin has allowed 578 of 789 decided applications (73%) in Computer Architecture, Software, and Information Security.
Jason Lin holds a public record across 3 art units within Technology Center 2100 (Computer Architecture, Software, and Information Security). Of 789 disposed applications, 578 were allowed, yielding an overall allowance rate of 73%. The allowance rate varies across his art units, ranging from 48% to 85%. This pooled figure reflects the aggregate of his work across these units and does not represent the rate for any single art unit or predict outcomes in specific applications.
A pooled record aggregates data across multiple art units, masking variation among them. The overall allowance rate of 73% describes past dispositions across all units combined and is historical, not predictive. Individual art units may have materially different allowance rates—in this case, spanning 48% to 85%—so a pooled figure alone does not indicate the likely outcome in any particular application or art unit. Per-art-unit detail is available separately.
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 control or regulating systems.
Allowance rate for applications with an examiner interview versus without one.
A correlation, not proof that interviews cause allowances. Based on 274 decided applications with an interview and 189 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 64 decided applications with an interview and 153 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 67 decided applications with an interview and 42 without.
Methodology. This page pools every art unit in which Examiner Jason Lin 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: 826 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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