Examiner Nathan L Laughlin has allowed 570 of 840 decided applications in Computer Architecture, Software, and Information Security.
Nathan L Laughlin maintains a pooled allowance rate of 68% across hundreds of decided applications in Technology Center 2100 (Computer Architecture, Software, and Information Security). His record spans 7 art units within this technology center. The allowance rate ranges from 55% to 80% across these art units, reflecting variation in the decided applications across different subject areas within TC 2100. This pooled figure represents applications that have been allowed or abandoned, and excludes pending applications from the calculation.
A pooled record aggregates an examiner's allowance rates across multiple art units into a single overall figure. This aggregate describes past dispositions and does not forecast the outcome of any particular application. Individual art units within TC 2100 may show different allowance rates; the pooled rate is a summary across all of them. The range illustrates variation but does not indicate performance in any specific art unit or predict how any future application will be examined.
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.
Grounds can co-occur, so the four don't sum to 100%. The art-unit figure is the unweighted mean across examiners in the art unit; §102 and §112 carry no art-unit benchmark.
Allowance rate for applications with an examiner interview versus without one.
A correlation, not proof that interviews cause allowances. Based on 106 decided applications with an interview and 154 without.
Primarily examines neural-network / biological-model computing, and machine learning.
Grounds can co-occur, so the four don't sum to 100%. The art-unit figure is the unweighted mean across examiners in the art unit; §102 and §112 carry no art-unit benchmark.
Allowance rate for applications with an examiner interview versus without one.
A correlation, not proof that interviews cause allowances. Based on 48 decided applications with an interview and 156 without.
Primarily examines neural-network / biological-model computing, and machine learning.
Grounds can co-occur, so the four don't sum to 100%. The art-unit figure is the unweighted mean across examiners in the art unit; §102 and §112 carry no art-unit benchmark.
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 93 without.
Primarily examines neural-network / biological-model computing, and machine learning.
Grounds can co-occur, so the four don't sum to 100%. The art-unit figure is the unweighted mean across examiners in the art unit; §102 and §112 carry no art-unit benchmark.
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 79 without.
Grounds can co-occur, so the four don't sum to 100%. The art-unit figure is the unweighted mean across examiners in the art unit; §102 and §112 carry no art-unit benchmark.
Allowance rate for applications with an examiner interview versus without one.
A correlation, not proof that interviews cause allowances. Based on 22 decided applications with an interview and 74 without.
Primarily examines neural-network / biological-model computing, and machine learning.
Grounds can co-occur, so the four don't sum to 100%. The art-unit figure is the unweighted mean across examiners in the art unit; §102 and §112 carry no art-unit benchmark.
Based on 36 applications — too small a sample to characterize the rejection mix reliably; shown for completeness.
Primarily examines neural-network / biological-model computing, and machine learning.
Lynch LLP represents applicants in patent prosecution before the USPTO. These are general resources about the firm's services — not advice about this examiner or any specific application.
Methodology. This page pools every art unit in which Examiner Nathan L Laughlin 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 July 14, 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: 895 applications.
Rejection rates. Each §-rate is the share of this examiner's applications that drew at least one office-action rejection in which that statutory ground appears; applications with no rejection on record are excluded, and because grounds can co-occur the four do not sum to 100%. The art-unit figure beside each is the unweighted mean of the per-examiner rates across the art unit, published for §101 and §103 only. Beside the overall allowance rate we show a benchmark: for a single-art-unit examiner it is exactly that art unit's average, labeled “art-unit average”; for an examiner spanning several art units it is the “weighted peer average” — the per-art-unit averages, weighted by this examiner's application count in each — labeled distinctly because it is a blended figure, not any single art unit's average. Both are built from the same per-art-unit averages the panels show.
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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