Prosecution Insights
Prosecution Insights
3 pending office actions • 3 art units • 3 examiners • 0 of 3 (0%) have an AI response strategy ready • 12 patents granted in the last 365 days
Based on the USPTO statutory response window for each pending office action. 2 of the docket's apps have a known mailing date; the rest are excluded from the tile counts.
Difficulty is derived from the rejection statutes on the most recent pending office action. §101-driven and multi-statute cases are graded Hard; §112-only and obviousness-type double-patenting cases are graded Easy; everything else is Medium. "Unknown" means we have not yet parsed a statute for that office action.
| Bucket | Cases |
|---|---|
| §101 only | 1 (33%) |
| §103 only | 2 (67%) |
How the docket's pending cases split across USPTO tech-center bands.
Manual office-action response work runs about 10 hours per case. The time-saved bands below show what IP Author's prosecution pipeline typically delivers — a conservative 20% on the low end, 35% in the middle, 50% on the high end.
| Examiner | Apps on this docket | Allow rate | Interview lift |
|---|---|---|---|
| VIG, NARESH | 1 | 36.9% | +43.9% |
| SCHNEE, HAL W | 1 | 84.5% | +22.3% |
| ALLEN, NICHOLAS E | 1 | 76.5% | +15.6% |
Cases in front of an examiner with an allow rate of 80%+ where the difficulty is Easy or Medium. The top 1 ordered by deadline are shown.
| App # | Title | Examiner | Due in |
|---|---|---|---|
| 18327579 | MACHINE LEARNING ANOMALY DETECTION ON QUALITY OF SERVICE NETWORKING METRICS | SCHNEE, HAL W | 34d |
Multi-statute / §101-driven matters, or cases in front of an examiner with an allow rate under 30%. The top 1 ordered by deadline are shown.
| App # | Title | Examiner | Due in |
|---|---|---|---|
| 18644058 | SELECTION OF CONTENT FOR INSERTION IN VIDEOS | VIG, NARESH | — |
Cases in front of an examiner whose interview lift is 10 percentage points or more — i.e. interviewed cases historically resolve more favorably than non-interviewed ones. The top 3 ordered by deadline are shown.
| App # | Title | Examiner | Due in |
|---|---|---|---|
| 18092859 | Semantics Content Searching | ALLEN, NICHOLAS E | 128d overdue |
| 18327579 | MACHINE LEARNING ANOMALY DETECTION ON QUALITY OF SERVICE NETWORKING METRICS | SCHNEE, HAL W | 34d |
| 18644058 | SELECTION OF CONTENT FOR INSERTION IN VIDEOS | VIG, NARESH | — |
| Art Unit | Apps |
|---|---|
| 3622 | 1 |
| 2129 | 1 |
| 2154 | 1 |
| App # | Title | Examiner | Art Unit | Statutes | Status | Due in | AI | Filed |
|---|---|---|---|---|---|---|---|---|
| 18644058 | SELECTION OF CONTENT FOR INSERTION IN VIDEOS | VIG, NARESH | 3622 | §101 | Non-Final OA | — | Pending | Apr 23, 2024 |
| 18327579 | MACHINE LEARNING ANOMALY DETECTION ON QUALITY OF SERVICE NETWORKING METRICS | SCHNEE, HAL W | 2129 | §103 | Non-Final OA | 34d | Pending | Jun 01, 2023 |
| 18092859 | Semantics Content Searching | ALLEN, NICHOLAS E | 2154 | §103 | Final Rejection | 128d overdue | Pending | Jan 03, 2023 |
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