Prosecution Insights
Prosecution Insights
4 pending office actions • 2 art units • 2 examiners • 0 of 4 (0%) have an AI response strategy ready • 2 patents granted in the last 365 days
Based on the USPTO statutory response window for each pending office action. 4 of the docket's apps have a known mailing date; the rest are excluded from the tile counts.
Every pending office action with a known statutory deadline, placed on a days-until-due axis. Dots left of Today are overdue; the further right, the more runway. Cases that share a deadline window stack vertically. 4 of the docket's apps have a known mailing date.
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 | 2 (50%) |
| §103 only | 1 (25%) |
| §112 only | 1 (25%) |
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 |
|---|---|---|---|
| STARKS, WILBERT L | 3 | 75.5% | +4.1% |
| RIFKIN, BEN M | 1 | 43.8% | +15.7% |
Multi-statute / §101-driven matters, or cases in front of an examiner with an allow rate under 30%. The top 2 ordered by deadline are shown.
| App # | Title | Examiner | Due in |
|---|---|---|---|
| 17219699 | MACHINE LEARNING MODEL GENERATION PLATFORM | STARKS, WILBERT L | 45d overdue |
| 17404037 | GAME THEORETIC DEEP NEURAL NETWORKS FOR GLOBAL OPTIMIZATION OF MACHINE LEARNING MODEL GENERATION | STARKS, WILBERT L | 34d |
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 1 ordered by deadline are shown.
| App # | Title | Examiner | Due in |
|---|---|---|---|
| 18584259 | MACHINE LEARNING MODEL GENERATOR | RIFKIN, BEN M | 30d overdue |
| Art Unit | Apps |
|---|---|
| 2122 | 3 |
| 2123 | 1 |
| App # | Title | Examiner | Art Unit | Statutes | Status | Due in | AI | Filed |
|---|---|---|---|---|---|---|---|---|
| 18584259 | MACHINE LEARNING MODEL GENERATOR | RIFKIN, BEN M | 2123 | §103 | Non-Final OA | 30d overdue | Pending | Feb 22, 2024 |
| 17404037 | GAME THEORETIC DEEP NEURAL NETWORKS FOR GLOBAL OPTIMIZATION OF MACHINE LEARNING MODEL GENERATION | STARKS, WILBERT L | 2122 | §101 | Final Rejection | 34d | Pending | Aug 17, 2021 |
| 17372430 | SECURE ENVIRONMENT FOR A MACHINE LEARNING MODEL GENERATION PLATFORM | STARKS, WILBERT L | 2122 | §112 | Non-Final OA | 12d | Pending | Jul 10, 2021 |
| 17219699 | MACHINE LEARNING MODEL GENERATION PLATFORM | STARKS, WILBERT L | 2122 | §101 | Non-Final OA | 45d overdue | Pending | Mar 31, 2021 |
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