Prosecution Insights
Prosecution Insights
Technology area: Transportation, E-Commerce & Mechanical Systems
2 pending office actions • 1 art units • 2 examiners • 0 of 2 (0%) have an AI response strategy ready • 1 patents granted in the last 365 days
Stackadapt Inc. has 2 pending office actions within the Transportation, E-Commerce & Mechanical Systems technology area. These actions are concentrated within 1 distinct art unit, yet they are being reviewed by 2 distinct examiners. This suggests that while the subject matter is narrow enough to stay within a single art unit, the prosecution experience may still vary due to different examiner approaches. The use of distinct examiners for only the actions means that each examiner has a significant impact on the portfolio.
The busiest examiner, POUNCIL, DARNELL A, is responsible for 1 pending office action. Since there are distinct examiners for the total actions, each examiner holds equal weight in the current pending portfolio. Practitioners should note that even within the 1 distinct art unit, the presence of multiple examiners requires adapting to different individual styles for each of the pending actions. This structure prevents a single examiner from controlling the entire outcome of the pending applications.
Based on the USPTO statutory response window for each pending office action. 1 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 (50%) |
| §101 + other | 1 (50%) |
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 |
|---|---|---|---|
| POUNCIL, DARNELL A | 1 | 21.4% | +30.7% |
| OSMAN BILAL AHMED, AFAF | 1 | 16.2% | +14.1% |
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 |
|---|---|---|---|
| 18715555 | MACHINE-LEARNING ARCHITECTURE FOR DEFINING END USER AUDIENCES FOR AUTOMATED ONLINE CONTENT SELECTION | OSMAN BILAL AHMED, AFAF | 36d |
| 19347354 | MULTI-GOAL CONTENT OBJECT DATA-PLACEMENT CONFIGURATIONS | POUNCIL, DARNELL A | — |
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 2 ordered by deadline are shown.
| App # | Title | Examiner | Due in |
|---|---|---|---|
| 18715555 | MACHINE-LEARNING ARCHITECTURE FOR DEFINING END USER AUDIENCES FOR AUTOMATED ONLINE CONTENT SELECTION | OSMAN BILAL AHMED, AFAF | 36d |
| 19347354 | MULTI-GOAL CONTENT OBJECT DATA-PLACEMENT CONFIGURATIONS | POUNCIL, DARNELL A | — |
| Art Unit | Apps |
|---|---|
| 3622 | 2 |
| App # | Title | Examiner | Art Unit | Statutes | Status | Due in | AI | Filed |
|---|---|---|---|---|---|---|---|---|
| 19347354 | MULTI-GOAL CONTENT OBJECT DATA-PLACEMENT CONFIGURATIONS | POUNCIL, DARNELL A | 3622 | §101§102§103 | Non-Final OA | — | Pending | Oct 01, 2025 |
| 18715555 | MACHINE-LEARNING ARCHITECTURE FOR DEFINING END USER AUDIENCES FOR AUTOMATED ONLINE CONTENT SELECTION | OSMAN BILAL AHMED, AFAF | 3622 | §101 | Final Rejection | 36d | Pending | May 31, 2024 |
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