Prosecution Insights
Prosecution Insights
Technology areas: Communications • Transportation, E-Commerce & Mechanical Systems
4 pending office actions • 4 art units • 4 examiners • 0 of 4 (0%) have an AI response strategy ready • 3 patents granted in the last 365 days
Etsy Inc. has 3 pending office actions within the Transportation, E-Commerce & Mechanical Systems technology area. These actions are distributed among 3 distinct examiners. Each action is also located in a different division, as evidenced by the 3 distinct art units involved in the portfolio. The presence of 3 distinct art units indicates that Etsy Inc. is navigating a diverse range of technical classifications.
The busiest examiner, WANG, JIN CHENG, is currently handling 1 busiest examiner pending action. With 3 pending office actions and 3 distinct examiners, the workload is distributed with a single action per examiner. This lack of concentration means that prosecution strategies must be individualized for each case. The company must manage 3 distinct examiners to resolve the 3 pending office actions.
Since WANG, JIN CHENG manages 1 busiest examiner pending action, his specific feedback will only impact a single case. Practitioners should be prepared for varying requirements across the 3 distinct art units. Success in the Transportation, E-Commerce & Mechanical Systems area will require a flexible approach to satisfy the 3 distinct examiners. This distribution prevents any single examiner from dominating the current pending workload.
Based on the USPTO statutory response window for each pending office action. 3 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. 3 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 | 1 (25%) |
| §101 + other | 2 (50%) |
| §103 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 |
|---|---|---|---|
| STROUD, CHRISTOPHER | 1 | 28.2% | +20.8% |
| WANG, JIN CHENG | 1 | 59.5% | +10.4% |
| KANG, TIMOTHY J | 1 | 45.7% | +26.8% |
| MACASIANO, MARILYN G | 1 | 57.5% | +17.1% |
Multi-statute / §101-driven matters, or cases in front of an examiner with an allow rate under 30%. The top 3 ordered by deadline are shown.
| App # | Title | Examiner | Due in |
|---|---|---|---|
| 18390738 | PERSONALIZATION FROM SEQUENCES AND REPRESENTATIONS IN ADS | MACASIANO, MARILYN G | 73d overdue |
| 18593326 | IMPLEMENTING MACHINE LEARNING IN A LOW LATENCY ENVIRONMENT | KANG, TIMOTHY J | 15d overdue |
| 19045157 | MACHINE LEARNING-BASED REVIEW GENERATION | STROUD, CHRISTOPHER | — |
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 4 ordered by deadline are shown.
| App # | Title | Examiner | Due in |
|---|---|---|---|
| 18390738 | PERSONALIZATION FROM SEQUENCES AND REPRESENTATIONS IN ADS | MACASIANO, MARILYN G | 73d overdue |
| 18593326 | IMPLEMENTING MACHINE LEARNING IN A LOW LATENCY ENVIRONMENT | KANG, TIMOTHY J | 15d overdue |
| 18601325 | MODIFYING IMAGES FOR IMPROVED SEARCH | WANG, JIN CHENG | 29d |
| 19045157 | MACHINE LEARNING-BASED REVIEW GENERATION | STROUD, CHRISTOPHER | — |
| Art Unit | Apps |
|---|---|
| 3621 | 1 |
| 2617 | 1 |
| 3689 | 1 |
| 3622 | 1 |
| App # | Title | Examiner | Art Unit | Statutes | Status | Due in | AI | Filed |
|---|---|---|---|---|---|---|---|---|
| 19045157 | MACHINE LEARNING-BASED REVIEW GENERATION | STROUD, CHRISTOPHER | 3621 | §101§103 | Final Rejection | — | Pending | Feb 04, 2025 |
| 18601325 | MODIFYING IMAGES FOR IMPROVED SEARCH | WANG, JIN CHENG | 2617 | §103 | Final Rejection | 29d | Pending | Mar 11, 2024 |
| 18593326 | IMPLEMENTING MACHINE LEARNING IN A LOW LATENCY ENVIRONMENT | KANG, TIMOTHY J | 3689 | §101§103 | Non-Final OA | 15d overdue | Pending | Mar 01, 2024 |
| 18390738 | PERSONALIZATION FROM SEQUENCES AND REPRESENTATIONS IN ADS | MACASIANO, MARILYN G | 3622 | §101 | Non-Final OA | 73d overdue | Pending | Dec 20, 2023 |
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