Prosecution Insights
Prosecution Insights
11 pending office actions • 8 art units • 11 examiners • 0 of 11 (0%) have an AI response strategy ready • 25 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 | 1 (9%) |
| §101 + other | 4 (36%) |
| §103 only | 2 (18%) |
| Double-patenting only | 1 (9%) |
| Multi-statute (no §101) | 3 (27%) |
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 |
|---|---|---|---|
| MORTELL, JOHN F | 1 | 67.0% | +25.7% |
| BULLINGTON, ROBERT P | 1 | 42.9% | +30.8% |
| NGUYEN, MINH CHAU | 1 | 82.9% | +8.4% |
| YOUNG, TIFFANY P | 1 | 80.1% | +22.4% |
| BROWN, VERNAL U | 1 | 69.6% | +13.8% |
| BROCKINGTON III, WILLIAM S | 1 | 42.1% | +54.7% |
| NGUYEN, CHUONG P | 1 | 79.0% | +13.4% |
| WU, NICHOLAS S | 1 | 52.9% | +32.4% |
| POOS, MADISON LYNN | 1 | 59.7% | +21.9% |
| ALLEN, JEFFREY R | 1 | 47.7% | +27.0% |
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 |
|---|---|---|---|
| 18961246 | LOCATION-BASED PICK UP AND DELIVERY SERVICES | NGUYEN, MINH CHAU | — |
Multi-statute / §101-driven matters, or cases in front of an examiner with an allow rate under 30%. The top 8 ordered by deadline are shown.
| App # | Title | Examiner | Due in |
|---|---|---|---|
| 15870166 | METHODS FOR LOADING A PARCEL CARRIER TO AN UNMANNED AERIAL VEHICLE | SANDERSON, JOSEPH W | 1190d overdue |
| 18140317 | AUTOMATICALLY PREDICTING SHIPPER BEHAVIOR USING MACHINE LEARNING MODELS | WU, NICHOLAS S | 25d overdue |
| 18758830 | CLASSIFYING RFID TAG READS TO INCREASE CONFIDENCE OF TAG INVENTORY | BROWN, VERNAL U | 17d overdue |
| 19232567 | SECURITY SCHEME FOR IDENTIFICATION TAGS | MORTELL, JOHN F | — |
| 19040606 | LOGISTICS DRIVING SIMULATOR | BULLINGTON, ROBERT P | — |
| 18903927 | LOGISTICS OPERATION ENVIRONMENT MAPPING FOR AUTONOMOUS VEHICLES | YOUNG, TIFFANY P | — |
| 18756983 | INTELLIGENT SYSTEMS AND METHODS FOR AUTONOMOUS MOVEMENT OPTIMIZATION | BROCKINGTON III, WILLIAM S | — |
| 18382996 | IDENTIFYING AN ASSET SORT LOCATION | NGUYEN, CHUONG P | — |
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 8 ordered by deadline are shown.
| App # | Title | Examiner | Due in |
|---|---|---|---|
| 18140317 | AUTOMATICALLY PREDICTING SHIPPER BEHAVIOR USING MACHINE LEARNING MODELS | WU, NICHOLAS S | 25d overdue |
| 18758830 | CLASSIFYING RFID TAG READS TO INCREASE CONFIDENCE OF TAG INVENTORY | BROWN, VERNAL U | 17d overdue |
| 17711739 | PAYLOAD CONTAINER FOR UNMANNED SYSTEM DELIVERY | ALLEN, JEFFREY R | 9d overdue |
| 19232567 | SECURITY SCHEME FOR IDENTIFICATION TAGS | MORTELL, JOHN F | — |
| 19040606 | LOGISTICS DRIVING SIMULATOR | BULLINGTON, ROBERT P | — |
| 18903927 | LOGISTICS OPERATION ENVIRONMENT MAPPING FOR AUTONOMOUS VEHICLES | YOUNG, TIFFANY P | — |
| 18756983 | INTELLIGENT SYSTEMS AND METHODS FOR AUTONOMOUS MOVEMENT OPTIMIZATION | BROCKINGTON III, WILLIAM S | — |
| 18382996 | IDENTIFYING AN ASSET SORT LOCATION | NGUYEN, CHUONG P | — |
| Art Unit | Apps |
|---|---|
| 3733 | 2 |
| 3715 | 1 |
| 2459 | 1 |
| 3665 | 1 |
| 2686 | 1 |
| 3646 | 1 |
| 2148 | 1 |
| 3619 | 1 |
| App # | Title | Examiner | Art Unit | Statutes | Status | Due in | AI | Filed |
|---|---|---|---|---|---|---|---|---|
| 19232567 | SECURITY SCHEME FOR IDENTIFICATION TAGS | MORTELL, JOHN F | — | §101 | Non-Final OA | — | Pending | Jun 09, 2025 |
| 19040606 | LOGISTICS DRIVING SIMULATOR | BULLINGTON, ROBERT P | 3715 | §101§112 | Non-Final OA | — | Pending | Jan 29, 2025 |
| 18961246 | LOCATION-BASED PICK UP AND DELIVERY SERVICES | NGUYEN, MINH CHAU | 2459 | DP | Final Rejection | — | Pending | Nov 26, 2024 |
| 18903927 | LOGISTICS OPERATION ENVIRONMENT MAPPING FOR AUTONOMOUS VEHICLES | YOUNG, TIFFANY P | 3665 | §102§103Other | Non-Final OA | — | Pending | Oct 01, 2024 |
| 18758830 | CLASSIFYING RFID TAG READS TO INCREASE CONFIDENCE OF TAG INVENTORY | BROWN, VERNAL U | 2686 | §101§103 | Non-Final OA | 17d overdue | Pending | Jun 28, 2024 |
| 18756983 | INTELLIGENT SYSTEMS AND METHODS FOR AUTONOMOUS MOVEMENT OPTIMIZATION | BROCKINGTON III, WILLIAM S | — | §101§103§112 | Non-Final OA | — | Pending | Jun 27, 2024 |
| 18382996 | IDENTIFYING AN ASSET SORT LOCATION | NGUYEN, CHUONG P | 3646 | §103§112 | Non-Final OA | — | Pending | Oct 23, 2023 |
| 18140317 | AUTOMATICALLY PREDICTING SHIPPER BEHAVIOR USING MACHINE LEARNING MODELS | WU, NICHOLAS S | 2148 | §101§103 | Non-Final OA | 25d overdue | Pending | Apr 27, 2023 |
| 17887812 | TEMPERATURE CONTROLLED PAYLOAD CONTAINER | POOS, MADISON LYNN | 3733 | §103 | Non-Final OA | — | Pending | Aug 15, 2022 |
| 17711739 | PAYLOAD CONTAINER FOR UNMANNED SYSTEM DELIVERY | ALLEN, JEFFREY R | 3733 | §103 | Final Rejection | 9d overdue | Pending | Apr 01, 2022 |
| 15870166 | METHODS FOR LOADING A PARCEL CARRIER TO AN UNMANNED AERIAL VEHICLE | SANDERSON, JOSEPH W | 3619 | §103§112 | Non-Final OA | 1190d overdue | Pending | Jan 12, 2018 |
IP Author helps IP teams respond to office actions faster with AI-generated responses, examiner analytics, and prosecution intelligence.
Start Free Trial