Technology areas: Computing & Software • Communications • Transportation, E-Commerce & Mechanical Systems
9 pending office actions • 8 art units • 9 examiners • 0 of 9 (0%) have an AI response strategy ready • 12 patents granted in the last 365 days
DoorDash Inc. has 9 pending office actions in Transportation, E-Commerce & Mechanical Systems. Those matters involve 9 distinct examiners and 8 distinct art units, indicating that the portfolio’s current prosecution activity is distributed across several examination assignments rather than concentrated in one unit.
CORRIELUS, JEAN M is the busiest listed examiner, with 1 pending matter. Because that examiner accounts for only part of the active docket, counsel should track office-action strategy across the broader examiner and art-unit mix.
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 (11%) |
| §101 + other | 5 (56%) |
| §103 only | 1 (11%) |
| Multi-statute (no §101) | 2 (22%) |
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 |
|---|---|---|---|
| CORRIELUS, JEAN M | 1 | 84.1% | +12.5% |
| WHITTINGTON, JESS G | 1 | 73.0% | +17.7% |
| RAMETTA, JULIA THERESE | 1 | — | — |
| WALLICK, STEPHANIE SHOSHANA | 1 | 31.0% | +40.0% |
| SIMPSON, DIONE N | 1 | 32.3% | +32.3% |
| GREGORY, BERNARR E | 1 | 90.5% | +6.7% |
| KASSIM, IMAD MUTEE | 1 | 74.6% | +30.8% |
| THIRUGNANAM, GANDHI | 1 | 73.1% | +13.2% |
| BARGEON, BRITTANY E | 1 | 44.9% | +33.8% |
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 |
|---|---|---|---|
| 19382645 | QUERY SEGMENTATION AND ENTITY LINKING WITH LARGE LANGUAGE MODELS | CORRIELUS, JEAN M | — |
| 19269883 | SYSTEM AND METHOD FOR PREDICTED PINS | WHITTINGTON, JESS G | — |
| 19082577 | DYNAMIC MAPPING USING IMAGE DATA | RAMETTA, JULIA THERESE | — |
| 19067414 | NEXT GEN ETA MACHINE LEARNING (ML) SYSTEM | WALLICK, STEPHANIE SHOSHANA | — |
| 18982207 | API FOR REMOTE MONITORING OF APPLICATION INTERACTION | SIMPSON, DIONE N | — |
| 18906063 | SYSTEMS FOR AUTONOMOUS AND AUTOMATED DELIVERY VEHICLES TO COMMUNICATE WITH THIRD PARTIES | GREGORY, BERNARR E | — |
| 18633918 | ENSEMBLE LEARNING MODEL FOR TIME-SERIES FORECASTING | KASSIM, IMAD MUTEE | — |
| 18585663 | GENERATING MAPPING INFORMATION BASED ON IMAGE LOCATIONS | THIRUGNANAM, GANDHI | — |
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 7 ordered by deadline are shown.
| App # | Title | Examiner | Due in |
|---|---|---|---|
| 18360204 | ITEM AGGREGATION WITH DATA CONVERSION | BARGEON, BRITTANY E | 38d overdue |
| 19382645 | QUERY SEGMENTATION AND ENTITY LINKING WITH LARGE LANGUAGE MODELS | CORRIELUS, JEAN M | — |
| 19269883 | SYSTEM AND METHOD FOR PREDICTED PINS | WHITTINGTON, JESS G | — |
| 19067414 | NEXT GEN ETA MACHINE LEARNING (ML) SYSTEM | WALLICK, STEPHANIE SHOSHANA | — |
| 18982207 | API FOR REMOTE MONITORING OF APPLICATION INTERACTION | SIMPSON, DIONE N | — |
| 18633918 | ENSEMBLE LEARNING MODEL FOR TIME-SERIES FORECASTING | KASSIM, IMAD MUTEE | — |
| 18585663 | GENERATING MAPPING INFORMATION BASED ON IMAGE LOCATIONS | THIRUGNANAM, GANDHI | — |
| Art Unit | Apps |
|---|---|
| 2159 | 1 |
| 2669 | 1 |
| 3628 | 1 |
| 3629 | 1 |
| 3648 | 1 |
| 2129 | 1 |
| 2672 | 1 |
| 3688 | 1 |
| App # | Title | Examiner | Art Unit | Statutes | Status | Due in | AI | Filed |
|---|---|---|---|---|---|---|---|---|
| 19382645 | QUERY SEGMENTATION AND ENTITY LINKING WITH LARGE LANGUAGE MODELS | CORRIELUS, JEAN M | 2159 | §101§103 | Non-Final OA | — | Pending | Nov 07, 2025 |
| 19269883 | SYSTEM AND METHOD FOR PREDICTED PINS | WHITTINGTON, JESS G | — | §101Other | Non-Final OA | — | Pending | Jul 15, 2025 |
| 19082577 | DYNAMIC MAPPING USING IMAGE DATA | RAMETTA, JULIA THERESE | 2669 | §101§103 | Non-Final OA | — | Pending | Mar 18, 2025 |
| 19067414 | NEXT GEN ETA MACHINE LEARNING (ML) SYSTEM | WALLICK, STEPHANIE SHOSHANA | 3628 | §101§103 | Final Rejection | — | Pending | Feb 28, 2025 |
| 18982207 | API FOR REMOTE MONITORING OF APPLICATION INTERACTION | SIMPSON, DIONE N | 3629 | §101§103 | Non-Final OA | — | Pending | Dec 16, 2024 |
| 18906063 | SYSTEMS FOR AUTONOMOUS AND AUTOMATED DELIVERY VEHICLES TO COMMUNICATE WITH THIRD PARTIES | GREGORY, BERNARR E | 3648 | §103§112 | Non-Final OA | — | Pending | Oct 03, 2024 |
| 18633918 | ENSEMBLE LEARNING MODEL FOR TIME-SERIES FORECASTING | KASSIM, IMAD MUTEE | 2129 | §102§103 | Non-Final OA | — | Pending | Apr 12, 2024 |
| 18585663 | GENERATING MAPPING INFORMATION BASED ON IMAGE LOCATIONS | THIRUGNANAM, GANDHI | 2672 | §101§103§112 | Final Rejection | — | Pending | Feb 23, 2024 |
| 18360204 | ITEM AGGREGATION WITH DATA CONVERSION | BARGEON, BRITTANY E | 3688 | §103 | Final Rejection | 38d overdue | Pending | Jul 27, 2023 |
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Start Free TrialDoorDash Inc. has 9 pending office actions as of October 2026, spread across 8 art units and 9 examiners.
Jean M Corrielus has the most, with 1 of DoorDash Inc.'s pending office actions.
Mostly Transportation, E-Commerce & Mechanical Systems (USPTO Tech Center 3600), based on where its pending office actions are examined.
