Prosecution Insights
Prosecution Insights
6 pending office actions • 6 art units • 6 examiners • 0 of 6 (0%) have an AI response strategy ready • 34 patents granted in the last 365 days
Based on the USPTO statutory response window for each pending office action. 6 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. 6 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 (17%) |
| §103 only | 5 (83%) |
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 |
|---|---|---|---|
| SAMPLE, JONATHAN L | 1 | 82.8% | +11.8% |
| CAIN, AARON G | 1 | 41.2% | +27.9% |
| BLUST, JASON W | 1 | 79.6% | +15.9% |
| DRYDEN, EMMA ELIZABETH | 1 | 61.5% | +30.0% |
| DAY, ROBERT N | 1 | 20.8% | +20.0% |
| CHUANG, SU-TING | 1 | 50.0% | +37.2% |
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 |
|---|---|---|---|
| 19011228 | GENERATING AND/OR USING TRAINING INSTANCES THAT INCLUDE PREVIOUSLY CAPTURED ROBOT VISION DATA AND DRIVABILITY LABELS | SAMPLE, JONATHAN L | 22d |
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 |
|---|---|---|---|
| 17918365 | LEARNING OPTIONS FOR ACTION SELECTION WITH META-GRADIENTS IN MULTI-TASK REINFORCEMENT LEARNING | DAY, ROBERT N | 103d overdue |
| 17337376 | STABLE AND EFFICIENT TRAINING OF ADVERSARIAL MODELS BY AN ITERATED UPDATE OPERATION OF SECOND ORDER OR HIGHER | CHUANG, SU-TING | 41d |
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 6 ordered by deadline are shown.
| App # | Title | Examiner | Due in |
|---|---|---|---|
| 18065726 | Depth-Based 3D Human Pose Detection and Tracking | DRYDEN, EMMA ELIZABETH | 111d overdue |
| 17918365 | LEARNING OPTIONS FOR ACTION SELECTION WITH META-GRADIENTS IN MULTI-TASK REINFORCEMENT LEARNING | DAY, ROBERT N | 103d overdue |
| 18102053 | USING EMBEDDINGS, GENERATED USING ROBOT ACTION MODELS, IN CONTROLLING ROBOT TO PERFORM ROBOTIC TASK | BLUST, JASON W | 3d overdue |
| 18176937 | Robot Collaboration via Cloud Server | CAIN, AARON G | 2d overdue |
| 19011228 | GENERATING AND/OR USING TRAINING INSTANCES THAT INCLUDE PREVIOUSLY CAPTURED ROBOT VISION DATA AND DRIVABILITY LABELS | SAMPLE, JONATHAN L | 22d |
| 17337376 | STABLE AND EFFICIENT TRAINING OF ADVERSARIAL MODELS BY AN ITERATED UPDATE OPERATION OF SECOND ORDER OR HIGHER | CHUANG, SU-TING | 41d |
| Art Unit | Apps |
|---|---|
| 3657 | 1 |
| 3656 | 1 |
| 2132 | 1 |
| 2677 | 1 |
| 2122 | 1 |
| 2146 | 1 |
| App # | Title | Examiner | Art Unit | Statutes | Status | Due in | AI | Filed |
|---|---|---|---|---|---|---|---|---|
| 19011228 | GENERATING AND/OR USING TRAINING INSTANCES THAT INCLUDE PREVIOUSLY CAPTURED ROBOT VISION DATA AND DRIVABILITY LABELS | SAMPLE, JONATHAN L | 3657 | §103 | Non-Final OA | 22d | Pending | Jan 06, 2025 |
| 18176937 | Robot Collaboration via Cloud Server | CAIN, AARON G | 3656 | §103 | Non-Final OA | 2d overdue | Pending | Mar 01, 2023 |
| 18102053 | USING EMBEDDINGS, GENERATED USING ROBOT ACTION MODELS, IN CONTROLLING ROBOT TO PERFORM ROBOTIC TASK | BLUST, JASON W | 2132 | §103 | Final Rejection | 3d overdue | Pending | Jan 26, 2023 |
| 18065726 | Depth-Based 3D Human Pose Detection and Tracking | DRYDEN, EMMA ELIZABETH | 2677 | §103 | Non-Final OA | 111d overdue | Pending | Dec 14, 2022 |
| 17918365 | LEARNING OPTIONS FOR ACTION SELECTION WITH META-GRADIENTS IN MULTI-TASK REINFORCEMENT LEARNING | DAY, ROBERT N | 2122 | §103 | Final Rejection | 103d overdue | Pending | Oct 12, 2022 |
| 17337376 | STABLE AND EFFICIENT TRAINING OF ADVERSARIAL MODELS BY AN ITERATED UPDATE OPERATION OF SECOND ORDER OR HIGHER | CHUANG, SU-TING | 2146 | §101 | Non-Final OA | 41d | Pending | Jun 02, 2021 |
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