Prosecution Insights
Prosecution Insights
5 pending office actions • 3 art units • 4 examiners • 0 of 5 (0%) have an AI response strategy ready
Based on the USPTO statutory response window for each pending office action. 5 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. 5 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 |
|---|---|
| §103 only | 4 (80%) |
| §112 only | 1 (20%) |
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 |
|---|---|---|---|
| MISIASZEK, AMBER ALTSCHUL | 2 | 47.0% | +24.6% |
| SZUMNY, JONATHON A | 1 | 58.1% | +58.9% |
| LULTSCHIK, WILLIAM G | 1 | 22.4% | +32.3% |
| EVANS, ASHLEY ELIZABETH | 1 | 10.0% | +29.9% |
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 |
|---|---|---|---|
| 18365899 | METHODS AND SYSTEMS FOR GENERATING PERSONALIZED TREATMENTS VIA CAUSAL INFERENCE | LULTSCHIK, WILLIAM G | 49d overdue |
| 18148991 | SYSTEMS AND METHODS FOR PREDICTING, DETECTING, AND MONITORING OF ACUTE ILLNESS | EVANS, ASHLEY ELIZABETH | 16d overdue |
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 5 ordered by deadline are shown.
| App # | Title | Examiner | Due in |
|---|---|---|---|
| 18365899 | METHODS AND SYSTEMS FOR GENERATING PERSONALIZED TREATMENTS VIA CAUSAL INFERENCE | LULTSCHIK, WILLIAM G | 49d overdue |
| 18904684 | SENSOR-BASED MACHINE LEARNING IN A HEALTH PREDICTION ENVIRONMENT | MISIASZEK, AMBER ALTSCHUL | 23d overdue |
| 18148991 | SYSTEMS AND METHODS FOR PREDICTING, DETECTING, AND MONITORING OF ACUTE ILLNESS | EVANS, ASHLEY ELIZABETH | 16d overdue |
| 18634598 | ACTIVE LEARNING FOR WEARABLE HEALTH SENSOR | SZUMNY, JONATHON A | 4d overdue |
| 19330712 | SENSOR-BASED MACHINE LEARNING IN A HEALTH PREDICTION ENVIRONMENT | MISIASZEK, AMBER ALTSCHUL | 71d |
| Art Unit | Apps |
|---|---|
| 3682 | 3 |
| 3686 | 1 |
| 3687 | 1 |
| App # | Title | Examiner | Art Unit | Statutes | Status | Due in | AI | Filed |
|---|---|---|---|---|---|---|---|---|
| 19330712 | SENSOR-BASED MACHINE LEARNING IN A HEALTH PREDICTION ENVIRONMENT | MISIASZEK, AMBER ALTSCHUL | 3682 | §103 | Non-Final OA | 71d | Pending | Sep 16, 2025 |
| 18904684 | SENSOR-BASED MACHINE LEARNING IN A HEALTH PREDICTION ENVIRONMENT | MISIASZEK, AMBER ALTSCHUL | 3682 | §103 | Non-Final OA | 23d overdue | Pending | Oct 02, 2024 |
| 18634598 | ACTIVE LEARNING FOR WEARABLE HEALTH SENSOR | SZUMNY, JONATHON A | 3686 | §112 | Final Rejection | 4d overdue | Pending | Apr 12, 2024 |
| 18365899 | METHODS AND SYSTEMS FOR GENERATING PERSONALIZED TREATMENTS VIA CAUSAL INFERENCE | LULTSCHIK, WILLIAM G | 3682 | §103 | Non-Final OA | 49d overdue | Pending | Aug 04, 2023 |
| 18148991 | SYSTEMS AND METHODS FOR PREDICTING, DETECTING, AND MONITORING OF ACUTE ILLNESS | EVANS, ASHLEY ELIZABETH | 3687 | §103 | Non-Final OA | 16d overdue | Pending | Dec 30, 2022 |
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