Prosecution Insights
Prosecution Insights
Technology area: Transportation, E-Commerce & Mechanical Systems
1 pending office actions • 1 art units • 1 examiners • 0 of 1 (0%) have an AI response strategy ready
Ming Chuan University has 1 pending office action in the Transportation, E-Commerce & Mechanical Systems technology area. This action is handled by 1 distinct examiner and 1 distinct art unit. This indicates a focused prosecution effort for a specific mechanical or transportation-related innovation, allowing the university to concentrate its resources on a single set of technical requirements within the USPTO.
The busiest examiner for this portfolio is BOROWSKI, MICHAEL, who is responsible for the 1 pending office action. Since there is only 1 distinct examiner and 1 distinct art unit involved, the university's prosecution strategy is streamlined. Success depends on addressing the specific technical concerns of this single examiner within the mechanical systems art unit. By focusing on the 1 pending office action, the applicant can work toward a resolution with the 1 distinct examiner, ensuring that the innovation is reviewed according to the specific standards of the assigned art unit.
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 + other | 1 (100%) |
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 |
|---|---|---|---|
| BOROWSKI, MICHAEL | 1 | 27.6% | +57.1% |
Multi-statute / §101-driven matters, or cases in front of an examiner with an allow rate under 30%. The top 1 ordered by deadline are shown.
| App # | Title | Examiner | Due in |
|---|---|---|---|
| 18790579 | SYSTEM FOR OPTIMIZING ELECTRIC VEHICLE CHARGING SCHEDULES AND IMPROVING ACCURACY OF PREDICTING GREENHOUSE GAS EMISSIONS OF ELECTRIC VEHICLE CHARGING BASED ON TRANSFER LEARNING AND DEEP REINFORCEMENT LEARNING | BOROWSKI, MICHAEL | 79d |
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 1 ordered by deadline are shown.
| App # | Title | Examiner | Due in |
|---|---|---|---|
| 18790579 | SYSTEM FOR OPTIMIZING ELECTRIC VEHICLE CHARGING SCHEDULES AND IMPROVING ACCURACY OF PREDICTING GREENHOUSE GAS EMISSIONS OF ELECTRIC VEHICLE CHARGING BASED ON TRANSFER LEARNING AND DEEP REINFORCEMENT LEARNING | BOROWSKI, MICHAEL | 79d |
| Art Unit | Apps |
|---|---|
| 3624 | 1 |
| App # | Title | Examiner | Art Unit | Statutes | Status | Due in | AI | Filed |
|---|---|---|---|---|---|---|---|---|
| 18790579 | SYSTEM FOR OPTIMIZING ELECTRIC VEHICLE CHARGING SCHEDULES AND IMPROVING ACCURACY OF PREDICTING GREENHOUSE GAS EMISSIONS OF ELECTRIC VEHICLE CHARGING BASED ON TRANSFER LEARNING AND DEEP REINFORCEMENT LEARNING | BOROWSKI, MICHAEL | 3624 | §101§103§112 | Final Rejection | 79d | Pending | Jul 31, 2024 |
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