Prosecution Insights
Prosecution Insights
Technology areas: Computing & Software • Transportation, E-Commerce & Mechanical Systems
3 pending office actions • 2 art units • 3 examiners • 0 of 3 (0%) have an AI response strategy ready • 10 patents granted in the last 365 days
Intelligent Fusion Technology Inc. has 3 pending office actions in the Transportation, E-Commerce & Mechanical Systems technology area. These actions are spread across 3 distinct examiners and 2 distinct art units, indicating a diverse range of technical review. This distribution suggests that the company is pursuing multiple innovations that span different sub-specialties within the transportation and mechanical sectors.
The busiest examiner, RIFKIN, BEN M, is responsible for 1 pending action. With the 3 pending office actions distributed among different examiners, the company must manage multiple prosecution tracks simultaneously. The fact that these actions are split between 2 distinct art units further emphasizes the need for a versatile strategy to address the unique requirements of each examiner. Practitioners should note that with multiple distinct examiners involved, the company's success depends on navigating different individual perspectives.
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 (33%) |
| Multi-statute (no §101) | 2 (67%) |
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 |
|---|---|---|---|
| RIFKIN, BEN M | 1 | 44.2% | +17.1% |
| ZHU, NOAH YI MIN | 1 | 80.5% | +14.5% |
| RUTTEN, JAMES D | 1 | 63.3% | +37.7% |
Multi-statute / §101-driven matters, or cases in front of an examiner with an allow rate under 30%. The top 3 ordered by deadline are shown.
| App # | Title | Examiner | Due in |
|---|---|---|---|
| 18324986 | Method, System and Storage Medium for Remaining Useful Life Prediction of Aircraft Engine Based on Gaussian Process Regression Integrated Deep Learning | RUTTEN, JAMES D | 49d |
| 18428822 | MAINTENANCE SCHEDULING USING EXPLAINABLE REINFORCEMENT LEARNING | RIFKIN, BEN M | — |
| 18461825 | SYSTEMS AND METHODS FOR LINEAR FREQUENCY-MODULATED CONTINUOUS-WAVE (LFMCW) RADAR | ZHU, NOAH YI MIN | — |
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 3 ordered by deadline are shown.
| App # | Title | Examiner | Due in |
|---|---|---|---|
| 18324986 | Method, System and Storage Medium for Remaining Useful Life Prediction of Aircraft Engine Based on Gaussian Process Regression Integrated Deep Learning | RUTTEN, JAMES D | 49d |
| 18428822 | MAINTENANCE SCHEDULING USING EXPLAINABLE REINFORCEMENT LEARNING | RIFKIN, BEN M | — |
| 18461825 | SYSTEMS AND METHODS FOR LINEAR FREQUENCY-MODULATED CONTINUOUS-WAVE (LFMCW) RADAR | ZHU, NOAH YI MIN | — |
| Art Unit | Apps |
|---|---|
| 3648 | 1 |
| 2121 | 1 |
| App # | Title | Examiner | Art Unit | Statutes | Status | Due in | AI | Filed |
|---|---|---|---|---|---|---|---|---|
| 18428822 | MAINTENANCE SCHEDULING USING EXPLAINABLE REINFORCEMENT LEARNING | RIFKIN, BEN M | — | §101§103 | Non-Final OA | — | Pending | Jan 31, 2024 |
| 18461825 | SYSTEMS AND METHODS FOR LINEAR FREQUENCY-MODULATED CONTINUOUS-WAVE (LFMCW) RADAR | ZHU, NOAH YI MIN | 3648 | §103§112 | Non-Final OA | — | Pending | Sep 06, 2023 |
| 18324986 | Method, System and Storage Medium for Remaining Useful Life Prediction of Aircraft Engine Based on Gaussian Process Regression Integrated Deep Learning | RUTTEN, JAMES D | 2121 | §103§112 | Final Rejection | 49d | Pending | May 28, 2023 |
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