Prosecution Insights
Prosecution Insights
Technology areas: Communications • Medical Devices & Mechanical Engineering
6 pending office actions • 4 art units • 6 examiners • 0 of 6 (0%) have an AI response strategy ready • 13 patents granted in the last 365 days
Brainlab SE currently has 5 pending office actions within the Medical Devices & Mechanical Engineering technology area. These actions are distributed among 5 distinct examiners, meaning each pending matter is being reviewed by a different individual. These 5 actions are spread across 3 distinct art units.
The busiest examiner for the portfolio is JACOB, OOMMEN, who holds 1 busiest examiner pending action. With 5 pending office actions and 5 distinct examiners, the workload is evenly split, with each examiner managing exactly one action. This structure suggests that the company's work in Medical Devices & Mechanical Engineering covers several specialized sub-fields.
Practitioners must manage interactions with 5 distinct examiners across 3 distinct art units. While the portfolio is not large, the lack of examiner concentration means that strategies may need to be tailored for each of the 5 pending office actions. The resolution of these matters will depend on the specific requirements of each examiner and the 3 distinct art units involved.
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 | 2 (33%) |
| §103 only | 3 (50%) |
| Multi-statute (no §101) | 1 (17%) |
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 |
|---|---|---|---|
| FRITH, SEAN A | 1 | 62.5% | +26.7% |
| JACOB, OOMMEN | 1 | 79.1% | +17.5% |
| ALDARRAJI, ZAINAB MOHAMMED | 1 | 66.7% | +18.7% |
| ASGHAR, AMINAH | 1 | 61.2% | +46.7% |
| KOZIOL, STEPHEN R | 1 | 77.6% | +25.1% |
| COCHRAN, BRIANNA RENAE | 1 | 45.5% | +66.7% |
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 |
|---|---|---|---|
| 19355210 | METHOD OF SAMPLING RELEVANT SURFACE POINTS OF A SUBJECT | FRITH, SEAN A | — |
| 18717154 | DETERMINING PHYSICALLY CORRECT DIGITALLY RECONSTRUCTED RADIOGRAPHS | ALDARRAJI, ZAINAB MOHAMMED | — |
| 18527723 | Tracking a Part of a Surface of a Patient's Body Using Thermographic Images | ASGHAR, AMINAH | — |
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 |
|---|---|---|---|
| 19355210 | METHOD OF SAMPLING RELEVANT SURFACE POINTS OF A SUBJECT | FRITH, SEAN A | — |
| 18992666 | AUGMENTATION OVERLAY DEVICE | JACOB, OOMMEN | — |
| 18717154 | DETERMINING PHYSICALLY CORRECT DIGITALLY RECONSTRUCTED RADIOGRAPHS | ALDARRAJI, ZAINAB MOHAMMED | — |
| 18527723 | Tracking a Part of a Surface of a Patient's Body Using Thermographic Images | ASGHAR, AMINAH | — |
| 18566265 | METHOD FOR TRAINING AND USING A DEEP LEARNING ALGORITHM TO COMPARE MEDICAL IMAGES BASED ON DIMENSIONALITY-REDUCED REPRESENTATIONS | KOZIOL, STEPHEN R | — |
| 18559744 | ANALYSIS AND AUGMENTATION OF DISPLAY DATA | COCHRAN, BRIANNA RENAE | — |
| Art Unit | Apps |
|---|---|
| 3797 | 2 |
| 3798 | 1 |
| 2665 | 1 |
| 2615 | 1 |
| App # | Title | Examiner | Art Unit | Statutes | Status | Due in | AI | Filed |
|---|---|---|---|---|---|---|---|---|
| 19355210 | METHOD OF SAMPLING RELEVANT SURFACE POINTS OF A SUBJECT | FRITH, SEAN A | 3798 | §101§102§112Other | Non-Final OA | — | Pending | Oct 10, 2025 |
| 18992666 | AUGMENTATION OVERLAY DEVICE | JACOB, OOMMEN | 3797 | §103 | Final Rejection | — | Pending | Jan 09, 2025 |
| 18717154 | DETERMINING PHYSICALLY CORRECT DIGITALLY RECONSTRUCTED RADIOGRAPHS | ALDARRAJI, ZAINAB MOHAMMED | — | §101§102§103§112 | Non-Final OA | — | Pending | Jun 06, 2024 |
| 18527723 | Tracking a Part of a Surface of a Patient's Body Using Thermographic Images | ASGHAR, AMINAH | 3797 | §103§112 | Final Rejection | — | Pending | Dec 04, 2023 |
| 18566265 | METHOD FOR TRAINING AND USING A DEEP LEARNING ALGORITHM TO COMPARE MEDICAL IMAGES BASED ON DIMENSIONALITY-REDUCED REPRESENTATIONS | KOZIOL, STEPHEN R | 2665 | §103 | Non-Final OA | — | Pending | Dec 01, 2023 |
| 18559744 | ANALYSIS AND AUGMENTATION OF DISPLAY DATA | COCHRAN, BRIANNA RENAE | 2615 | §103 | Non-Final OA | — | Pending | Nov 08, 2023 |
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