Prosecution Insights
Prosecution Insights
Technology areas: Biotechnology & Pharmaceuticals • Transportation, E-Commerce & Mechanical Systems
4 pending office actions • 4 art units • 4 examiners • 0 of 4 (0%) have an AI response strategy ready • 1 patents granted in the last 365 days
Ampel Biosolutions LLC currently manages 4 pending office actions in the Biotechnology & Pharmaceuticals technology area. These matters are distributed across 4 distinct art units, showing technical diversity for the company's active filings. This distribution indicates that the company's innovations are being evaluated by 4 different specialized groups within the patent office, requiring an understanding of various art unit practices.
The workload involves 4 distinct examiners, with each examiner handling 1 case. PAULSON, SHEETAL R. is the busiest examiner with 1 pending office action, accounting for 1 of the company's total 4 pending actions. Because there are 4 distinct examiners for 4 pending actions, the company faces 4 different individual perspectives across the 4 distinct art units. This requires a flexible prosecution strategy that can address the individual requirements of 4 different examiners, as there is no concentration of workload to simplify the interaction.
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 | 3 (75%) |
| Multi-statute (no §101) | 1 (25%) |
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 |
|---|---|---|---|
| PAULSON, SHEETAL R. | 1 | 39.1% | +16.4% |
| DAUNER, JOSEPH G | 1 | 56.9% | +35.2% |
| CLOW, LORI A | 1 | 64.2% | +28.5% |
| SKIBINSKY, ANNA | 1 | 39.0% | +29.1% |
Multi-statute / §101-driven matters, or cases in front of an examiner with an allow rate under 30%. The top 4 ordered by deadline are shown.
| App # | Title | Examiner | Due in |
|---|---|---|---|
| 18806109 | Unsupervised Machine Learning Methods | DAUNER, JOSEPH G | 28d |
| 19199682 | Methods and Systems for Evaluation of Lupus Based on Ancestry-Associated Molecular Pathways | PAULSON, SHEETAL R. | — |
| 18752362 | Unsupervised Machine Learning Methods | CLOW, LORI A | — |
| 18572087 | METHODS AND SYSTEMS FOR MACHINE LEARNING ANALYSIS OF INFLAMMATORY SKIN DISEASES | SKIBINSKY, ANNA | — |
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 4 ordered by deadline are shown.
| App # | Title | Examiner | Due in |
|---|---|---|---|
| 18806109 | Unsupervised Machine Learning Methods | DAUNER, JOSEPH G | 28d |
| 19199682 | Methods and Systems for Evaluation of Lupus Based on Ancestry-Associated Molecular Pathways | PAULSON, SHEETAL R. | — |
| 18752362 | Unsupervised Machine Learning Methods | CLOW, LORI A | — |
| 18572087 | METHODS AND SYSTEMS FOR MACHINE LEARNING ANALYSIS OF INFLAMMATORY SKIN DISEASES | SKIBINSKY, ANNA | — |
| Art Unit | Apps |
|---|---|
| 3615 | 1 |
| 1682 | 1 |
| 1687 | 1 |
| 1635 | 1 |
| App # | Title | Examiner | Art Unit | Statutes | Status | Due in | AI | Filed |
|---|---|---|---|---|---|---|---|---|
| 19199682 | Methods and Systems for Evaluation of Lupus Based on Ancestry-Associated Molecular Pathways | PAULSON, SHEETAL R. | 3615 | §101§102 | Final Rejection | — | Pending | May 06, 2025 |
| 18806109 | Unsupervised Machine Learning Methods | DAUNER, JOSEPH G | 1682 | §102§103§112 | Final Rejection | 28d | Pending | Aug 15, 2024 |
| 18752362 | Unsupervised Machine Learning Methods | CLOW, LORI A | 1687 | §101§102§112 | Non-Final OA | — | Pending | Jun 24, 2024 |
| 18572087 | METHODS AND SYSTEMS FOR MACHINE LEARNING ANALYSIS OF INFLAMMATORY SKIN DISEASES | SKIBINSKY, ANNA | 1635 | §101§103§112 | Final Rejection | — | Pending | Dec 19, 2023 |
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