Prosecution Insights
Prosecution Insights
Technology areas: Biotechnology & Pharmaceuticals • Communications • Transportation, E-Commerce & Mechanical Systems
11 pending office actions • 5 art units • 11 examiners • 0 of 11 (0%) have an AI response strategy ready • 17 patents granted in the last 365 days
Recursion Pharmaceuticals Inc. has 11 pending office actions within the Biotechnology & Pharmaceuticals technology area. This active workload is spread across 5 distinct art units, indicating that while the technology area is focused, the specific applications fall under several different examination groups. The portfolio involves 11 distinct examiners, ensuring that each application is receiving a unique review from a different individual within the patent office.
The busiest examiner, SIOZOPOULOS, CONSTANTINE B, holds 1 pending office action, which is the same amount held by every other examiner involved with the portfolio. This ratio between pending actions and examiners suggests a lack of examiner concentration. For the legal team, this means that prosecution tactics must be developed independently for each of the pending office actions, as no single examiner is currently reviewing multiple applications for the company. The 5 distinct art units involved also suggest that the pending office actions are subject to the varying internal practices of multiple specialized groups.
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 only | 1 (9%) |
| §101 + other | 6 (55%) |
| §103 only | 1 (9%) |
| §102 only | 1 (9%) |
| Multi-statute (no §101) | 2 (18%) |
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 |
|---|---|---|---|
| SIOZOPOULOS, CONSTANTINE B | 1 | 58.0% | +38.8% |
| TO, BAOQUOC N | 1 | 89.8% | +7.9% |
| ARMSTRONG, ANGELA A | 1 | 73.9% | +8.8% |
| NGUYEN, LEON VIET Q | 1 | 85.3% | +9.9% |
| ROSSI, VY BUI | 1 | 29.5% | +36.4% |
| SHERALI, ISHRAT I | 1 | 93.2% | +6.2% |
| PARK, HYUN D | 1 | 41.7% | +22.8% |
| ISMAIL, REHANA | 1 | 75.8% | +34.8% |
| VASSELL, MEREDITH ABBOTT | 1 | 30.3% | +47.0% |
| BAILEY, STEVEN WILLIAM | 1 | 31.6% | +15.2% |
Cases in front of an examiner with an allow rate of 80%+ where the difficulty is Easy or Medium. The top 1 ordered by deadline are shown.
| App # | Title | Examiner | Due in |
|---|---|---|---|
| 18813537 | UTILIZING MASKED AUTOENCODER GENERATIVE MODELS TO EXTRACT MICROSCOPY REPRESENTATION AUTOENCODER EMBEDDINGS | NGUYEN, LEON VIET Q | — |
Multi-statute / §101-driven matters, or cases in front of an examiner with an allow rate under 30%. The top 8 ordered by deadline are shown.
| App # | Title | Examiner | Due in |
|---|---|---|---|
| 17101545 | HIGH THROUGHPUT GENE EDITING SYSTEM AND METHOD | ARIETI, RUTH SOPHIA | 60d overdue |
| 19167738 | COMPUTATIONAL DRUG TARGET SELECTION | SIOZOPOULOS, CONSTANTINE B | — |
| 19270223 | UTILIZING MACHINE LEARNING MODELS TO SYNTHESIZE PERTURBATION DATA TO GENERATE PERTURBATION HEATMAP GRAPHICAL USER INTERFACES | TO, BAOQUOC N | — |
| 19000315 | UTILIZING LANGUAGE MACHINE LEARNING MODELS FOR AUTONOMOUS EXECUTIONS OF COMPUTERIZED TECH-BIO EXPLORATION TOOLS | ARMSTRONG, ANGELA A | — |
| 18753906 | DETERMINING PHENOMIC RELATIONSHIPS BETWEEN COMPOUNDS AND CELL PERTURBATIONS UTILIZING MACHINE LEARNING MODELS | ROSSI, VY BUI | — |
| 18672492 | MULTI-MODAL PAIR MATCHING FOR A MULTI-MODAL MACHINE LEARNING MODEL LEARNING PROCESS | SHERALI, ISHRAT I | — |
| 18663991 | SYSTEMS AND METHODS FOR HIGH THROUGHPUT COMPOUND LIBRARY CREATION | PARK, HYUN D | — |
| 18231219 | Drug Optimization by Active Learning | VASSELL, MEREDITH ABBOTT | — |
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 |
|---|---|---|---|
| 19167738 | COMPUTATIONAL DRUG TARGET SELECTION | SIOZOPOULOS, CONSTANTINE B | — |
| 18753906 | DETERMINING PHENOMIC RELATIONSHIPS BETWEEN COMPOUNDS AND CELL PERTURBATIONS UTILIZING MACHINE LEARNING MODELS | ROSSI, VY BUI | — |
| 18663991 | SYSTEMS AND METHODS FOR HIGH THROUGHPUT COMPOUND LIBRARY CREATION | PARK, HYUN D | — |
| 18234341 | Heterocycle RMB39 Modulators | ISMAIL, REHANA | — |
| 18231219 | Drug Optimization by Active Learning | VASSELL, MEREDITH ABBOTT | — |
| 18138705 | Computational Drug Target Selection | BAILEY, STEVEN WILLIAM | — |
| Art Unit | Apps |
|---|---|
| 3686 | 1 |
| 1685 | 1 |
| 2667 | 1 |
| 1625 | 1 |
| 1635 | 1 |
| App # | Title | Examiner | Art Unit | Statutes | Status | Due in | AI | Filed |
|---|---|---|---|---|---|---|---|---|
| 19167738 | COMPUTATIONAL DRUG TARGET SELECTION | SIOZOPOULOS, CONSTANTINE B | 3686 | §101 | Non-Final OA | — | Pending | Sep 22, 2025 |
| 19270223 | UTILIZING MACHINE LEARNING MODELS TO SYNTHESIZE PERTURBATION DATA TO GENERATE PERTURBATION HEATMAP GRAPHICAL USER INTERFACES | TO, BAOQUOC N | — | §101§103Other | Non-Final OA | — | Pending | Jul 15, 2025 |
| 19000315 | UTILIZING LANGUAGE MACHINE LEARNING MODELS FOR AUTONOMOUS EXECUTIONS OF COMPUTERIZED TECH-BIO EXPLORATION TOOLS | ARMSTRONG, ANGELA A | — | §101§103 | Non-Final OA | — | Pending | Dec 23, 2024 |
| 18813537 | UTILIZING MASKED AUTOENCODER GENERATIVE MODELS TO EXTRACT MICROSCOPY REPRESENTATION AUTOENCODER EMBEDDINGS | NGUYEN, LEON VIET Q | — | §103 | Final Rejection | — | Pending | Aug 23, 2024 |
| 18753906 | DETERMINING PHENOMIC RELATIONSHIPS BETWEEN COMPOUNDS AND CELL PERTURBATIONS UTILIZING MACHINE LEARNING MODELS | ROSSI, VY BUI | 1685 | §101§102DP | Final Rejection | — | Pending | Jun 25, 2024 |
| 18672492 | MULTI-MODAL PAIR MATCHING FOR A MULTI-MODAL MACHINE LEARNING MODEL LEARNING PROCESS | SHERALI, ISHRAT I | 2667 | §101§103 | Non-Final OA | — | Pending | May 23, 2024 |
| 18663991 | SYSTEMS AND METHODS FOR HIGH THROUGHPUT COMPOUND LIBRARY CREATION | PARK, HYUN D | — | §103§112Other | Non-Final OA | — | Pending | May 14, 2024 |
| 18234341 | Heterocycle RMB39 Modulators | ISMAIL, REHANA | 1625 | §102 | Final Rejection | — | Pending | Aug 15, 2023 |
| 18231219 | Drug Optimization by Active Learning | VASSELL, MEREDITH ABBOTT | — | §101§103§112DP | Non-Final OA | — | Pending | Aug 07, 2023 |
| 18138705 | Computational Drug Target Selection | BAILEY, STEVEN WILLIAM | — | §101§102§103 | Non-Final OA | — | Pending | Apr 24, 2023 |
| 17101545 | HIGH THROUGHPUT GENE EDITING SYSTEM AND METHOD | ARIETI, RUTH SOPHIA | 1635 | §103§112 | Non-Final OA | 60d overdue | Pending | Nov 23, 2020 |
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