Prosecution Insights
Prosecution Insights
6 pending office actions • 3 art units • 6 examiners • 0 of 6 (0%) have an AI response strategy ready • 15 patents granted in the last 365 days
Based on the USPTO statutory response window for each pending office action. 2 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 (50%) |
| §103 only | 3 (50%) |
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 |
|---|---|---|---|
| ARMSTRONG, ANGELA A | 1 | 74.0% | +8.4% |
| ROSSI, VY BUI | 1 | 30.2% | +38.7% |
| SHERALI, ISHRAT I | 1 | 93.3% | +6.0% |
| BAILEY, STEVEN WILLIAM | 1 | 32.0% | +20.2% |
| ARIETI, RUTH SOPHIA | 1 | 45.3% | +72.1% |
| HAYES, JONATHAN EDWARD | 1 | 37.5% | +23.5% |
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 |
|---|---|---|---|
| 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 | — |
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 |
|---|---|---|---|
| 17101545 | HIGH THROUGHPUT GENE EDITING SYSTEM AND METHOD | ARIETI, RUTH SOPHIA | 60d overdue |
| 16538475 | DESIGN OF MOLECULES | HAYES, JONATHAN EDWARD | 5d overdue |
| 18753906 | DETERMINING PHENOMIC RELATIONSHIPS BETWEEN COMPOUNDS AND CELL PERTURBATIONS UTILIZING MACHINE LEARNING MODELS | ROSSI, VY BUI | — |
| 18138705 | Computational Drug Target Selection | BAILEY, STEVEN WILLIAM | — |
| Art Unit | Apps |
|---|---|
| 1685 | 2 |
| 2667 | 1 |
| 1635 | 1 |
| App # | Title | Examiner | Art Unit | Statutes | Status | Due in | AI | Filed |
|---|---|---|---|---|---|---|---|---|
| 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 |
| 18753906 | DETERMINING PHENOMIC RELATIONSHIPS BETWEEN COMPOUNDS AND CELL PERTURBATIONS UTILIZING MACHINE LEARNING MODELS | ROSSI, VY BUI | 1685 | §101§102§112Other | 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 |
| 18138705 | Computational Drug Target Selection | BAILEY, STEVEN WILLIAM | — | §103 | Non-Final OA | — | Pending | Apr 24, 2023 |
| 17101545 | HIGH THROUGHPUT GENE EDITING SYSTEM AND METHOD | ARIETI, RUTH SOPHIA | 1635 | §103 | Non-Final OA | 60d overdue | Pending | Nov 23, 2020 |
| 16538475 | DESIGN OF MOLECULES | HAYES, JONATHAN EDWARD | 1685 | §103 | Final Rejection | 5d overdue | Pending | Aug 12, 2019 |
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