Prosecution Insights
Prosecution Insights
3 pending office actions • 3 art units • 3 examiners • 0 of 3 (0%) have an AI response strategy ready
Based on the USPTO statutory response window for each pending office action. 3 of the docket's apps have a known mailing date; the rest are excluded from the tile counts.
Every pending office action with a known statutory deadline, placed on a days-until-due axis. Dots left of Today are overdue; the further right, the more runway. Cases that share a deadline window stack vertically. 3 of the docket's apps have a known mailing date.
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 (33%) |
| §103 only | 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 |
|---|---|---|---|
| SINGH, AMRESH | 1 | 75.8% | +22.3% |
| ANYA, CHARLES E | 1 | 81.7% | +33.3% |
| KRAISINGER, EMILY MARIE | 1 | 32.8% | +45.5% |
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 |
|---|---|---|---|
| 18459235 | System and Method Of Annotating Data Models Allowing The Factoring In Or Out Of Recurring Events To Improve The Outcome Of Predictive Systems | ANYA, CHARLES E | 52d overdue |
Multi-statute / §101-driven matters, or cases in front of an examiner with an allow rate under 30%. The top 1 ordered by deadline are shown.
| App # | Title | Examiner | Due in |
|---|---|---|---|
| 18324076 | Automatic Normalization for Service Level Agreement Monitoring and Conformance Engine | KRAISINGER, EMILY MARIE | 20d |
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 |
|---|---|---|---|
| 18459235 | System and Method Of Annotating Data Models Allowing The Factoring In Or Out Of Recurring Events To Improve The Outcome Of Predictive Systems | ANYA, CHARLES E | 52d overdue |
| 18825943 | Self-Learning and Repairing Robotic Process Automation for Telecom Expense Management | SINGH, AMRESH | 5d |
| 18324076 | Automatic Normalization for Service Level Agreement Monitoring and Conformance Engine | KRAISINGER, EMILY MARIE | 20d |
| Art Unit | Apps |
|---|---|
| 2159 | 1 |
| 2194 | 1 |
| 3626 | 1 |
| App # | Title | Examiner | Art Unit | Statutes | Status | Due in | AI | Filed |
|---|---|---|---|---|---|---|---|---|
| 18825943 | Self-Learning and Repairing Robotic Process Automation for Telecom Expense Management | SINGH, AMRESH | 2159 | §103 | Final Rejection | 5d | Pending | Sep 05, 2024 |
| 18459235 | System and Method Of Annotating Data Models Allowing The Factoring In Or Out Of Recurring Events To Improve The Outcome Of Predictive Systems | ANYA, CHARLES E | 2194 | §103 | Non-Final OA | 52d overdue | Pending | Aug 31, 2023 |
| 18324076 | Automatic Normalization for Service Level Agreement Monitoring and Conformance Engine | KRAISINGER, EMILY MARIE | 3626 | §101 | Non-Final OA | 20d | Pending | May 25, 2023 |
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