Prosecution Insights
Prosecution Insights
4 pending office actions • 4 art units • 4 examiners • 0 of 4 (0%) have an AI response strategy ready • 5 patents granted in the last 365 days
Based on the USPTO statutory response window for each pending office action. 4 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. 4 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 |
|---|---|
| §103 only | 2 (50%) |
| §102 only | 1 (25%) |
| §112 only | 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 |
|---|---|---|---|
| PATEL, SHERYL GOPAL | 1 | 12.5% | +18.8% |
| GILLS, KURTIS | 1 | 57.5% | +29.2% |
| NEWTON, CHAD A | 1 | 36.9% | +25.4% |
| MAHARAJ, DEVIKA S | 1 | 53.8% | +11.3% |
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 |
|---|---|---|---|
| 18612790 | Machine Learning Platform for Predictive Malady Treatment | PATEL, SHERYL GOPAL | 36d overdue |
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 |
|---|---|---|---|
| 18612790 | Machine Learning Platform for Predictive Malady Treatment | PATEL, SHERYL GOPAL | 36d overdue |
| 18241511 | SYSTEMS AND METHODS FOR MEDICAL PATIENT CLASSIFICATION | NEWTON, CHAD A | 12d |
| 18370262 | Artificial Intelligence System for Forward Looking Scheduling | GILLS, KURTIS | 19d |
| 18120895 | TECHNOLOGIES FOR USING MACHINE LEARNING MODELS TO ASSESS TIME SERIES DATA | MAHARAJ, DEVIKA S | 62d |
| Art Unit | Apps |
|---|---|
| 3685 | 1 |
| 3624 | 1 |
| 3681 | 1 |
| 2123 | 1 |
| App # | Title | Examiner | Art Unit | Statutes | Status | Due in | AI | Filed |
|---|---|---|---|---|---|---|---|---|
| 18612790 | Machine Learning Platform for Predictive Malady Treatment | PATEL, SHERYL GOPAL | 3685 | §102 | Final Rejection | 36d overdue | Pending | Mar 21, 2024 |
| 18370262 | Artificial Intelligence System for Forward Looking Scheduling | GILLS, KURTIS | 3624 | §103 | Final Rejection | 19d | Pending | Sep 19, 2023 |
| 18241511 | SYSTEMS AND METHODS FOR MEDICAL PATIENT CLASSIFICATION | NEWTON, CHAD A | 3681 | §103 | Non-Final OA | 12d | Pending | Sep 01, 2023 |
| 18120895 | TECHNOLOGIES FOR USING MACHINE LEARNING MODELS TO ASSESS TIME SERIES DATA | MAHARAJ, DEVIKA S | 2123 | §112 | Final Rejection | 62d | Pending | Mar 13, 2023 |
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