Prosecution Insights
Prosecution Insights
5 pending office actions • 3 art units • 4 examiners • 0 of 5 (0%) have an AI response strategy ready • 28 patents granted in the last 365 days
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 | 2 (40%) |
| §101 + other | 1 (20%) |
| §103 only | 2 (40%) |
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 |
|---|---|---|---|
| KUDO, KEN | 2 | — | — |
| LEE, MICHAEL CHRISTOPHER | 1 | 62.1% | +26.4% |
| BAKER, EZRA JAMES | 1 | 37.5% | +41.7% |
| KHAN, SHAHID K | 1 | 74.4% | +15.0% |
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 |
|---|---|---|---|
| 17846062 | CHESS SELF-LEARNING METHOD AND DEVICE BASED ON MACHINE LEARNING | KHAN, SHAHID K | 16d overdue |
| 18331887 | INDOOR PASSIVE HUMAN BEHAVIOR RECOGNITION METHOD AND DEVICE | BAKER, EZRA JAMES | 34d |
| 18433343 | CHARACTERIZATION METHOD BASED ON DEEP REINFORCEMENT LEARNING FOR DISCRETE MANUFACTURING INDUSTRY DATA | LEE, MICHAEL CHRISTOPHER | — |
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 |
|---|---|---|---|
| 17846062 | CHESS SELF-LEARNING METHOD AND DEVICE BASED ON MACHINE LEARNING | KHAN, SHAHID K | 16d overdue |
| 18331887 | INDOOR PASSIVE HUMAN BEHAVIOR RECOGNITION METHOD AND DEVICE | BAKER, EZRA JAMES | 34d |
| 18433343 | CHARACTERIZATION METHOD BASED ON DEEP REINFORCEMENT LEARNING FOR DISCRETE MANUFACTURING INDUSTRY DATA | LEE, MICHAEL CHRISTOPHER | — |
| Art Unit | Apps |
|---|---|
| 2671 | 2 |
| 2126 | 1 |
| 2146 | 1 |
| App # | Title | Examiner | Art Unit | Statutes | Status | Due in | AI | Filed |
|---|---|---|---|---|---|---|---|---|
| 18680317 | METHOD FOR PROCESSING LOW RESOLUTION DEGRADED IMAGE, SYSTEM, STORAGE MEDIUM, AND DEVICE THEREFOR | KUDO, KEN | 2671 | §103 | Final Rejection | — | Pending | May 31, 2024 |
| 18675032 | METHOD FOR IMAGE MOTION DEBLURRING, APPARATUS, ELECTRONIC DEVICE AND MEDIUM THEREFOR | KUDO, KEN | 2671 | §103 | Final Rejection | 61d | Pending | May 27, 2024 |
| 18433343 | CHARACTERIZATION METHOD BASED ON DEEP REINFORCEMENT LEARNING FOR DISCRETE MANUFACTURING INDUSTRY DATA | LEE, MICHAEL CHRISTOPHER | — | §101 | Non-Final OA | — | Pending | Feb 05, 2024 |
| 18331887 | INDOOR PASSIVE HUMAN BEHAVIOR RECOGNITION METHOD AND DEVICE | BAKER, EZRA JAMES | 2126 | §101 | Final Rejection | 34d | Pending | Jun 08, 2023 |
| 17846062 | CHESS SELF-LEARNING METHOD AND DEVICE BASED ON MACHINE LEARNING | KHAN, SHAHID K | 2146 | §101§103 | Non-Final OA | 16d overdue | Pending | Jun 22, 2022 |
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