Technology area: Computing & Software
2 pending office actions • 2 art units • 2 examiners • 0 of 2 (0%) have an AI response strategy ready • 19 patents granted in the last 365 days
Mohamed Bin Zayed University Of Artificial Intelligence has 2 pending office actions in Computing & Software. The matters are handled by 2 distinct examiners in 2 distinct art units, showing that the pending work is divided across separate examination assignments.
COHEN, ZARED ORION is the busiest examiner, with 1 pending matter. The distribution supports application-specific review of claim scope, cited art, and response timing rather than a single examiner-focused approach.
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 |
|---|---|
| §112 only | 1 (50%) |
| Multi-statute (no §101) | 1 (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 |
|---|---|---|---|
| COHEN, ZARED ORION | 1 | — | — |
| JONES, CHARLES JEFFREY | 1 | 28.0% | +43.1% |
Multi-statute / §101-driven matters, or cases in front of an examiner with an allow rate under 30%. The top 2 ordered by deadline are shown.
| App # | Title | Examiner | Due in |
|---|---|---|---|
| 18738622 | METHOD AND SYSTEM FOR DETERMINING UNCERTAINTY IN PERSONALIZED FEDERATED LEARNING | COHEN, ZARED ORION | — |
| 18482372 | DRONE DETECTION USING MODIFIED ONE-STAGE OBJECT DETECTORS | JONES, CHARLES JEFFREY | — |
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 1 ordered by deadline are shown.
| App # | Title | Examiner | Due in |
|---|---|---|---|
| 18482372 | DRONE DETECTION USING MODIFIED ONE-STAGE OBJECT DETECTORS | JONES, CHARLES JEFFREY | — |
| Art Unit | Apps |
|---|---|
| 2148 | 1 |
| 2122 | 1 |
| App # | Title | Examiner | Art Unit | Statutes | Status | Due in | AI | Filed |
|---|---|---|---|---|---|---|---|---|
| 18738622 | METHOD AND SYSTEM FOR DETERMINING UNCERTAINTY IN PERSONALIZED FEDERATED LEARNING | COHEN, ZARED ORION | 2148 | §103§112 | Non-Final OA | — | Pending | Jun 10, 2024 |
| 18482372 | DRONE DETECTION USING MODIFIED ONE-STAGE OBJECT DETECTORS | JONES, CHARLES JEFFREY | 2122 | §112 | Final Rejection | — | Pending | Oct 06, 2023 |
IP Author helps IP teams respond to office actions faster with AI-generated responses, examiner analytics, and prosecution intelligence.
Start Free TrialMohamed Bin Zayed University Of Artificial Intelligence has 2 pending office actions as of October 2026, spread across 2 art units and 2 examiners.
Zared Orion Cohen has the most, with 1 of Mohamed Bin Zayed University Of Artificial Intelligence's pending office actions.
Mostly Computing & Software (USPTO Tech Center 2100), based on where its pending office actions are examined.
