Prosecution Insights
Prosecution Insights
4 pending office actions • 4 art units • 4 examiners • 0 of 4 (0%) have an AI response strategy ready • 8 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 | 3 (75%) |
| No statute on record | 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 |
|---|---|---|---|
| CHRISTENSEN, SCOTT B | 1 | 77.7% | +32.8% |
| ALI, AFAQ | 1 | 90.2% | +12.0% |
| POUDEL, SAMIKSHYA NMN | 1 | 47.4% | +81.8% |
| ULLAH, SHARIF E | 1 | 84.4% | +22.2% |
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.
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 |
|---|---|---|---|
| 18398985 | TECHNIQUES FOR ACCURATE LEARNING OF BASELINES FOR CHARACTERIZING ADVANCED APPLICATION-LAYER FLOOD ATTACK TOOLS | ALI, AFAQ | 30d overdue |
| 18398997 | TECHNIQUES FOR ACCURATE LEARNING OF BASELINES FOR THE DETECTION OF ADVANCED APPLICATION LAYER FLOOD ATTACK TOOLS | CHRISTENSEN, SCOTT B | 14d |
| 17933404 | CHARACTERIZATION AND MITIGATION OF RANDOMIZED DDoS ATTACKS | ULLAH, SHARIF E | 27d |
| 18471923 | SYSTEM AND METHOD FOR DETECTING DOCUMENT OBJECT MODEL CROSS-SITE SCRIPTING VULNERABILITY | POUDEL, SAMIKSHYA NMN | 28d |
| Art Unit | Apps |
|---|---|
| 2444 | 1 |
| 2434 | 1 |
| 2436 | 1 |
| 2495 | 1 |
| App # | Title | Examiner | Art Unit | Statutes | Status | Due in | AI | Filed |
|---|---|---|---|---|---|---|---|---|
| 18398997 | TECHNIQUES FOR ACCURATE LEARNING OF BASELINES FOR THE DETECTION OF ADVANCED APPLICATION LAYER FLOOD ATTACK TOOLS | CHRISTENSEN, SCOTT B | 2444 | §103 | Final Rejection | 14d | Pending | Dec 28, 2023 |
| 18398985 | TECHNIQUES FOR ACCURATE LEARNING OF BASELINES FOR CHARACTERIZING ADVANCED APPLICATION-LAYER FLOOD ATTACK TOOLS | ALI, AFAQ | 2434 | §103 | Final Rejection | 30d overdue | Pending | Dec 28, 2023 |
| 18471923 | SYSTEM AND METHOD FOR DETECTING DOCUMENT OBJECT MODEL CROSS-SITE SCRIPTING VULNERABILITY | POUDEL, SAMIKSHYA NMN | 2436 | §103 | Non-Final OA | 28d | Pending | Sep 21, 2023 |
| 17933404 | CHARACTERIZATION AND MITIGATION OF RANDOMIZED DDoS ATTACKS | ULLAH, SHARIF E | 2495 | — | Non-Final OA | 27d | Pending | Sep 19, 2022 |
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