Prosecution Insights
Prosecution Insights
3 pending office actions • 3 art units • 3 examiners • 0 of 3 (0%) have an AI response strategy ready • 10 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 |
|---|---|
| §103 only | 3 (100%) |
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 |
|---|---|---|---|
| THOMAS-HOMESCU, ANNE L | 1 | 77.0% | +35.8% |
| KAZEMINEZHAD, FARZAD | 1 | 70.9% | +67.7% |
| WASAFF, JOHN S. | 1 | 33.4% | +43.6% |
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 |
|---|---|---|---|
| 18175487 | SYSTEM FOR TRAINING NEURAL NETWORK TO DETECT ANOMALIES IN EVENT DATA | WASAFF, JOHN S. | 99d overdue |
| 18389749 | METHOD AND SYSTEM FOR RECOMMENDATION OF SUITABLE ASSETS USING LARGE LANGUAGE MODEL (LLM) | THOMAS-HOMESCU, ANNE L | 52d overdue |
| 18457607 | SYSTEM AND METHOD FOR MULTI-STAGE PROCESSING OF USER QUERY FOR ENHANCED INFORMATION RETRIEVAL | KAZEMINEZHAD, FARZAD | 62d |
| Art Unit | Apps |
|---|---|
| 2656 | 1 |
| 2653 | 1 |
| 3629 | 1 |
| App # | Title | Examiner | Art Unit | Statutes | Status | Due in | AI | Filed |
|---|---|---|---|---|---|---|---|---|
| 18389749 | METHOD AND SYSTEM FOR RECOMMENDATION OF SUITABLE ASSETS USING LARGE LANGUAGE MODEL (LLM) | THOMAS-HOMESCU, ANNE L | 2656 | §103 | Final Rejection | 52d overdue | Pending | Dec 19, 2023 |
| 18457607 | SYSTEM AND METHOD FOR MULTI-STAGE PROCESSING OF USER QUERY FOR ENHANCED INFORMATION RETRIEVAL | KAZEMINEZHAD, FARZAD | 2653 | §103 | Final Rejection | 62d | Pending | Aug 29, 2023 |
| 18175487 | SYSTEM FOR TRAINING NEURAL NETWORK TO DETECT ANOMALIES IN EVENT DATA | WASAFF, JOHN S. | 3629 | §103 | Non-Final OA | 99d overdue | Pending | Feb 27, 2023 |
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