Prosecution Insights
Prosecution Insights
5 pending office actions • 5 art units • 5 examiners • 0 of 5 (0%) have an AI response strategy ready • 9 patents granted in the last 365 days
Based on the USPTO statutory response window for each pending office action. 5 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. 5 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 | 1 (20%) |
| §103 only | 4 (80%) |
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 |
|---|---|---|---|
| NEWAY, SAMUEL G | 1 | 75.3% | +7.7% |
| NGUYEN, PHONG H | 1 | 70.7% | +20.2% |
| PULLIAS, JESSE SCOTT | 1 | 83.0% | +12.7% |
| SCHMIDT, KARI L | 1 | 74.2% | +42.9% |
| NANO, SARGON N | 1 | 81.0% | -2.0% |
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.
| App # | Title | Examiner | Due in |
|---|---|---|---|
| 18636297 | System and Method for Utilizing a Large Language Model (LLM) for Labeling Data-Items for Training a Machine Learning (ML) Model | PULLIAS, JESSE SCOTT | 65d |
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 |
|---|---|---|---|
| 18383076 | Email Security and Prevention of Phishing Attacks Using a Large Language Model (LLM) Engine | NANO, SARGON N | 30d 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 3 ordered by deadline are shown.
| App # | Title | Examiner | Due in |
|---|---|---|---|
| 18494562 | Output Privacy System | SCHMIDT, KARI L | 89d overdue |
| 18732019 | INTEGRITY OF PORTABLE EXECUTABLES IN DEPLOYMENT PACKAGES | NGUYEN, PHONG H | 29d |
| 18636297 | System and Method for Utilizing a Large Language Model (LLM) for Labeling Data-Items for Training a Machine Learning (ML) Model | PULLIAS, JESSE SCOTT | 65d |
| Art Unit | Apps |
|---|---|
| 2657 | 1 |
| 2156 | 1 |
| 2655 | 1 |
| 2439 | 1 |
| 2443 | 1 |
| App # | Title | Examiner | Art Unit | Statutes | Status | Due in | AI | Filed |
|---|---|---|---|---|---|---|---|---|
| 18788970 | Combined Machine Learning and Large Language Models | NEWAY, SAMUEL G | 2657 | §103 | Non-Final OA | 5d overdue | Pending | Jul 30, 2024 |
| 18732019 | INTEGRITY OF PORTABLE EXECUTABLES IN DEPLOYMENT PACKAGES | NGUYEN, PHONG H | 2156 | §103 | Final Rejection | 29d | Pending | Jun 03, 2024 |
| 18636297 | System and Method for Utilizing a Large Language Model (LLM) for Labeling Data-Items for Training a Machine Learning (ML) Model | PULLIAS, JESSE SCOTT | 2655 | §103 | Final Rejection | 65d | Pending | Apr 16, 2024 |
| 18494562 | Output Privacy System | SCHMIDT, KARI L | 2439 | §103 | Final Rejection | 89d overdue | Pending | Oct 25, 2023 |
| 18383076 | Email Security and Prevention of Phishing Attacks Using a Large Language Model (LLM) Engine | NANO, SARGON N | 2443 | §101 | Non-Final OA | 30d overdue | Pending | Oct 24, 2023 |
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