Prosecution Insights
Prosecution Insights
5 pending office actions • 4 art units • 5 examiners • 0 of 5 (0%) have an AI response strategy ready • 2 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 | 3 (60%) |
| §102 only | 1 (20%) |
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 |
|---|---|---|---|
| WANG, JIN CHENG | 1 | 59.2% | +10.1% |
| KANG, TIMOTHY J | 1 | 46.1% | +25.0% |
| MACASIANO, MARILYN G | 1 | 57.2% | +17.4% |
| LOHARIKAR, ANAND R | 1 | 69.1% | +25.9% |
| MAIDO, MAGGIE T | 1 | 61.5% | +27.6% |
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 |
|---|---|---|---|
| 18390738 | PERSONALIZATION FROM SEQUENCES AND REPRESENTATIONS IN ADS | MACASIANO, MARILYN G | 73d 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 5 ordered by deadline are shown.
| App # | Title | Examiner | Due in |
|---|---|---|---|
| 18390738 | PERSONALIZATION FROM SEQUENCES AND REPRESENTATIONS IN ADS | MACASIANO, MARILYN G | 73d overdue |
| 17832415 | Automated Model Selection | MAIDO, MAGGIE T | 46d overdue |
| 18593326 | IMPLEMENTING MACHINE LEARNING IN A LOW LATENCY ENVIRONMENT | KANG, TIMOTHY J | 15d overdue |
| 18387081 | CROSS-DOMAIN RECOMMENDATION VIA CONTRASTIVE LEARNING OF USER BEHAVIORS IN ATTENTIVE SEQUENCE MODELS | LOHARIKAR, ANAND R | 19d |
| 18601325 | MODIFYING IMAGES FOR IMPROVED SEARCH | WANG, JIN CHENG | 29d |
| Art Unit | Apps |
|---|---|
| 3689 | 2 |
| 2617 | 1 |
| 3622 | 1 |
| 2129 | 1 |
| App # | Title | Examiner | Art Unit | Statutes | Status | Due in | AI | Filed |
|---|---|---|---|---|---|---|---|---|
| 18601325 | MODIFYING IMAGES FOR IMPROVED SEARCH | WANG, JIN CHENG | 2617 | §103 | Final Rejection | 29d | Pending | Mar 11, 2024 |
| 18593326 | IMPLEMENTING MACHINE LEARNING IN A LOW LATENCY ENVIRONMENT | KANG, TIMOTHY J | 3689 | §103 | Final Rejection | 15d overdue | Pending | Mar 01, 2024 |
| 18390738 | PERSONALIZATION FROM SEQUENCES AND REPRESENTATIONS IN ADS | MACASIANO, MARILYN G | 3622 | §101 | Non-Final OA | 73d overdue | Pending | Dec 20, 2023 |
| 18387081 | CROSS-DOMAIN RECOMMENDATION VIA CONTRASTIVE LEARNING OF USER BEHAVIORS IN ATTENTIVE SEQUENCE MODELS | LOHARIKAR, ANAND R | 3689 | §102 | Non-Final OA | 19d | Pending | Nov 06, 2023 |
| 17832415 | Automated Model Selection | MAIDO, MAGGIE T | 2129 | §103 | Final Rejection | 46d overdue | Pending | Jun 03, 2022 |
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