Prosecution Insights
Prosecution Insights
11 pending office actions • 11 art units • 11 examiners • 0 of 11 (0%) have an AI response strategy ready • 1 patents granted in the last 365 days
Based on the USPTO statutory response window for each pending office action. 11 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. 11 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 | 3 (27%) |
| §103 only | 7 (64%) |
| No statute on record | 1 (9%) |
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 |
|---|---|---|---|
| ISLAM, MOHAMMAD K | 1 | 83.2% | +16.4% |
| BOGGS JR., JAMES | 1 | 60.7% | +35.9% |
| LOTTICH, JOSHUA P | 1 | 90.7% | +4.3% |
| ULLAH, ARIF | 1 | 46.9% | +37.3% |
| DUONG, HIEN LUONGVAN | 1 | 74.7% | +23.2% |
| CARVALHO, ERROL A | 1 | 15.3% | +18.5% |
| SHALU, ZELALEM W | 1 | 29.1% | +20.3% |
| TC 3600, DOCKET | 1 | 4.2% | -1.8% |
| MARU, MATIYAS T | 1 | 62.2% | +7.5% |
| WONG, WILLIAM | 1 | 30.2% | +26.9% |
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 |
|---|---|---|---|
| 18734127 | FLEXIBLE ARTIFICIAL INTELLIGENCE BASED SYSTEM WITH PROMPT ENHANCEMENT | ISLAM, MOHAMMAD K | 71d overdue |
Multi-statute / §101-driven matters, or cases in front of an examiner with an allow rate under 30%. The top 4 ordered by deadline are shown.
| App # | Title | Examiner | Due in |
|---|---|---|---|
| 17194920 | Automatically Learning Process Characteristics for Model Optimization | TC 3600, DOCKET | 73d overdue |
| 17232593 | Removing Bias from Artificial Intelligence Models | CARVALHO, ERROL A | 66d overdue |
| 17232667 | Impact Score Based Target Action Assignment | SHALU, ZELALEM W | 3d overdue |
| 18390056 | INTERFACE FOR VISUALIZING AND IMPROVING MODEL PERFORMANCE | LOTTICH, JOSHUA P | 21d |
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 7 ordered by deadline are shown.
| App # | Title | Examiner | Due in |
|---|---|---|---|
| 18491235 | AUTOMATICALLY LEARNING PROCESS CHARACTERISTICS FOR MODEL OPTIMIZATION | ULLAH, ARIF | 114d overdue |
| 18734127 | FLEXIBLE ARTIFICIAL INTELLIGENCE BASED SYSTEM WITH PROMPT ENHANCEMENT | ISLAM, MOHAMMAD K | 71d overdue |
| 17232593 | Removing Bias from Artificial Intelligence Models | CARVALHO, ERROL A | 66d overdue |
| 18606523 | ENTERPRISE-SPECIFIC CONTEXT-AWARE AUGMENTED ANALYTICS | BOGGS JR., JAMES | 25d overdue |
| 17232667 | Impact Score Based Target Action Assignment | SHALU, ZELALEM W | 3d overdue |
| 16576449 | Using Routing Rules to Generate Custom Models For Deployment as a Set | WONG, WILLIAM | 13d |
| 18471202 | USER INTERFACE FOR COLLABORATIVE ANALYTICS PLATFORM | DUONG, HIEN LUONGVAN | 33d |
| Art Unit | Apps |
|---|---|
| 2653 | 1 |
| 2657 | 1 |
| 2113 | 1 |
| 3625 | 1 |
| 2147 | 1 |
| 3622 | 1 |
| 2145 | 1 |
| 3600 | 1 |
| 2148 | 1 |
| 2144 | 1 |
| App # | Title | Examiner | Art Unit | Statutes | Status | Due in | AI | Filed |
|---|---|---|---|---|---|---|---|---|
| 18734127 | FLEXIBLE ARTIFICIAL INTELLIGENCE BASED SYSTEM WITH PROMPT ENHANCEMENT | ISLAM, MOHAMMAD K | 2653 | §103 | Non-Final OA | 71d overdue | Pending | Jun 05, 2024 |
| 18606523 | ENTERPRISE-SPECIFIC CONTEXT-AWARE AUGMENTED ANALYTICS | BOGGS JR., JAMES | 2657 | §103 | Non-Final OA | 25d overdue | Pending | Mar 15, 2024 |
| 18390056 | INTERFACE FOR VISUALIZING AND IMPROVING MODEL PERFORMANCE | LOTTICH, JOSHUA P | 2113 | §101 | Non-Final OA | 21d | Pending | Dec 20, 2023 |
| 18491235 | AUTOMATICALLY LEARNING PROCESS CHARACTERISTICS FOR MODEL OPTIMIZATION | ULLAH, ARIF | 3625 | Other | Non-Final OA | 114d overdue | Pending | Oct 20, 2023 |
| 18471202 | USER INTERFACE FOR COLLABORATIVE ANALYTICS PLATFORM | DUONG, HIEN LUONGVAN | 2147 | §103 | Final Rejection | 33d | Pending | Sep 20, 2023 |
| 17232593 | Removing Bias from Artificial Intelligence Models | CARVALHO, ERROL A | 3622 | §101 | Final Rejection | 66d overdue | Pending | Apr 16, 2021 |
| 17232667 | Impact Score Based Target Action Assignment | SHALU, ZELALEM W | 2145 | §103 | Non-Final OA | 3d overdue | Pending | Apr 16, 2021 |
| 17194920 | Automatically Learning Process Characteristics for Model Optimization | TC 3600, DOCKET | 3600 | §101 | Final Rejection | 73d overdue | Pending | Mar 08, 2021 |
| 17155458 | Training and Deploying Model Frontiers to Automatically Adjust to Business Realities | MARU, MATIYAS T | 2148 | §103 | Non-Final OA | 128d overdue | Pending | Jan 22, 2021 |
| 16576449 | Using Routing Rules to Generate Custom Models For Deployment as a Set | WONG, WILLIAM | 2144 | §103 | Final Rejection | 13d | Pending | Sep 19, 2019 |
| 16512647 | Analyzing Performance of Models Trained with Varying Constraints | BEAN, GRIFFIN TANNER | 2121 | §103 | Final Rejection | 54d | Pending | Jul 16, 2019 |
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