Prosecution Insights
Prosecution Insights
Technology areas: Computing & Software • Communications
6 pending office actions • 6 art units • 6 examiners • 0 of 6 (0%) have an AI response strategy ready • 3 patents granted in the last 365 days
Aible Inc. has 6 pending office actions in the Computing & Software technology area. These actions are perfectly distributed across 6 distinct examiners and 6 distinct art units, indicating a broad and varied prosecution landscape for the company. This 1 to 1 to 1 ratio between actions, examiners, and art units means that every single pending matter is being evaluated by a unique individual in a unique technical group, maximizing the diversity of feedback the company receives. Practitioners must manage 6 entirely separate prosecution tracks.
The busiest examiner, TRAN, TAN H, is responsible for 1 pending office action. This distribution shows that each of the 6 pending office actions is being handled by 6 distinct examiners, as no one holds more than 1 pending office action. For the practitioner, this suggests that there is no opportunity to leverage a relationship with a single examiner to resolve multiple pending matters. Instead, the company must manage 6 independent prosecution tracks across 6 distinct art units, which may lead to varied timelines and outcomes within the Computing & Software sector.
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 + other | 3 (50%) |
| §103 only | 1 (17%) |
| Multi-statute (no §101) | 2 (33%) |
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 |
|---|---|---|---|
| TRAN, TAN H | 1 | 60.9% | +32.6% |
| ISLAM, MOHAMMAD K | 1 | 82.9% | +17.2% |
| BOGGS JR., JAMES | 1 | 62.6% | +34.3% |
| CARVALHO, ERROL A | 1 | 15.2% | +18.3% |
| WONG, WILLIAM | 1 | 30.7% | +27.8% |
| BEAN, GRIFFIN TANNER | 1 | 28.1% | +15.3% |
Multi-statute / §101-driven matters, or cases in front of an examiner with an allow rate under 30%. The top 5 ordered by deadline are shown.
| App # | Title | Examiner | Due in |
|---|---|---|---|
| 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 |
| 16576449 | Using Routing Rules to Generate Custom Models For Deployment as a Set | WONG, WILLIAM | 13d |
| 16512647 | Analyzing Performance of Models Trained with Varying Constraints | BEAN, GRIFFIN TANNER | 54d |
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 6 ordered by deadline are shown.
| App # | Title | Examiner | Due in |
|---|---|---|---|
| 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 |
| 16576449 | Using Routing Rules to Generate Custom Models For Deployment as a Set | WONG, WILLIAM | 13d |
| 16512647 | Analyzing Performance of Models Trained with Varying Constraints | BEAN, GRIFFIN TANNER | 54d |
| 18741352 | User Interface for Impact Analysis | TRAN, TAN H | — |
| Art Unit | Apps |
|---|---|
| 2141 | 1 |
| 2653 | 1 |
| 2657 | 1 |
| 3622 | 1 |
| 2144 | 1 |
| 2121 | 1 |
| App # | Title | Examiner | Art Unit | Statutes | Status | Due in | AI | Filed |
|---|---|---|---|---|---|---|---|---|
| 18741352 | User Interface for Impact Analysis | TRAN, TAN H | 2141 | §103Other | Non-Final OA | — | Pending | Jun 12, 2024 |
| 18734127 | FLEXIBLE ARTIFICIAL INTELLIGENCE BASED SYSTEM WITH PROMPT ENHANCEMENT | ISLAM, MOHAMMAD K | 2653 | §101§102§103§112 | Final Rejection | 71d overdue | Pending | Jun 05, 2024 |
| 18606523 | ENTERPRISE-SPECIFIC CONTEXT-AWARE AUGMENTED ANALYTICS | BOGGS JR., JAMES | 2657 | §103§112 | Final Rejection | 25d overdue | Pending | Mar 15, 2024 |
| 17232593 | Removing Bias from Artificial Intelligence Models | CARVALHO, ERROL A | 3622 | §101§112 | Final Rejection | 66d overdue | Pending | Apr 16, 2021 |
| 16576449 | Using Routing Rules to Generate Custom Models For Deployment as a Set | WONG, WILLIAM | 2144 | §103§112 | Non-Final OA | 13d | Pending | Sep 19, 2019 |
| 16512647 | Analyzing Performance of Models Trained with Varying Constraints | BEAN, GRIFFIN TANNER | 2121 | §101§103 | Final Rejection | 54d | Pending | Jul 16, 2019 |
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