Prosecution Insights
Prosecution Insights
Technology areas: Computing & Software • Communications
3 pending office actions • 3 art units • 3 examiners • 0 of 3 (0%) have an AI response strategy ready • 7 patents granted in the last 365 days
Smart Engines Service LLC is currently managing 3 pending office actions within the Computing & Software technology area. These matters are distributed among 3 distinct examiners, which indicates that each individual case is receiving a unique evaluation. This lack of examiner overlap suggests that the company must tailor its arguments to 3 different sets of examiner preferences and communication styles to move its applications forward.
The 3 pending office actions are situated within 3 distinct art units. This distribution across art units implies that the technical subject matter of the 3 pending office actions is diverse within the Computing & Software field. TRAN, DUY ANH is the busiest examiner for this portfolio, currently overseeing 1 pending office action, which confirms that the workload is evenly spread across the 3 distinct examiners.
For a practitioner, the presence of 3 distinct examiners across 3 distinct art units suggests a need for diverse advocacy across multiple technical sub-sectors. The fact that the busiest examiner, TRAN, DUY ANH, holds only 1 pending office action means that no single official dominates the current prosecution landscape. This portfolio structure requires monitoring the specific tendencies of each examiner to ensure consistent results across the 3 distinct art units involved.
Based on the USPTO statutory response window for each pending office action. 2 of the docket's apps have a known mailing date; the rest are excluded from the tile counts.
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 | 1 (33%) |
| §103 only | 1 (33%) |
| §102 only | 1 (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, DUY ANH | 1 | 80.0% | +17.0% |
| MAUNI, HUMAIRA ZAHIN | 1 | 46.7% | +38.1% |
| HONORE, EVEL NMN | 1 | 51.9% | +26.4% |
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 |
|---|---|---|---|
| 18377206 | APPROXIMATE MODELING OF NEXT COMBINED RESULT FOR STOPPING TEXT-FIELD RECOGNITION IN A VIDEO STREAM | TRAN, DUY ANH | 141d overdue |
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 |
|---|---|---|---|
| 17495642 | DISTANCE-BASED PAIRS GENERATION FOR TRAINING METRIC NEURAL NETWORKS | HONORE, EVEL NMN | — |
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 |
|---|---|---|---|
| 18377206 | APPROXIMATE MODELING OF NEXT COMBINED RESULT FOR STOPPING TEXT-FIELD RECOGNITION IN A VIDEO STREAM | TRAN, DUY ANH | 141d overdue |
| 18104043 | NEURON-BY-NEURON QUANTIZATION FOR EFFICIENT TRAINING OF LOW-BIT QUANTIZED NEURAL NETWORKS | MAUNI, HUMAIRA ZAHIN | 92d overdue |
| 17495642 | DISTANCE-BASED PAIRS GENERATION FOR TRAINING METRIC NEURAL NETWORKS | HONORE, EVEL NMN | — |
| Art Unit | Apps |
|---|---|
| 2674 | 1 |
| 2141 | 1 |
| 2142 | 1 |
| App # | Title | Examiner | Art Unit | Statutes | Status | Due in | AI | Filed |
|---|---|---|---|---|---|---|---|---|
| 18377206 | APPROXIMATE MODELING OF NEXT COMBINED RESULT FOR STOPPING TEXT-FIELD RECOGNITION IN A VIDEO STREAM | TRAN, DUY ANH | 2674 | §102Other | Non-Final OA | 141d overdue | Pending | Oct 05, 2023 |
| 18104043 | NEURON-BY-NEURON QUANTIZATION FOR EFFICIENT TRAINING OF LOW-BIT QUANTIZED NEURAL NETWORKS | MAUNI, HUMAIRA ZAHIN | 2141 | §103 | Final Rejection | 92d overdue | Pending | Jan 31, 2023 |
| 17495642 | DISTANCE-BASED PAIRS GENERATION FOR TRAINING METRIC NEURAL NETWORKS | HONORE, EVEL NMN | 2142 | §101§103 | Non-Final OA | — | Pending | Oct 06, 2021 |
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