Prosecution Insights
Prosecution Insights
4 pending office actions • 4 art units • 4 examiners • 0 of 4 (0%) have an AI response strategy ready • 8 patents granted in the last 365 days
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 | 2 (50%) |
| §103 only | 1 (25%) |
| §102 only | 1 (25%) |
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 | 79.9% | +17.3% |
| SAINI, AMANDEEP SINGH | 1 | 89.7% | +8.4% |
| MAUNI, HUMAIRA ZAHIN | 1 | 45.8% | +51.0% |
| HONORE, EVEL NMN | 1 | 48.0% | +24.6% |
Multi-statute / §101-driven matters, or cases in front of an examiner with an allow rate under 30%. The top 2 ordered by deadline are shown.
| App # | Title | Examiner | Due in |
|---|---|---|---|
| 18137181 | METHOD FOR DETECTING (RECOGNIZING) THE FACT OF PRESENTING A DIGITAL COPY OF A DOCUMENT IN THE FORM OF A SCREEN CAPTURE | SAINI, AMANDEEP SINGH | — |
| 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 |
| 2662 | 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 |
| 18137181 | METHOD FOR DETECTING (RECOGNIZING) THE FACT OF PRESENTING A DIGITAL COPY OF A DOCUMENT IN THE FORM OF A SCREEN CAPTURE | SAINI, AMANDEEP SINGH | 2662 | §101§103 | Non-Final OA | — | Pending | Apr 20, 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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