Technology areas: Computing & Software • Communications
15 pending office actions • 1 client • 15 examiners • 13 art units • 0 of 15 (0%) have an AI response strategy ready • 13 patents granted in the last 365 days
ZILKA-KOTAB, PC- NVID operates in Communications and has 11 pending office actions. The profile reports 1303 career applications, supplying a broad historical base for assessing prosecution activity in this technology area. Its resolved allowance rate is 96.49%, which means the reported resolved outcomes are predominantly allowances.
Allowed matters involve an average of 4.22 office actions. This action average adds an important dimension to the allowance result: a favorable resolution rate does not mean that examination generally ends after a single exchange. For practitioners, the profile suggests planning for sustained prosecution work in communications cases, including repeated opportunities to clarify claim scope and address examiner objections. The pending docket is smaller than the historical application base, while still representing active matters requiring current attention.
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 | 3 (20%) |
| §103 only | 6 (40%) |
| Multi-statute (no §101) | 6 (40%) |
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 |
|---|---|---|---|
| UDDIN, MD I | 1 | 77.0% | +73.5% |
| TRAN, JENNY NGAN | 1 | 41.7% | +50.0% |
| NGUYEN, CHAN T H | 1 | 86.5% | +2.7% |
| TSWEI, YU-JANG | 1 | 83.7% | +15.9% |
| PROTAZI, BRIGITER DIVULALE | 1 | 50.0% | +100.0% |
| TORRES, JOSE | 1 | 82.0% | +12.2% |
| BAYNES, SAMUEL DAVID | 1 | 85.7% | +22.2% |
| PHAKOUSONH, DARAVANH | 1 | 16.7% | +50.0% |
| PHAM, NAM D | 1 | 90.8% | +1.8% |
| FLORA, NURUN N | 1 | 86.0% | +1.9% |
Cases in front of an examiner with an allow rate of 80%+ where the difficulty is Easy or Medium. The top 3 ordered by deadline are shown.
| App # | Title | Examiner | Due in |
|---|---|---|---|
| 18915138 | REDUCING LEVEL OF DETAIL OF A POLYGON MESH TO DECREASE A COMPLEXITY OF RENDERED GEOMETRY WITHIN A SCENE | TSWEI, YU-JANG | — |
| 18637288 | REDUCING FALSE-NEGATIVES IN 3D OBJECT DETECTION VIA MULTI-STAGE TRAINING | BAYNES, SAMUEL DAVID | — |
| 18531544 | VOXEL-TO-3D CONTENT GENERATOR | FLORA, NURUN N | — |
Multi-statute / §101-driven matters, or cases in front of an examiner with an allow rate under 30%. The top 8 ordered by deadline are shown.
| App # | Title | Examiner | Due in |
|---|---|---|---|
| 18212629 | SYNTHETIC DATASET GENERATOR | LUDWIG, PETER L | 30d overdue |
| 19094441 | CLOSEST SILHOUETTE QUERIES FOR RAY TRAVERSAL | TRAN, JENNY NGAN | — |
| 19084565 | CAMERA WITH COMPRESSIVE SENSING | NGUYEN, CHAN T H | — |
| 18892186 | SINGLE IMAGE TO REALISTIC 3D OBJECT GENERATION VIA SEMI-SUPERVISED 2D AND 3D JOINT TRAINING | PROTAZI, BRIGITER DIVULALE | — |
| 18882629 | DUAL FORMULATION FOR A COMPUTER VISION RETENTION MODEL | TORRES, JOSE | — |
| 18602951 | COMPRESSION OF MACHINE LEARNING MODELS VIA SPARSIFICATION AND QUANTIZATION | PHAKOUSONH, DARAVANH | — |
| 18593742 | VARIATIONAL INFERENCING BY A DIFFUSION MODEL | PHAM, NAM D | — |
| 18232757 | MACHINE LEARNING FOR MASK OPTIMIZATION IN INVERSE LITHOGRAPHY TECHNOLOGIES | KUDO, KEN | — |
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 |
|---|---|---|---|
| 18818422 | Untitled | UDDIN, MD I | — |
| 19094441 | CLOSEST SILHOUETTE QUERIES FOR RAY TRAVERSAL | TRAN, JENNY NGAN | — |
| 18915138 | REDUCING LEVEL OF DETAIL OF A POLYGON MESH TO DECREASE A COMPLEXITY OF RENDERED GEOMETRY WITHIN A SCENE | TSWEI, YU-JANG | — |
| 18892186 | SINGLE IMAGE TO REALISTIC 3D OBJECT GENERATION VIA SEMI-SUPERVISED 2D AND 3D JOINT TRAINING | PROTAZI, BRIGITER DIVULALE | — |
| 18882629 | DUAL FORMULATION FOR A COMPUTER VISION RETENTION MODEL | TORRES, JOSE | — |
| 18637288 | REDUCING FALSE-NEGATIVES IN 3D OBJECT DETECTION VIA MULTI-STAGE TRAINING | BAYNES, SAMUEL DAVID | — |
| 18602951 | COMPRESSION OF MACHINE LEARNING MODELS VIA SPARSIFICATION AND QUANTIZATION | PHAKOUSONH, DARAVANH | — |
| Client (Assignee) | Pending OAs |
|---|---|
| NVIDIA | 15 |
| Art Unit | Apps |
|---|---|
| 2169 | 1 |
| 2638 | 1 |
| 2612 | 1 |
| 2664 | 1 |
| 2665 | 1 |
| 2121 | 1 |
| 2487 | 1 |
| 2619 | 1 |
| 2400 | 1 |
| 2671 | 1 |
| App # | Title | Client | Examiner | Art Unit | Statutes | Status | Due in | AI | Filed |
|---|---|---|---|---|---|---|---|---|---|
| 18818422 | Untitled | Nvidia Corporation | UDDIN, MD I | 2169 | §103 | Non-Final OA | — | Pending | |
| 19094441 | CLOSEST SILHOUETTE QUERIES FOR RAY TRAVERSAL | Nvidia Corporation | TRAN, JENNY NGAN | §103§112 | Non-Final OA | — | Pending | Mar 28, 2025 | |
| 19084565 | CAMERA WITH COMPRESSIVE SENSING | Nvidia Corporation | NGUYEN, CHAN T H | 2638 | §102§103 | Non-Final OA | — | Pending | Mar 19, 2025 |
| 18915138 | REDUCING LEVEL OF DETAIL OF A POLYGON MESH TO DECREASE A COMPLEXITY OF RENDERED GEOMETRY WITHIN A SCENE | Nvidia Corporation | TSWEI, YU-JANG | §103 | Final Rejection | — | Pending | Oct 14, 2024 | |
| 18892186 | SINGLE IMAGE TO REALISTIC 3D OBJECT GENERATION VIA SEMI-SUPERVISED 2D AND 3D JOINT TRAINING | Nvidia Corporation | PROTAZI, BRIGITER DIVULALE | 2612 | §103§112 | Final Rejection | — | Pending | Sep 20, 2024 |
| 18882629 | DUAL FORMULATION FOR A COMPUTER VISION RETENTION MODEL | Nvidia Corporation | TORRES, JOSE | 2664 | §103§112 | Non-Final OA | — | Pending | Sep 11, 2024 |
| 18637288 | REDUCING FALSE-NEGATIVES IN 3D OBJECT DETECTION VIA MULTI-STAGE TRAINING | Nvidia Corporation | BAYNES, SAMUEL DAVID | 2665 | §103 | Final Rejection | — | Pending | Apr 16, 2024 |
| 18602951 | COMPRESSION OF MACHINE LEARNING MODELS VIA SPARSIFICATION AND QUANTIZATION | Nvidia Corporation | PHAKOUSONH, DARAVANH | 2121 | §101§112 | Non-Final OA | — | Pending | Mar 12, 2024 |
| 18593742 | VARIATIONAL INFERENCING BY A DIFFUSION MODEL | Nvidia Corporation | PHAM, NAM D | 2487 | §102§103 | Non-Final OA | — | Pending | Mar 01, 2024 |
| 18531544 | VOXEL-TO-3D CONTENT GENERATOR | Nvidia Corporation | FLORA, NURUN N | 2619 | §103 | Non-Final OA | — | Pending | Dec 06, 2023 |
| 18243555 | LANDMARK DETECTION WITH AN ITERATIVE NEURAL NETWORK | Nvidia Corporation | CZEKAJ, DAVID J | 2400 | §103 | Non-Final OA | — | Pending | Sep 07, 2023 |
| 18232757 | MACHINE LEARNING FOR MASK OPTIMIZATION IN INVERSE LITHOGRAPHY TECHNOLOGIES | Nvidia Corporation | KUDO, KEN | 2671 | §102§103§112 | Final Rejection | — | Pending | Aug 10, 2023 |
| 18212629 | SYNTHETIC DATASET GENERATOR | Nvidia Corporation | LUDWIG, PETER L | 3627 | §101§103 | Final Rejection | 30d overdue | Pending | Jun 21, 2023 |
| 18203552 | DYNAMIC NEURAL NETWORK MODEL SPARSIFICATION | Nvidia Corporation | GORMLEY, AARON PATRICK | 2148 | §101§103 | Final Rejection | — | Pending | May 30, 2023 |
| 17893038 | PERFORMING VISUAL RELATIONAL REASONING | Nvidia Corporation | DUONG, JOHNNYKHOI BAO | 2667 | §103 | Non-Final OA | 4d overdue | Pending | Aug 22, 2022 |
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Start Free Trial96.5% of resolved applications prosecuted by Zilka-Kotab, Pc- Nvid were allowed, across 1,303 applications in USPTO records.
4.22 office actions per allowed application on average.
15 pending office actions as of October 2026, for 1 client, before 15 examiners in 13 art units.
