Prosecution Insights
Prosecution Insights
Technology areas: Computing & Software • Communications
6 pending office actions • 2 art units • 6 examiners • 0 of 6 (0%) have an AI response strategy ready • 3 patents granted in the last 365 days
Armada Systems, Inc. currently manages 7 pending office actions within the Communications technology area. These 7 pending office actions are distributed across 7 distinct examiners, indicating a prosecution landscape where the number of examiners matches the number of pending actions. This distribution suggests that the company's legal strategy involves navigating individual examiner preferences rather than concentrating efforts under a few specific officials. For practitioners, this means that successful outcomes depend on tailoring arguments to 7 different sets of examiner expectations.
The portfolio is currently active in 2 distinct art units. This concentration in 2 distinct art units contrasts with the 7 distinct examiners involved, suggesting that while the technical focus is narrow, the USPTO has assigned the work to a diverse group of individuals. AMY R HSU is identified as the busiest examiner for this portfolio, currently responsible for 1 pending office action. This distribution of the 7 pending office actions requires a broad management approach to ensure consistency in prosecution across the 2 distinct art units.
Based on the USPTO statutory response window for each pending office action. 1 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 only | 1 (17%) |
| §103 only | 2 (33%) |
| §112 only | 1 (17%) |
| Double-patenting only | 1 (17%) |
| Multi-statute (no §101) | 1 (17%) |
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 |
|---|---|---|---|
| HSU, AMY R | 1 | 86.6% | -1.2% |
| THOMPSON, JAMES A | 1 | 85.1% | +3.0% |
| PHAM, THIERRY L | 1 | 80.6% | +5.0% |
| SHARMA, NEERAJ | 1 | 84.7% | +12.1% |
| CHOUDHURY, RAQIUL A | 1 | 86.5% | +5.7% |
| WU, BENJAMIN C | 1 | 87.4% | +16.4% |
Cases in front of an examiner with an allow rate of 80%+ where the difficulty is Easy or Medium. The top 4 ordered by deadline are shown.
| App # | Title | Examiner | Due in |
|---|---|---|---|
| 18649868 | QUERY OF VIDEO SUBJECT MATTER | SHARMA, NEERAJ | 56d overdue |
| 19082045 | APPEARANCE INFILLING IN VIDEO | THOMPSON, JAMES A | — |
| 18820117 | NATURAL LANGUAGE STATISTICAL MODEL WITH WORKSPACES | PHAM, THIERRY L | — |
| 18649775 | CONTAINERIZED DATA CENTER APPARATUS FOR RAPID RESPONSE OR EMERGENCY DEPLOYMENT | CHOUDHURY, RAQIUL A | — |
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 |
|---|---|---|---|
| 19091656 | ROBOTIC CONTROL USING NATURAL LANGUAGE COMMANDS | HSU, AMY R | — |
| 18626231 | CLOUD-BASED FLEET AND ASSET MANAGEMENT FOR EDGE COMPUTING OF MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE WORKLOADS | WU, BENJAMIN C | — |
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 2 ordered by deadline are shown.
| App # | Title | Examiner | Due in |
|---|---|---|---|
| 18649868 | QUERY OF VIDEO SUBJECT MATTER | SHARMA, NEERAJ | 56d overdue |
| 18626231 | CLOUD-BASED FLEET AND ASSET MANAGEMENT FOR EDGE COMPUTING OF MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE WORKLOADS | WU, BENJAMIN C | — |
| Art Unit | Apps |
|---|---|
| 2659 | 1 |
| 2195 | 1 |
| App # | Title | Examiner | Art Unit | Statutes | Status | Due in | AI | Filed |
|---|---|---|---|---|---|---|---|---|
| 19091656 | ROBOTIC CONTROL USING NATURAL LANGUAGE COMMANDS | HSU, AMY R | — | §102§103Other | Non-Final OA | — | Pending | Mar 26, 2025 |
| 19082045 | APPEARANCE INFILLING IN VIDEO | THOMPSON, JAMES A | — | DP | Non-Final OA | — | Pending | Mar 17, 2025 |
| 18820117 | NATURAL LANGUAGE STATISTICAL MODEL WITH WORKSPACES | PHAM, THIERRY L | — | §103 | Non-Final OA | — | Pending | Aug 29, 2024 |
| 18649868 | QUERY OF VIDEO SUBJECT MATTER | SHARMA, NEERAJ | 2659 | §103 | Final Rejection | 56d overdue | Pending | Apr 29, 2024 |
| 18649775 | CONTAINERIZED DATA CENTER APPARATUS FOR RAPID RESPONSE OR EMERGENCY DEPLOYMENT | CHOUDHURY, RAQIUL A | — | §112 | Non-Final OA | — | Pending | Apr 29, 2024 |
| 18626231 | CLOUD-BASED FLEET AND ASSET MANAGEMENT FOR EDGE COMPUTING OF MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE WORKLOADS | WU, BENJAMIN C | 2195 | §101 | Non-Final OA | — | Pending | Apr 03, 2024 |
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