Prosecution Insights
Prosecution Insights
Technology areas: Computing & Software • Communications
8 pending office actions • 3 art units • 8 examiners • 0 of 8 (0%) have an AI response strategy ready • 7 patents granted in the last 365 days
Softeye Inc. manages a portfolio in the Communications technology area with 8 pending office actions. These actions are distributed among 8 distinct examiners, which means every pending matter is being handled by a different individual. The workload is spread across 3 distinct art units, indicating that the company's communications technologies are being reviewed by several different specialized groups.
SHIRLEY D. HICKS is the busiest examiner, currently overseeing 1 pending office action. Since the total pending office actions is 8, the busiest examiner pending count of 1 shows that the work is distributed, with no single examiner handling more than one case. This high number of 8 distinct examiners for the pending actions suggests a very diverse prosecution landscape.
The use of 3 distinct art units for the pending office actions implies that multiple applications are grouped within the same technical divisions. However, the distinct examiners ensure that each active matter receives a unique perspective. For a company in the Communications sector, managing these separate reviewers across the 3 distinct art units requires significant coordination to maintain consistency.
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 | 4 (50%) |
| §103 only | 2 (25%) |
| §102 only | 1 (12%) |
| Multi-statute (no §101) | 1 (12%) |
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 |
|---|---|---|---|
| HICKS, SHIRLEY D. | 1 | 60.8% | +50.5% |
| CORRIELUS, JEAN M | 1 | 84.0% | +12.5% |
| GILLIARD, DELOMIA L | 1 | 89.6% | +10.4% |
| AKHAVANNIK, HADI | 1 | 85.9% | +13.1% |
| YANG, JIANXUN | 1 | 74.1% | +19.3% |
| BAYNES, SAMUEL DAVID | 1 | 90.0% | +16.7% |
| MASTERS, KRISTEN MICHELLE | 1 | 64.7% | +24.1% |
| DEMETER, HILINA K | 1 | 72.1% | +18.8% |
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 |
|---|---|---|---|
| 18745462 | NETWORK INFRASTRUCTURE FOR USER-SPECIFIC GENERATIVE INTELLIGENCE | MASTERS, KRISTEN MICHELLE | 2d overdue |
| 19081911 | FOUNDATION MODEL PIPELINE FOR REAL-TIME EMBEDDED DEVICES | CORRIELUS, JEAN M | — |
| 18983220 | MACHINE-LEARNING ALGORITHMS FOR LOW-POWER APPLICATIONS | GILLIARD, DELOMIA L | — |
| 18983242 | MACHINE-LEARNING ALGORITHMS FOR LOW-POWER APPLICATIONS | AKHAVANNIK, HADI | — |
| 18983169 | MACHINE-LEARNING ALGORITHMS FOR LOW-POWER APPLICATIONS | BAYNES, SAMUEL DAVID | — |
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 8 ordered by deadline are shown.
| App # | Title | Examiner | Due in |
|---|---|---|---|
| 19081951 | FOUNDATION MODEL PIPELINE FOR REAL-TIME EMBEDDED DEVICES | HICKS, SHIRLEY D. | 98d overdue |
| 18745462 | NETWORK INFRASTRUCTURE FOR USER-SPECIFIC GENERATIVE INTELLIGENCE | MASTERS, KRISTEN MICHELLE | 2d overdue |
| 19081911 | FOUNDATION MODEL PIPELINE FOR REAL-TIME EMBEDDED DEVICES | CORRIELUS, JEAN M | — |
| 18983220 | MACHINE-LEARNING ALGORITHMS FOR LOW-POWER APPLICATIONS | GILLIARD, DELOMIA L | — |
| 18983242 | MACHINE-LEARNING ALGORITHMS FOR LOW-POWER APPLICATIONS | AKHAVANNIK, HADI | — |
| 18983261 | MACHINE-LEARNING ALGORITHMS FOR LOW-POWER APPLICATIONS | YANG, JIANXUN | — |
| 18983169 | MACHINE-LEARNING ALGORITHMS FOR LOW-POWER APPLICATIONS | BAYNES, SAMUEL DAVID | — |
| 18613100 | APPARATUS AND METHODS FOR AUGMENTING VISION WITH REGION-OF-INTEREST BASED PROCESSING | DEMETER, HILINA K | — |
| Art Unit | Apps |
|---|---|
| 2168 | 1 |
| 2659 | 1 |
| 2617 | 1 |
| App # | Title | Examiner | Art Unit | Statutes | Status | Due in | AI | Filed |
|---|---|---|---|---|---|---|---|---|
| 19081951 | FOUNDATION MODEL PIPELINE FOR REAL-TIME EMBEDDED DEVICES | HICKS, SHIRLEY D. | 2168 | §102 | Final Rejection | 98d overdue | Pending | Mar 17, 2025 |
| 19081911 | FOUNDATION MODEL PIPELINE FOR REAL-TIME EMBEDDED DEVICES | CORRIELUS, JEAN M | — | §101§102 | Non-Final OA | — | Pending | Mar 17, 2025 |
| 18983220 | MACHINE-LEARNING ALGORITHMS FOR LOW-POWER APPLICATIONS | GILLIARD, DELOMIA L | — | §102§103 | Non-Final OA | — | Pending | Dec 16, 2024 |
| 18983242 | MACHINE-LEARNING ALGORITHMS FOR LOW-POWER APPLICATIONS | AKHAVANNIK, HADI | — | §101§102§103 | Non-Final OA | — | Pending | Dec 16, 2024 |
| 18983261 | MACHINE-LEARNING ALGORITHMS FOR LOW-POWER APPLICATIONS | YANG, JIANXUN | — | §103 | Non-Final OA | — | Pending | Dec 16, 2024 |
| 18983169 | MACHINE-LEARNING ALGORITHMS FOR LOW-POWER APPLICATIONS | BAYNES, SAMUEL DAVID | — | §101§103§112 | Non-Final OA | — | Pending | Dec 16, 2024 |
| 18745462 | NETWORK INFRASTRUCTURE FOR USER-SPECIFIC GENERATIVE INTELLIGENCE | MASTERS, KRISTEN MICHELLE | 2659 | §101§102§103 | Final Rejection | 2d overdue | Pending | Jun 17, 2024 |
| 18613100 | APPARATUS AND METHODS FOR AUGMENTING VISION WITH REGION-OF-INTEREST BASED PROCESSING | DEMETER, HILINA K | 2617 | §103 | Non-Final OA | — | Pending | Mar 21, 2024 |
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