Technology areas: Computing & Software • Communications
8 pending office actions • 8 art units • 8 examiners • 0 of 8 (0%) have an AI response strategy ready • 6 patents granted in the last 365 days
Softeye Inc. has 8 pending office actions in Communications. The docket spans 8 distinct examiners and 8 distinct art units, indicating that each recorded matter is situated in its own examiner and art-unit context.
HICKS, SHIRLEY D. is the busiest examiner, with 1 pending matter. The absence of a concentrated examiner assignment makes centralized deadline control and consistent claim-position review more useful than relying on a single examiner playbook.
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 |
|---|---|---|---|
| CORRIELUS, JEAN M | 1 | 84.1% | +12.5% |
| HICKS, SHIRLEY D. | 1 | 60.3% | +51.2% |
| GILLIARD, DELOMIA L | 1 | 89.6% | +10.3% |
| YANG, JIANXUN | 1 | 74.3% | +19.1% |
| BAYNES, SAMUEL DAVID | 1 | 85.7% | +22.2% |
| AKHAVANNIK, HADI | 1 | 85.9% | +13.1% |
| MASTERS, KRISTEN MICHELLE | 1 | 65.5% | +20.6% |
| 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 | — |
| 18983169 | MACHINE-LEARNING ALGORITHMS FOR LOW-POWER APPLICATIONS | BAYNES, SAMUEL DAVID | — |
| 18983242 | MACHINE-LEARNING ALGORITHMS FOR LOW-POWER APPLICATIONS | AKHAVANNIK, HADI | — |
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 | — |
| 18983261 | MACHINE-LEARNING ALGORITHMS FOR LOW-POWER APPLICATIONS | YANG, JIANXUN | — |
| 18983169 | MACHINE-LEARNING ALGORITHMS FOR LOW-POWER APPLICATIONS | BAYNES, SAMUEL DAVID | — |
| 18983242 | MACHINE-LEARNING ALGORITHMS FOR LOW-POWER APPLICATIONS | AKHAVANNIK, HADI | — |
| 18613100 | APPARATUS AND METHODS FOR AUGMENTING VISION WITH REGION-OF-INTEREST BASED PROCESSING | DEMETER, HILINA K | — |
| Art Unit | Apps |
|---|---|
| 2159 | 1 |
| 2168 | 1 |
| 2661 | 1 |
| 2662 | 1 |
| 2665 | 1 |
| 2676 | 1 |
| 2659 | 1 |
| 2617 | 1 |
| App # | Title | Examiner | Art Unit | Statutes | Status | Due in | AI | Filed |
|---|---|---|---|---|---|---|---|---|
| 19081911 | FOUNDATION MODEL PIPELINE FOR REAL-TIME EMBEDDED DEVICES | CORRIELUS, JEAN M | 2159 | §101§102 | Non-Final OA | — | Pending | Mar 17, 2025 |
| 19081951 | FOUNDATION MODEL PIPELINE FOR REAL-TIME EMBEDDED DEVICES | HICKS, SHIRLEY D. | 2168 | §102 | Final Rejection | 98d overdue | Pending | Mar 17, 2025 |
| 18983220 | MACHINE-LEARNING ALGORITHMS FOR LOW-POWER APPLICATIONS | GILLIARD, DELOMIA L | 2661 | §102§103 | Non-Final OA | — | Pending | Dec 16, 2024 |
| 18983261 | MACHINE-LEARNING ALGORITHMS FOR LOW-POWER APPLICATIONS | YANG, JIANXUN | 2662 | §103 | Non-Final OA | — | Pending | Dec 16, 2024 |
| 18983169 | MACHINE-LEARNING ALGORITHMS FOR LOW-POWER APPLICATIONS | BAYNES, SAMUEL DAVID | 2665 | §101§103§112 | Non-Final OA | — | Pending | Dec 16, 2024 |
| 18983242 | MACHINE-LEARNING ALGORITHMS FOR LOW-POWER APPLICATIONS | AKHAVANNIK, HADI | 2676 | §101§102§103 | 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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Start Free TrialSofteye Inc. has 8 pending office actions as of October 2026, spread across 8 art units and 8 examiners.
Jean M Corrielus has the most, with 1 of Softeye Inc.'s pending office actions.
Mostly Communications (USPTO Tech Center 2600), based on where its pending office actions are examined.
