Prosecution Insights
Prosecution Insights
Technology areas: Computing & Software • Communications • Transportation, E-Commerce & Mechanical Systems
5 pending office actions • 4 art units • 5 examiners • 0 of 5 (0%) have an AI response strategy ready • 5 patents granted in the last 365 days
Vuno Inc. currently manages 5 pending office actions within the Transportation, E-Commerce & Mechanical Systems technology area. This workload is distributed across 4 distinct art units, indicating a diverse range of technical classifications for their patent applications. The prosecution involves 5 distinct examiners, suggesting that each pending action is being handled by a different individual. This lack of examiner overlap means that the company must navigate 5 unique sets of examination preferences simultaneously.
Sara Jessica Morice De Vargas is identified as the busiest examiner for this portfolio, currently overseeing 1 pending office action. This distribution shows that no single examiner holds a concentrated portion of the company's active cases. Practitioners should prepare for varied examination styles across the 4 distinct art units involved. The high ratio of distinct examiners to pending actions suggests a decentralized prosecution environment where strategies may need to be tailored for each individual case. This diversity in examination could lead to varying timelines for the 5 pending office actions. Success in this portfolio requires a flexible approach that addresses the specific standards of each of the 5 distinct examiners.
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 only | 1 (20%) |
| §101 + other | 2 (40%) |
| §103 only | 1 (20%) |
| Multi-statute (no §101) | 1 (20%) |
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 |
|---|---|---|---|
| MORICE DE VARGAS, SARA JESSICA | 1 | 8.8% | +22.1% |
| HOLMES, REX R | 1 | 80.2% | +17.9% |
| ORANGE, DAVID BENJAMIN | 1 | 32.1% | +28.8% |
| DEVORE, CHRISTOPHER DILLON | 1 | 57.1% | +36.1% |
| KHATTAR, RAJESH | 1 | 36.7% | +35.2% |
Multi-statute / §101-driven matters, or cases in front of an examiner with an allow rate under 30%. The top 4 ordered by deadline are shown.
| App # | Title | Examiner | Due in |
|---|---|---|---|
| 18260461 | METHOD FOR CLASSIFICATION USING DEEP LEARNING MODEL | ORANGE, DAVID BENJAMIN | 12d |
| 17356111 | METHOD TO DISPLAY LESION READINGS RESULT | KHATTAR, RAJESH | 56d |
| 18844841 | METHOD AND DEVICE FOR ANALYZING ELECTROCARDIOGRAM DATA | MORICE DE VARGAS, SARA JESSICA | — |
| 18231110 | PREDICTION METHOD USING STATIC AND DYNAMIC DATA | HOLMES, REX R | — |
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 5 ordered by deadline are shown.
| App # | Title | Examiner | Due in |
|---|---|---|---|
| 18260461 | METHOD FOR CLASSIFICATION USING DEEP LEARNING MODEL | ORANGE, DAVID BENJAMIN | 12d |
| 17356111 | METHOD TO DISPLAY LESION READINGS RESULT | KHATTAR, RAJESH | 56d |
| 18844841 | METHOD AND DEVICE FOR ANALYZING ELECTROCARDIOGRAM DATA | MORICE DE VARGAS, SARA JESSICA | — |
| 18231110 | PREDICTION METHOD USING STATIC AND DYNAMIC DATA | HOLMES, REX R | — |
| 17598289 | METHOD FOR IMPROVING REPRODUCTION PERFORMANCE OF TRAINED DEEP NEURAL NETWORK MODEL AND DEVICE USING SAME | DEVORE, CHRISTOPHER DILLON | — |
| Art Unit | Apps |
|---|---|
| 3681 | 1 |
| 2663 | 1 |
| 2147 | 1 |
| 3684 | 1 |
| App # | Title | Examiner | Art Unit | Statutes | Status | Due in | AI | Filed |
|---|---|---|---|---|---|---|---|---|
| 18844841 | METHOD AND DEVICE FOR ANALYZING ELECTROCARDIOGRAM DATA | MORICE DE VARGAS, SARA JESSICA | 3681 | §101§103 | Non-Final OA | — | Pending | Sep 06, 2024 |
| 18231110 | PREDICTION METHOD USING STATIC AND DYNAMIC DATA | HOLMES, REX R | — | §101§102 | Non-Final OA | — | Pending | Aug 07, 2023 |
| 18260461 | METHOD FOR CLASSIFICATION USING DEEP LEARNING MODEL | ORANGE, DAVID BENJAMIN | 2663 | §103§112 | Non-Final OA | 12d | Pending | Jul 05, 2023 |
| 17598289 | METHOD FOR IMPROVING REPRODUCTION PERFORMANCE OF TRAINED DEEP NEURAL NETWORK MODEL AND DEVICE USING SAME | DEVORE, CHRISTOPHER DILLON | 2147 | §103Other | Final Rejection | — | Pending | Sep 26, 2021 |
| 17356111 | METHOD TO DISPLAY LESION READINGS RESULT | KHATTAR, RAJESH | 3684 | §101 | Final Rejection | 56d | Pending | Jun 23, 2021 |
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