Prosecution Insights
Prosecution Insights
Technology areas: Computing & Software • Networking & Security
8 pending office actions • 3 art units • 8 examiners • 0 of 8 (0%) have an AI response strategy ready • 27 patents granted in the last 365 days
Gracenote Inc. currently has 8 pending office actions in the Computing & Software technology area. These actions are being reviewed by 8 distinct examiners, meaning every pending application is under the purview of a different official. This distribution requires the company to manage 8 separate examiner relationships simultaneously. The involvement of 8 examiners for only 8 actions ensures that no single examiner has an outsized impact on the current portfolio.
The portfolio is active across 3 distinct art units, which suggests a concentration of technical subject matter despite the number of examiners involved. Raquel Perez-Arroyo is the busiest examiner with 1 pending office action. The ratio of 8 examiners to 3 art units implies that multiple examiners within the same units are likely reviewing the company's applications. This concentration within 3 art units may allow for some consistency in the technical standards applied to the Computing & Software cases. Practitioners should focus on identifying commonalities in how these 3 units handle similar subject matter while tailoring their advocacy to the 8 different 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 + other | 2 (25%) |
| §103 only | 3 (38%) |
| §112 only | 1 (12%) |
| Double-patenting only | 2 (25%) |
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 |
|---|---|---|---|
| PEREZ-ARROYO, RAQUEL | 1 | 58.8% | +30.3% |
| HASAN, SYED HAROON | 1 | 81.6% | +15.5% |
| SALL, EL HADJI MALICK | 1 | 91.1% | -8.3% |
| MONIKANG, GEORGE C | 1 | 75.3% | +7.1% |
| CHEN, XUEMEI G | 1 | 77.0% | +25.6% |
| ZENATI, AMAL S | 1 | 79.8% | +14.7% |
| CASTRO, ALFONSO | 1 | 51.0% | +19.4% |
| HU, XIAOQIN | 1 | 61.5% | +56.2% |
Cases in front of an examiner with an allow rate of 80%+ where the difficulty is Easy or Medium. The top 1 ordered by deadline are shown.
| App # | Title | Examiner | Due in |
|---|---|---|---|
| 19174801 | MACHINE-CONTROL OF A DEVICE BASED ON MACHINE-DETECTED TRANSITIONS | SALL, EL HADJI MALICK | — |
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 |
|---|---|---|---|
| 17779538 | METHODS AND APPARATUS TO GENERATE RECOMMENDATIONS BASED ON ATTRIBUTE VECTORS | HU, XIAOQIN | 39d |
| 19275048 | METHODS AND APPARATUS TO IDENTIFY MEDIA | HASAN, SYED HAROON | — |
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 6 ordered by deadline are shown.
| App # | Title | Examiner | Due in |
|---|---|---|---|
| 17779538 | METHODS AND APPARATUS TO GENERATE RECOMMENDATIONS BASED ON ATTRIBUTE VECTORS | HU, XIAOQIN | 39d |
| 18548610 | Separating Media Content Into Program Segments and Advertisement Segments | CASTRO, ALFONSO | 41d |
| 19358007 | METHODS AND APPARATUS TO IDENTIFY MEDIA BASED ON HISTORICAL DATA | PEREZ-ARROYO, RAQUEL | — |
| 19275048 | METHODS AND APPARATUS TO IDENTIFY MEDIA | HASAN, SYED HAROON | — |
| 18999638 | KEYFRAME EXTRACTOR | CHEN, XUEMEI G | — |
| 18976016 | MACHINE-LED MOOD CHANGE | ZENATI, AMAL S | — |
| Art Unit | Apps |
|---|---|
| 2169 | 1 |
| 2421 | 1 |
| 2168 | 1 |
| App # | Title | Examiner | Art Unit | Statutes | Status | Due in | AI | Filed |
|---|---|---|---|---|---|---|---|---|
| 19358007 | METHODS AND APPARATUS TO IDENTIFY MEDIA BASED ON HISTORICAL DATA | PEREZ-ARROYO, RAQUEL | 2169 | §103 | Non-Final OA | — | Pending | Oct 14, 2025 |
| 19275048 | METHODS AND APPARATUS TO IDENTIFY MEDIA | HASAN, SYED HAROON | — | §101§103Other | Non-Final OA | — | Pending | Jul 21, 2025 |
| 19174801 | MACHINE-CONTROL OF A DEVICE BASED ON MACHINE-DETECTED TRANSITIONS | SALL, EL HADJI MALICK | — | DP | Non-Final OA | — | Pending | Apr 09, 2025 |
| 19021910 | Methods and Apparatus for Harmonic Source Enhancement | MONIKANG, GEORGE C | — | DP | Non-Final OA | — | Pending | Jan 15, 2025 |
| 18999638 | KEYFRAME EXTRACTOR | CHEN, XUEMEI G | — | §112Other | Non-Final OA | — | Pending | Dec 23, 2024 |
| 18976016 | MACHINE-LED MOOD CHANGE | ZENATI, AMAL S | — | §103Other | Non-Final OA | — | Pending | Dec 10, 2024 |
| 18548610 | Separating Media Content Into Program Segments and Advertisement Segments | CASTRO, ALFONSO | 2421 | §103Other | Final Rejection | 41d | Pending | Sep 01, 2023 |
| 17779538 | METHODS AND APPARATUS TO GENERATE RECOMMENDATIONS BASED ON ATTRIBUTE VECTORS | HU, XIAOQIN | 2168 | §101§112 | Non-Final OA | 39d | Pending | May 24, 2022 |
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