Prosecution Insights
Prosecution Insights
Technology area: Computing & Software
5 pending office actions • 5 art units • 5 examiners • 0 of 5 (0%) have an AI response strategy ready • 46 patents granted in the last 365 days
EMC IP Holding Company LLC has 5 pending office actions within the Computing & Software technology area. These actions are distributed across 5 distinct examiners and 5 distinct art units. HOANG, AMY P is the busiest examiner for the company, currently managing 1 pending office action.
The distribution of 5 pending office actions across 5 distinct examiners suggests a broad prosecution front where no 1 examiner or art unit dominates the current workload. Each of the 5 distinct art units represents a different point of contact for the portfolio's active matters. This variety implies that the applicant's technology spans several different classifications within the computing sector. With 5 distinct examiners involved, the prosecution team must adapt to 5 distinct sets of internal office behaviors. No examiner is handling more than 1 pending action, which prevents any 1 individual from having outsized influence over the portfolio's immediate success. This decentralized approach across 5 distinct art units requires a versatile strategy to address the unique requirements of each examiner.
Based on the USPTO statutory response window for each pending office action. 4 of the docket's apps have a known mailing date; the rest are excluded from the tile counts.
Every pending office action with a known statutory deadline, placed on a days-until-due axis. Dots left of Today are overdue; the further right, the more runway. Cases that share a deadline window stack vertically. 4 of the docket's apps have a known mailing date.
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 | 3 (60%) |
| 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 |
|---|---|---|---|
| HOANG, AMY P | 1 | 72.0% | +65.3% |
| PHAM, JESSICA THUY | 1 | 18.2% | +90.0% |
| KAPOOR, DEVAN | 1 | 7.1% | +11.1% |
| SMITH, KEVIN LEE | 1 | 36.9% | +20.0% |
| PHUNG, STEVEN HUYNH | 1 | 73.9% | +30.2% |
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 |
|---|---|---|---|
| 17507649 | Identifying Test Dependencies Using Binary Neural Networks | KAPOOR, DEVAN | 53d overdue |
| 17404011 | METHOD, ELECTRONIC DEVICE, AND COMPUTER PROGRAM PRODUCT FOR SAMPLE MANAGEMENT | PHUNG, STEVEN HUYNH | 15d |
| 18230029 | EDGE DATA GATHERING USING REINFORCEMENT LEARNING AND DISTRIBUTION CLIQUES | HOANG, AMY P | 20d |
| 17487489 | METHOD, ELECTRONIC DEVICE, AND COMPUTER PROGRAM PRODUCT FOR MANAGING DISK | SMITH, KEVIN LEE | 34d |
| 17451793 | ADVERSARIAL IMAGE GENERATOR TO IMPROVE DNN IMAGE SEGMENTATION MODEL ROBUSTNESS FOR AUTONOMOUS VEHICLE | PHAM, JESSICA THUY | — |
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 |
|---|---|---|---|
| 17507649 | Identifying Test Dependencies Using Binary Neural Networks | KAPOOR, DEVAN | 53d overdue |
| 17404011 | METHOD, ELECTRONIC DEVICE, AND COMPUTER PROGRAM PRODUCT FOR SAMPLE MANAGEMENT | PHUNG, STEVEN HUYNH | 15d |
| 18230029 | EDGE DATA GATHERING USING REINFORCEMENT LEARNING AND DISTRIBUTION CLIQUES | HOANG, AMY P | 20d |
| 17487489 | METHOD, ELECTRONIC DEVICE, AND COMPUTER PROGRAM PRODUCT FOR MANAGING DISK | SMITH, KEVIN LEE | 34d |
| 17451793 | ADVERSARIAL IMAGE GENERATOR TO IMPROVE DNN IMAGE SEGMENTATION MODEL ROBUSTNESS FOR AUTONOMOUS VEHICLE | PHAM, JESSICA THUY | — |
| Art Unit | Apps |
|---|---|
| 2143 | 1 |
| 2121 | 1 |
| 2126 | 1 |
| 2122 | 1 |
| 2125 | 1 |
| App # | Title | Examiner | Art Unit | Statutes | Status | Due in | AI | Filed |
|---|---|---|---|---|---|---|---|---|
| 18230029 | EDGE DATA GATHERING USING REINFORCEMENT LEARNING AND DISTRIBUTION CLIQUES | HOANG, AMY P | 2143 | §101§103 | Final Rejection | 20d | Pending | Aug 03, 2023 |
| 17451793 | ADVERSARIAL IMAGE GENERATOR TO IMPROVE DNN IMAGE SEGMENTATION MODEL ROBUSTNESS FOR AUTONOMOUS VEHICLE | PHAM, JESSICA THUY | 2121 | §103§112 | Final Rejection | — | Pending | Oct 21, 2021 |
| 17507649 | Identifying Test Dependencies Using Binary Neural Networks | KAPOOR, DEVAN | 2126 | §101§103 | Non-Final OA | 53d overdue | Pending | Oct 21, 2021 |
| 17487489 | METHOD, ELECTRONIC DEVICE, AND COMPUTER PROGRAM PRODUCT FOR MANAGING DISK | SMITH, KEVIN LEE | 2122 | §101§103 | Non-Final OA | 34d | Pending | Sep 28, 2021 |
| 17404011 | METHOD, ELECTRONIC DEVICE, AND COMPUTER PROGRAM PRODUCT FOR SAMPLE MANAGEMENT | PHUNG, STEVEN HUYNH | 2125 | §101 | Final Rejection | 15d | Pending | Aug 17, 2021 |
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