Prosecution Insights
Prosecution Insights
Technology areas: Computing & Software • Transportation, E-Commerce & Mechanical Systems
7 pending office actions • 6 art units • 7 examiners • 0 of 7 (0%) have an AI response strategy ready • 8 patents granted in the last 365 days
Pagerduty Inc. is currently managing 7 pending office actions within the Transportation, E-Commerce & Mechanical Systems technology area. This workload is distributed across 7 distinct examiners, meaning every pending action is being reviewed by a different individual. The portfolio is also spread across 6 distinct art units, indicating a high level of technical diversity in the company's current patent filings. This broad distribution suggests that the company's innovation spans 6 specialized classifications within the broader technology area.
JOSEPH M WAESCO is identified as the busiest examiner for the company, with 1 busiest examiner pending office action. Because there are 7 pending office actions and 7 distinct examiners, the workload is distributed with no single examiner handling more than 1 case. This ratio of 7 examiners to 7 pending actions across 6 distinct art units requires the company to manage a wide array of examiner relationships simultaneously. Practitioners should be prepared for varied prosecution styles given that 7 distinct examiners are involved in the 7 pending office actions. This decentralized environment means that the outcome of 1 matter is unlikely to directly influence the others through examiner overlap.
Based on the USPTO statutory response window for each pending office action. 3 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. 3 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 | 2 (29%) |
| §101 + other | 3 (43%) |
| §103 only | 1 (14%) |
| Multi-statute (no §101) | 1 (14%) |
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 |
|---|---|---|---|
| WAESCO, JOSEPH M | 1 | 46.5% | +42.6% |
| KASSIM, HAFIZ A | 1 | 44.7% | +53.8% |
| HO, ANDY | 1 | 91.6% | +7.6% |
| PATEL, HIREN P | 1 | 78.9% | +36.2% |
| WERNER, MARSHALL L | 1 | 66.1% | +40.7% |
| RUHL, DENNIS WILLIAM | 1 | 26.3% | +23.5% |
| ZENG, WENWEI | 1 | — | — |
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.
Multi-statute / §101-driven matters, or cases in front of an examiner with an allow rate under 30%. The top 6 ordered by deadline are shown.
| App # | Title | Examiner | Due in |
|---|---|---|---|
| 18160755 | Alert Grouping For Noise Reduction | ZENG, WENWEI | 51d overdue |
| 18475845 | Smart Incident Status Updates | RUHL, DENNIS WILLIAM | 15d |
| 18800634 | MODIFYING AN EVENT NOTIFICATION CONFIGURATION BASED ON INCIDENT RESOLUTION DATA | KASSIM, HAFIZ A | 42d |
| 18923762 | Outage Risk Detection Alerts | WAESCO, JOSEPH M | — |
| 18427956 | Cached Variables For Event Management | PATEL, HIREN P | — |
| 18479862 | Incident Occurrence Prediction Using Classifiers | WERNER, MARSHALL L | — |
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 |
|---|---|---|---|
| 18475845 | Smart Incident Status Updates | RUHL, DENNIS WILLIAM | 15d |
| 18800634 | MODIFYING AN EVENT NOTIFICATION CONFIGURATION BASED ON INCIDENT RESOLUTION DATA | KASSIM, HAFIZ A | 42d |
| 18923762 | Outage Risk Detection Alerts | WAESCO, JOSEPH M | — |
| 18427956 | Cached Variables For Event Management | PATEL, HIREN P | — |
| 18479862 | Incident Occurrence Prediction Using Classifiers | WERNER, MARSHALL L | — |
| Art Unit | Apps |
|---|---|
| 3625 | 1 |
| 3623 | 1 |
| 2196 | 1 |
| 2125 | 1 |
| 3626 | 1 |
| 2146 | 1 |
| App # | Title | Examiner | Art Unit | Statutes | Status | Due in | AI | Filed |
|---|---|---|---|---|---|---|---|---|
| 18923762 | Outage Risk Detection Alerts | WAESCO, JOSEPH M | 3625 | §101 | Final Rejection | — | Pending | Oct 23, 2024 |
| 18800634 | MODIFYING AN EVENT NOTIFICATION CONFIGURATION BASED ON INCIDENT RESOLUTION DATA | KASSIM, HAFIZ A | 3623 | §101 | Final Rejection | 42d | Pending | Aug 12, 2024 |
| 18798975 | Auto Pause Incident Notification | HO, ANDY | — | §103 | Non-Final OA | — | Pending | Aug 09, 2024 |
| 18427956 | Cached Variables For Event Management | PATEL, HIREN P | 2196 | §103§112 | Non-Final OA | — | Pending | Jan 31, 2024 |
| 18479862 | Incident Occurrence Prediction Using Classifiers | WERNER, MARSHALL L | 2125 | §101§102§103 | Non-Final OA | — | Pending | Oct 03, 2023 |
| 18475845 | Smart Incident Status Updates | RUHL, DENNIS WILLIAM | 3626 | §101§102§103 | Final Rejection | 15d | Pending | Sep 27, 2023 |
| 18160755 | Alert Grouping For Noise Reduction | ZENG, WENWEI | 2146 | §101§103 | Final Rejection | 51d overdue | Pending | Jan 27, 2023 |
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