Technology area: Transportation, E-Commerce & Mechanical Systems
10 pending office actions • 8 art units • 9 examiners • 0 of 10 (0%) have an AI response strategy ready • 18 patents granted in the last 365 days
CVS Pharmacy Inc. maintains a broad prosecution profile with 8 pending office actions in the Transportation, E-Commerce & Mechanical Systems technology area. This portfolio is fragmented, as the 8 actions are spread across 6 distinct art units. Such a wide distribution across art units suggests that the company's patenting activities cover a diverse range of technical implementations within the broader e-commerce and mechanical sectors.
The human element of the prosecution is similarly varied, involving 7 distinct examiners. YESILDAG, MEHMET is the busiest examiner for the portfolio, currently handling 2 of the pending office actions. For the practitioner, this data indicates a multifaceted management task, as most of the 8 pending office actions are being reviewed by different examiners in different art units. The high number of 6 distinct art units means that prosecution strategies must be tailored to multiple different supervisory patent examiners and unit-specific guidelines, with very little opportunity for consolidated arguments across the portfolio.
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 | 3 (30%) |
| §101 + other | 3 (30%) |
| Multi-statute (no §101) | 4 (40%) |
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 |
|---|---|---|---|
| YESILDAG, MEHMET | 2 | 34.0% | +28.1% |
| FRY, PATRICK B | 1 | 53.9% | +8.0% |
| ROSEN, NICHOLAS D | 1 | 70.4% | +22.4% |
| SEREBOFF, NEAL | 1 | 28.2% | +33.3% |
| ROTONDI, CONNOR JON | 1 | 0.0% | +0.0% |
| PARK, GRACE A | 1 | 76.3% | +17.6% |
| RANDALL, JR., KELVIN L | 1 | 44.8% | +16.3% |
| HICKS, AUSTIN JAMES | 1 | 75.0% | +25.8% |
| SHELDEN, BION A | 1 | 22.5% | +18.7% |
Multi-statute / §101-driven matters, or cases in front of an examiner with an allow rate under 30%. The top 8 ordered by deadline are shown.
| App # | Title | Examiner | Due in |
|---|---|---|---|
| 18496282 | Agitator Configuration for Modular Dispensers | RANDALL, JR., KELVIN L | 30d overdue |
| 18757266 | NUTRITION AND DEPRESCRIPTION | SEREBOFF, NEAL | 22d |
| 19030136 | AUTOMATED PILL FULFILLMENT SYSTEMS AND METHODS | FRY, PATRICK B | — |
| 19021943 | Intelligent Pre-Processing and Fulfillment of Mixed Orders | ROSEN, NICHOLAS D | — |
| 18789398 | Enterprise Workload Sharing System | YESILDAG, MEHMET | — |
| 18395003 | WATER PRE-TREATMENT SYSTEM FOR MEDICAL DEVICE | ROTONDI, CONNOR JON | — |
| 18388414 | MACHINE LEARNING SYSTEM FOR PREDICTING BIOMARKERS | PARK, GRACE A | — |
| 18213062 | GENERALIZED MACHINE LEARNING PIPELINE | HICKS, AUSTIN JAMES | — |
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 |
|---|---|---|---|
| 18496282 | Agitator Configuration for Modular Dispensers | RANDALL, JR., KELVIN L | 30d overdue |
| 18757266 | NUTRITION AND DEPRESCRIPTION | SEREBOFF, NEAL | 22d |
| 19021943 | Intelligent Pre-Processing and Fulfillment of Mixed Orders | ROSEN, NICHOLAS D | — |
| 18789398 | Enterprise Workload Sharing System | YESILDAG, MEHMET | — |
| 18388414 | MACHINE LEARNING SYSTEM FOR PREDICTING BIOMARKERS | PARK, GRACE A | — |
| 18213062 | GENERALIZED MACHINE LEARNING PIPELINE | HICKS, AUSTIN JAMES | — |
| 17902608 | AI BASED METHODS AND SYSTEMS FOR TRACKING CHRONIC CONDITIONS | SHELDEN, BION A | — |
| 17885069 | Enterprise Workload Sharing System | YESILDAG, MEHMET | — |
| Art Unit | Apps |
|---|---|
| 3624 | 2 |
| 3731 | 1 |
| 3689 | 1 |
| 3683 | 1 |
| 1779 | 1 |
| 3651 | 1 |
| 2142 | 1 |
| 3685 | 1 |
| App # | Title | Examiner | Art Unit | Statutes | Status | Due in | AI | Filed |
|---|---|---|---|---|---|---|---|---|
| 19030136 | AUTOMATED PILL FULFILLMENT SYSTEMS AND METHODS | FRY, PATRICK B | 3731 | §102§103 | Final Rejection | — | Pending | Jan 17, 2025 |
| 19021943 | Intelligent Pre-Processing and Fulfillment of Mixed Orders | ROSEN, NICHOLAS D | 3689 | §101DP | Non-Final OA | — | Pending | Jan 15, 2025 |
| 18789398 | Enterprise Workload Sharing System | YESILDAG, MEHMET | 3624 | §101 | Final Rejection | — | Pending | Jul 30, 2024 |
| 18757266 | NUTRITION AND DEPRESCRIPTION | SEREBOFF, NEAL | 3683 | §101 | Non-Final OA | 22d | Pending | Jun 27, 2024 |
| 18395003 | WATER PRE-TREATMENT SYSTEM FOR MEDICAL DEVICE | ROTONDI, CONNOR JON | 1779 | §103§112 | Non-Final OA | — | Pending | Dec 22, 2023 |
| 18388414 | MACHINE LEARNING SYSTEM FOR PREDICTING BIOMARKERS | PARK, GRACE A | — | §102§103 | Non-Final OA | — | Pending | Nov 09, 2023 |
| 18496282 | Agitator Configuration for Modular Dispensers | RANDALL, JR., KELVIN L | 3651 | §102§103 | Non-Final OA | 30d overdue | Pending | Oct 27, 2023 |
| 18213062 | GENERALIZED MACHINE LEARNING PIPELINE | HICKS, AUSTIN JAMES | 2142 | §101§102§103§112 | Final Rejection | — | Pending | Jun 22, 2023 |
| 17902608 | AI BASED METHODS AND SYSTEMS FOR TRACKING CHRONIC CONDITIONS | SHELDEN, BION A | 3685 | §101§103 | Non-Final OA | — | Pending | Sep 02, 2022 |
| 17885069 | Enterprise Workload Sharing System | YESILDAG, MEHMET | 3624 | §101 | Final Rejection | — | Pending | Aug 10, 2022 |
IP Author helps IP teams respond to office actions faster with AI-generated responses, examiner analytics, and prosecution intelligence.
Start Free Trial