Prosecution Insights
Prosecution Insights
Technology area: Transportation, E-Commerce & Mechanical Systems
6 pending office actions • 5 art units • 6 examiners • 0 of 6 (0%) have an AI response strategy ready • 2 patents granted in the last 365 days
Zs Associates Inc. is currently managing 6 pending office actions within the Transportation, E-Commerce & Mechanical Systems technology area. These actions are distributed across 6 distinct examiners, which means each examiner is responsible for a single action. The busiest examiner, DETWEILER, JAMES M, is responsible for 1 pending office action, illustrating that the workload is not concentrated under any single individual.
The portfolio spans 5 distinct art units, which is fewer than the 6 distinct examiners involved. This distribution across 5 distinct art units may require the applicant to adapt to varying examination standards and procedural nuances. With 6 pending office actions, the prosecution strategy must address different styles of rejection and negotiation. The fact that these matters exist across the assigned units highlights an active, diverse, and ongoing prosecution phase for the company.
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 | 3 (50%) |
| §101 + other | 3 (50%) |
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 |
|---|---|---|---|
| DETWEILER, JAMES M | 1 | 38.8% | +43.5% |
| NOVAK, REBECCA R | 1 | 5.9% | +6.7% |
| LEE, WILLIAM MICHAEL | 1 | — | — |
| YESILDAG, MEHMET | 1 | 34.0% | +28.1% |
| BARR, MARY EVANGELINE | 1 | 35.8% | +32.6% |
| GO, JOHN PHILIP | 1 | 34.1% | +43.1% |
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 |
|---|---|---|---|
| 18658025 | SYSTEMS AND METHODS FOR MACHINE LEARNING MODEL TO CALCULATE USER ELASTICITY AND GENERATE RECOMMENDATIONS USING HETEROGENEOUS DATA | DETWEILER, JAMES M | 67d overdue |
| 18102619 | METHODS AND APPARATUS FOR MACHINE LEARNING TO CALCULATE A PATIENT BURDEN SCORE FOR PARTICIPATION IN A CLINICAL TRIAL | GO, JOHN PHILIP | 2d overdue |
| 18103711 | ELECTRONIC PLATFORM FOR PRESENTING BURDEN SCORES FOR PARTICIPATION IN A CLINICAL TRIAL | BARR, MARY EVANGELINE | 26d |
| 18215096 | SYSTEMS AND METHODS FOR ARTIFICIAL INTELLIGENCE GUIDED SELLLING | YESILDAG, MEHMET | 34d |
| 18439958 | ELECTRONIC PLATFORM FOR IMPLEMENTING A MULTI-MODEL ARCHITECTURE FOR LINKING SPEAKER AND ATTENDEE ENTITY PROFILES | NOVAK, REBECCA R | — |
| 18407056 | MACHINE LEARNING ARCHITECTURE FOR DETECTING EARLY ADOPTERS | LEE, WILLIAM MICHAEL | — |
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 4 ordered by deadline are shown.
| App # | Title | Examiner | Due in |
|---|---|---|---|
| 18658025 | SYSTEMS AND METHODS FOR MACHINE LEARNING MODEL TO CALCULATE USER ELASTICITY AND GENERATE RECOMMENDATIONS USING HETEROGENEOUS DATA | DETWEILER, JAMES M | 67d overdue |
| 18102619 | METHODS AND APPARATUS FOR MACHINE LEARNING TO CALCULATE A PATIENT BURDEN SCORE FOR PARTICIPATION IN A CLINICAL TRIAL | GO, JOHN PHILIP | 2d overdue |
| 18103711 | ELECTRONIC PLATFORM FOR PRESENTING BURDEN SCORES FOR PARTICIPATION IN A CLINICAL TRIAL | BARR, MARY EVANGELINE | 26d |
| 18215096 | SYSTEMS AND METHODS FOR ARTIFICIAL INTELLIGENCE GUIDED SELLLING | YESILDAG, MEHMET | 34d |
| Art Unit | Apps |
|---|---|
| 3621 | 1 |
| 3629 | 1 |
| 3624 | 1 |
| 3682 | 1 |
| 3681 | 1 |
| App # | Title | Examiner | Art Unit | Statutes | Status | Due in | AI | Filed |
|---|---|---|---|---|---|---|---|---|
| 18658025 | SYSTEMS AND METHODS FOR MACHINE LEARNING MODEL TO CALCULATE USER ELASTICITY AND GENERATE RECOMMENDATIONS USING HETEROGENEOUS DATA | DETWEILER, JAMES M | 3621 | §101DP | Final Rejection | 67d overdue | Pending | May 08, 2024 |
| 18439958 | ELECTRONIC PLATFORM FOR IMPLEMENTING A MULTI-MODEL ARCHITECTURE FOR LINKING SPEAKER AND ATTENDEE ENTITY PROFILES | NOVAK, REBECCA R | 3629 | §101 | Non-Final OA | — | Pending | Feb 13, 2024 |
| 18407056 | MACHINE LEARNING ARCHITECTURE FOR DETECTING EARLY ADOPTERS | LEE, WILLIAM MICHAEL | — | §101§103 | Non-Final OA | — | Pending | Jan 08, 2024 |
| 18215096 | SYSTEMS AND METHODS FOR ARTIFICIAL INTELLIGENCE GUIDED SELLLING | YESILDAG, MEHMET | 3624 | §101§102 | Final Rejection | 34d | Pending | Jun 27, 2023 |
| 18103711 | ELECTRONIC PLATFORM FOR PRESENTING BURDEN SCORES FOR PARTICIPATION IN A CLINICAL TRIAL | BARR, MARY EVANGELINE | 3682 | §101 | Final Rejection | 26d | Pending | Jan 31, 2023 |
| 18102619 | METHODS AND APPARATUS FOR MACHINE LEARNING TO CALCULATE A PATIENT BURDEN SCORE FOR PARTICIPATION IN A CLINICAL TRIAL | GO, JOHN PHILIP | 3681 | §101 | Non-Final OA | 2d overdue | Pending | Jan 27, 2023 |
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