Prosecution Insights
Prosecution Insights
Technology areas: Communications • Transportation, E-Commerce & Mechanical Systems
4 pending office actions • 4 art units • 4 examiners • 0 of 4 (0%) have an AI response strategy ready • 5 patents granted in the last 365 days
Mitchell International Inc. is currently active in the Transportation, E-Commerce & Mechanical Systems technology area. The company's patent portfolio includes 5 pending office actions. This workload is notably distributed across the patent office, as there are 5 distinct art units involved in the examination process. Such a high ratio of art units to pending actions suggests that the company's innovations span a variety of technical classifications within its primary technology area.
The prosecution of these applications is managed by 5 distinct examiners. This diversity in personnel means the company must navigate different examiner expectations and procedural styles simultaneously. HUYNH, EMILY is identified as the busiest examiner for the portfolio, currently overseeing 1 busiest examiner pending office action. Because each of the 5 pending office actions is handled by a different examiner, the company does not have a single primary point of contact for its overall prosecution strategy, requiring a broad and adaptable approach to its legal filings.
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 (50%) |
| §103 only | 2 (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 |
|---|---|---|---|
| CADEAU, WEDNEL | 1 | 71.8% | +19.2% |
| ARTIMEZ, DANA FERREN | 1 | 57.7% | +38.5% |
| CHEN, WENREN | 1 | 15.2% | +28.7% |
| WHITAKER, ANDREW B | 1 | 18.6% | +19.0% |
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 |
|---|---|---|---|
| 18462246 | VEHICLE REPAIR ESTIMATION GUIDED BY ARTIFICIAL INTELLIGENCE | CHEN, WENREN | 22d |
| 18460787 | MACHINE LEARNING PREDICTION OF REPAIR OR TOTAL LOSS ACTIONS | WHITAKER, ANDREW B | — |
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 |
|---|---|---|---|
| 18462246 | VEHICLE REPAIR ESTIMATION GUIDED BY ARTIFICIAL INTELLIGENCE | CHEN, WENREN | 22d |
| 18378117 | SYSTEMS AND METHODS FOR AUTOMATICALLY LINKING DIAGNOSTIC SCAN DATA | ARTIMEZ, DANA FERREN | 46d |
| 18887947 | PREDICTION OF MEDICAL TREATMENTS FOR BODILY INJURIES BASED ON SEVERITY OF VEHICLE DAMAGE | CADEAU, WEDNEL | — |
| 18460787 | MACHINE LEARNING PREDICTION OF REPAIR OR TOTAL LOSS ACTIONS | WHITAKER, ANDREW B | — |
| Art Unit | Apps |
|---|---|
| 2632 | 1 |
| 3667 | 1 |
| 3626 | 1 |
| 3629 | 1 |
| App # | Title | Examiner | Art Unit | Statutes | Status | Due in | AI | Filed |
|---|---|---|---|---|---|---|---|---|
| 18887947 | PREDICTION OF MEDICAL TREATMENTS FOR BODILY INJURIES BASED ON SEVERITY OF VEHICLE DAMAGE | CADEAU, WEDNEL | 2632 | §103 | Non-Final OA | — | Pending | Sep 17, 2024 |
| 18378117 | SYSTEMS AND METHODS FOR AUTOMATICALLY LINKING DIAGNOSTIC SCAN DATA | ARTIMEZ, DANA FERREN | 3667 | §103 | Final Rejection | 46d | Pending | Oct 09, 2023 |
| 18462246 | VEHICLE REPAIR ESTIMATION GUIDED BY ARTIFICIAL INTELLIGENCE | CHEN, WENREN | 3626 | §101§103 | Final Rejection | 22d | Pending | Sep 06, 2023 |
| 18460787 | MACHINE LEARNING PREDICTION OF REPAIR OR TOTAL LOSS ACTIONS | WHITAKER, ANDREW B | 3629 | §101§103 | Final Rejection | — | Pending | Sep 05, 2023 |
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