Prosecution Insights
Prosecution Insights
Technology area: Transportation, E-Commerce & Mechanical Systems
3 pending office actions • 3 art units • 3 examiners • 0 of 3 (0%) have an AI response strategy ready
Allio Fintech Corporation has 3 pending office actions in the Transportation, E-Commerce & Mechanical Systems technology area. These actions are spread across 3 distinct art units, showing a diverse technical footprint within this sector. The prosecution involves 3 distinct examiners, meaning each of the 3 pending actions is being reviewed by a different individual. This distribution across 3 distinct examiners requires the company to manage three separate examiner relationships.
The busiest examiner is APPLE, KIRSTEN SACHWITZ, who is responsible for 1 pending office action. Since there are 3 pending actions and 3 distinct examiners, the workload is completely decentralized. The company must manage 3 separate examiner relationships across 3 distinct art units, requiring attention to the specific preferences of each examiner involved. Practitioners should note that each of the 3 pending office actions is being handled by a unique examiner, which may lead to different prosecution outcomes across the 3 distinct art units.
Based on the USPTO statutory response window for each pending office action. 1 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 | 3 (100%) |
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 |
|---|---|---|---|
| APPLE, KIRSTEN SACHWITZ | 1 | 60.4% | +4.2% |
| JAMES, GREGORY MARK | 1 | 19.4% | +14.2% |
| SHARON, AYAL I | 1 | 43.4% | +29.4% |
Multi-statute / §101-driven matters, or cases in front of an examiner with an allow rate under 30%. The top 3 ordered by deadline are shown.
| App # | Title | Examiner | Due in |
|---|---|---|---|
| 18585913 | METHOD AND SYSTEM FOR FULLY AUTOMATED FULL SPECTRUM DYNAMIC EFFICIENT FRONTIER INVESTMENT ALLOCATION | SHARON, AYAL I | 62d |
| 19281410 | ADAPTIVE LEARNING FOR INVESTMENTS | APPLE, KIRSTEN SACHWITZ | — |
| 19257080 | ADAPTIVE LEARNING FOR INVESTMENTS | JAMES, GREGORY MARK | — |
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 2 ordered by deadline are shown.
| App # | Title | Examiner | Due in |
|---|---|---|---|
| 18585913 | METHOD AND SYSTEM FOR FULLY AUTOMATED FULL SPECTRUM DYNAMIC EFFICIENT FRONTIER INVESTMENT ALLOCATION | SHARON, AYAL I | 62d |
| 19257080 | ADAPTIVE LEARNING FOR INVESTMENTS | JAMES, GREGORY MARK | — |
| Art Unit | Apps |
|---|---|
| 3693 | 1 |
| 3692 | 1 |
| 3695 | 1 |
| App # | Title | Examiner | Art Unit | Statutes | Status | Due in | AI | Filed |
|---|---|---|---|---|---|---|---|---|
| 19281410 | ADAPTIVE LEARNING FOR INVESTMENTS | APPLE, KIRSTEN SACHWITZ | 3693 | §101§103 | Non-Final OA | — | Pending | Jul 25, 2025 |
| 19257080 | ADAPTIVE LEARNING FOR INVESTMENTS | JAMES, GREGORY MARK | 3692 | §101§103 | Non-Final OA | — | Pending | Jul 01, 2025 |
| 18585913 | METHOD AND SYSTEM FOR FULLY AUTOMATED FULL SPECTRUM DYNAMIC EFFICIENT FRONTIER INVESTMENT ALLOCATION | SHARON, AYAL I | 3695 | §101§103 | Final Rejection | 62d | Pending | Feb 23, 2024 |
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