Prosecution Insights
Prosecution Insights
Technology area: Communications
3 pending office actions • 1 art units • 2 examiners • 0 of 3 (0%) have an AI response strategy ready
Leela AI Inc. has 3 pending office actions within the Communications technology area. These actions are being handled by 2 distinct examiners, which indicates examiner overlap within the portfolio. Specifically, RUSH, ERIC is the busiest examiner for the company, managing 2 pending office actions. All these pending office actions are concentrated within 1 distinct art unit, suggesting technical and administrative focus.
For the legal team, the fact that 2 pending office actions are with RUSH, ERIC provides an opportunity to apply consistent arguments across multiple cases. The concentration in 1 distinct art unit further streamlines the prosecution process, as the applicant only needs to navigate the practices of a single administrative group. With 3 pending office actions in total, the workload is focused. The involvement of 2 distinct examiners within a single art unit means that the applicant can develop an understanding of that specific group's interpretation of communications technology. RUSH, ERIC is the primary point of contact for the majority of the current prosecution.
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 |
|---|---|
| §103 only | 1 (33%) |
| Multi-statute (no §101) | 2 (67%) |
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 |
|---|---|---|---|
| RUSH, ERIC | 2 | 60.8% | +36.1% |
| VANCHY JR, MICHAEL J | 1 | 66.8% | +20.1% |
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 |
|---|---|---|---|
| 19011538 | METHODS AND SYSTEMS FOR TRAINING AND EXECUTION OF IMPROVED LEARNING SYSTEMS FOR IDENTIFICATION OF COMPONENTS IN TIME-BASED DATA STREAMS | VANCHY JR, MICHAEL J | — |
| 18917709 | Computer Vision Learning System | RUSH, ERIC | — |
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 3 ordered by deadline are shown.
| App # | Title | Examiner | Due in |
|---|---|---|---|
| 19093703 | METHODS AND SYSTEMS FOR EXECUTION OF IMPROVED LEARNING SYSTEMS FOR IDENTIFICATION OF RULES COMPLIANCE BY COMPONENTS IN TIME-BASED DATA STREAMS | RUSH, ERIC | 37d overdue |
| 19011538 | METHODS AND SYSTEMS FOR TRAINING AND EXECUTION OF IMPROVED LEARNING SYSTEMS FOR IDENTIFICATION OF COMPONENTS IN TIME-BASED DATA STREAMS | VANCHY JR, MICHAEL J | — |
| 18917709 | Computer Vision Learning System | RUSH, ERIC | — |
| Art Unit | Apps |
|---|---|
| 2677 | 1 |
| App # | Title | Examiner | Art Unit | Statutes | Status | Due in | AI | Filed |
|---|---|---|---|---|---|---|---|---|
| 19093703 | METHODS AND SYSTEMS FOR EXECUTION OF IMPROVED LEARNING SYSTEMS FOR IDENTIFICATION OF RULES COMPLIANCE BY COMPONENTS IN TIME-BASED DATA STREAMS | RUSH, ERIC | 2677 | §103 | Non-Final OA | 37d overdue | Pending | Mar 28, 2025 |
| 19011538 | METHODS AND SYSTEMS FOR TRAINING AND EXECUTION OF IMPROVED LEARNING SYSTEMS FOR IDENTIFICATION OF COMPONENTS IN TIME-BASED DATA STREAMS | VANCHY JR, MICHAEL J | — | §103§112 | Non-Final OA | — | Pending | Jan 06, 2025 |
| 18917709 | Computer Vision Learning System | RUSH, ERIC | — | §102§103§112Other | Non-Final OA | — | Pending | Oct 16, 2024 |
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