Prosecution Insights
Prosecution Insights
Technology areas: Computing & Software • Transportation, E-Commerce & Mechanical Systems
3 pending office actions • 3 art units • 3 examiners • 0 of 3 (0%) have an AI response strategy ready
Arterys Inc. currently manages 3 pending office actions within the Computing & Software technology area. This portfolio is distributed across 3 distinct examiners and 3 distinct art units, indicating a broad spread of examination oversight rather than concentration in a single specialized group. Such a distribution suggests that the underlying technologies may touch upon diverse sub-sectors within the software domain. The presence of multiple art units implies that the company's innovations are being evaluated against different sets of technical precedents.
The busiest examiner for this portfolio is HANKS, BENJAMIN L, who is currently responsible for 1 pending office action. Because the total number of pending actions is 3 and the busiest examiner only handles 1, the workload is perfectly balanced across the examiners. This lack of concentration means that prosecution progress is not dependent on the schedule or tendencies of a single individual, but rather subject to the varied timelines of multiple USPTO personnel. For a practitioner, this distribution requires managing separate examiner relationships simultaneously. Each of the distinct examiners holds an equal share of the pending workload, ensuring that no single examiner's backlog disproportionately affects the entire portfolio's advancement.
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 | 1 (33%) |
| §103 only | 1 (33%) |
| Multi-statute (no §101) | 1 (33%) |
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 |
|---|---|---|---|
| HANKS, BENJAMIN L | 1 | 21.4% | +30.3% |
| MCINTOSH, ANDREW T | 1 | 77.4% | +18.2% |
| CHUANG, SU-TING | 1 | 51.3% | +39.4% |
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 |
|---|---|---|---|
| 17270810 | DEEP LEARNING-BASED COREGISTRATION | CHUANG, SU-TING | 17d overdue |
| 17287450 | SYSTEMS AND METHODS FOR TRACKING, ACCESSING AND MERGING PROTECTED HEALTH INFORMATION | HANKS, BENJAMIN L | 39d |
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 |
|---|---|---|---|
| 17270810 | DEEP LEARNING-BASED COREGISTRATION | CHUANG, SU-TING | 17d overdue |
| 17287450 | SYSTEMS AND METHODS FOR TRACKING, ACCESSING AND MERGING PROTECTED HEALTH INFORMATION | HANKS, BENJAMIN L | 39d |
| 17285731 | MACHINE LEARNING-BASED AUTOMATED ABNORMALITY DETECTION IN MEDICAL IMAGES AND PRESENTATION THEREOF | MCINTOSH, ANDREW T | — |
| Art Unit | Apps |
|---|---|
| 3684 | 1 |
| 2144 | 1 |
| 2146 | 1 |
| App # | Title | Examiner | Art Unit | Statutes | Status | Due in | AI | Filed |
|---|---|---|---|---|---|---|---|---|
| 17287450 | SYSTEMS AND METHODS FOR TRACKING, ACCESSING AND MERGING PROTECTED HEALTH INFORMATION | HANKS, BENJAMIN L | 3684 | §101§103 | Non-Final OA | 39d | Pending | Apr 21, 2021 |
| 17285731 | MACHINE LEARNING-BASED AUTOMATED ABNORMALITY DETECTION IN MEDICAL IMAGES AND PRESENTATION THEREOF | MCINTOSH, ANDREW T | 2144 | §103 | Non-Final OA | — | Pending | Apr 15, 2021 |
| 17270810 | DEEP LEARNING-BASED COREGISTRATION | CHUANG, SU-TING | 2146 | §103§112 | Non-Final OA | 17d overdue | Pending | Feb 23, 2021 |
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