Prosecution Insights
Prosecution Insights
Technology area: Computing & Software
3 pending office actions • 2 art units • 3 examiners • 0 of 3 (0%) have an AI response strategy ready • 5 patents granted in the last 365 days
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 | 1 (33%) |
| §101 + other | 1 (33%) |
| §103 only | 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 |
|---|---|---|---|
| WEHOVZ, OSCAR | 1 | 64.9% | +29.2% |
| SITIRICHE, LUIS A | 1 | 77.8% | +21.3% |
| CHUANG, SU-TING | 1 | 50.9% | +38.9% |
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 |
|---|---|---|---|
| 19159230 | METHODS, DEVICES AND SYSTEMS FOR RERANKING DIGITAL INFORMATION WITH A MACHINE LEARNING MODEL | WEHOVZ, OSCAR | — |
| 15360485 | SYSTEM AND METHOD FOR MODIFYING A KNOWLEDGE REPRESENTATION BASED ON A MACHINE LEARNING CLASSIFIER | CHUANG, SU-TING | — |
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 |
|---|---|---|---|
| 19159230 | METHODS, DEVICES AND SYSTEMS FOR RERANKING DIGITAL INFORMATION WITH A MACHINE LEARNING MODEL | WEHOVZ, OSCAR | — |
| 18518808 | METHOD, SYSTEM, AND COMPUTER PROGRAM FOR USER-DRIVEN DYNAMIC GENERATION OF SEMANTIC NETWORKS AND MEDIA SYNTHESIS | SITIRICHE, LUIS A | — |
| 15360485 | SYSTEM AND METHOD FOR MODIFYING A KNOWLEDGE REPRESENTATION BASED ON A MACHINE LEARNING CLASSIFIER | CHUANG, SU-TING | — |
| Art Unit | Apps |
|---|---|
| 2161 | 1 |
| 2146 | 1 |
| App # | Title | Examiner | Art Unit | Statutes | Status | Due in | AI | Filed |
|---|---|---|---|---|---|---|---|---|
| 19159230 | METHODS, DEVICES AND SYSTEMS FOR RERANKING DIGITAL INFORMATION WITH A MACHINE LEARNING MODEL | WEHOVZ, OSCAR | 2161 | §101§103§112 | Non-Final OA | — | Pending | Aug 22, 2025 |
| 18518808 | METHOD, SYSTEM, AND COMPUTER PROGRAM FOR USER-DRIVEN DYNAMIC GENERATION OF SEMANTIC NETWORKS AND MEDIA SYNTHESIS | SITIRICHE, LUIS A | — | §103Other | Non-Final OA | — | Pending | Nov 24, 2023 |
| 15360485 | SYSTEM AND METHOD FOR MODIFYING A KNOWLEDGE REPRESENTATION BASED ON A MACHINE LEARNING CLASSIFIER | CHUANG, SU-TING | 2146 | §101 | Non-Final OA | — | Pending | Nov 23, 2016 |
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