Prosecution Insights
Prosecution Insights
9 pending office actions • 5 art units • 9 examiners • 0 of 9 (0%) have an AI response strategy ready
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 | 5 (56%) |
| §103 only | 4 (44%) |
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 |
|---|---|---|---|
| ELLIS, MATTHEW J | 1 | 68.9% | +31.0% |
| CORRIELUS, JEAN M | 1 | 84.0% | +12.8% |
| KAMRUZZAMAN, MD | 1 | — | — |
| ROSTAMI, MOHAMMAD S | 1 | 67.2% | +26.0% |
| VO, TED T | 1 | 81.1% | +9.4% |
| DUAN, VIVIAN WEIJIA | 1 | 64.3% | +55.0% |
| LEE, TSU-CHANG | 1 | 72.8% | +14.4% |
| LI, LIANG Y | 1 | 61.3% | +69.0% |
| SITIRICHE, LUIS A | 1 | 77.6% | +21.4% |
Cases in front of an examiner with an allow rate of 80%+ where the difficulty is Easy or Medium. The top 2 ordered by deadline are shown.
| App # | Title | Examiner | Due in |
|---|---|---|---|
| 18973356 | LABEL MODIFYING TECHNIQUE | CORRIELUS, JEAN M | — |
| 18589738 | DETERMINING NATURAL LANGUAGE DESCRIPTION OF A SOFTWARE CODE | VO, TED T | — |
Multi-statute / §101-driven matters, or cases in front of an examiner with an allow rate under 30%. The top 5 ordered by deadline are shown.
| App # | Title | Examiner | Due in |
|---|---|---|---|
| 18611085 | MODIFYING SOFTWARE CODE | KAMRUZZAMAN, MD | — |
| 18605017 | MACHINE LEARNING MODELS THAT GENERATE DIVERSE EMBEDDED VECTORS, ACCORDING TO AN IMPLEMENTATION | ROSTAMI, MOHAMMAD S | — |
| 18589818 | DETERMINING SOURCE CODE OF A SOFTWARE CODE | DUAN, VIVIAN WEIJIA | — |
| 18482258 | DETERMINING SIMILARITY SAMPLES USING A MACHINE LEARNING OPERATION WITH CLUSTERING | LEE, TSU-CHANG | — |
| 18482251 | DETERMINING SIMILARITY SAMPLES USING A MACHINE LEARNING OPERATION | LI, LIANG Y | — |
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 7 ordered by deadline are shown.
| App # | Title | Examiner | Due in |
|---|---|---|---|
| 18973376 | DATA SELECTION TECHNIQUE FOR MACHINE LEARNING MODEL TRAINING | ELLIS, MATTHEW J | — |
| 18973356 | LABEL MODIFYING TECHNIQUE | CORRIELUS, JEAN M | — |
| 18605017 | MACHINE LEARNING MODELS THAT GENERATE DIVERSE EMBEDDED VECTORS, ACCORDING TO AN IMPLEMENTATION | ROSTAMI, MOHAMMAD S | — |
| 18589818 | DETERMINING SOURCE CODE OF A SOFTWARE CODE | DUAN, VIVIAN WEIJIA | — |
| 18482258 | DETERMINING SIMILARITY SAMPLES USING A MACHINE LEARNING OPERATION WITH CLUSTERING | LEE, TSU-CHANG | — |
| 18482251 | DETERMINING SIMILARITY SAMPLES USING A MACHINE LEARNING OPERATION | LI, LIANG Y | — |
| 18179248 | CLUSTERING ANALYSIS FOR DEDUPLICATION OF TRAINING SET SAMPLES FOR MACHINE LEARNING BASED COMPUTER THREAT ANALYSIS | SITIRICHE, LUIS A | — |
| Art Unit | Apps |
|---|---|
| 2191 | 3 |
| 2153 | 1 |
| 2159 | 1 |
| 2128 | 1 |
| 2143 | 1 |
| App # | Title | Examiner | Art Unit | Statutes | Status | Due in | AI | Filed |
|---|---|---|---|---|---|---|---|---|
| 18973376 | DATA SELECTION TECHNIQUE FOR MACHINE LEARNING MODEL TRAINING | ELLIS, MATTHEW J | 2153 | §103 | Final Rejection | — | Pending | Dec 09, 2024 |
| 18973356 | LABEL MODIFYING TECHNIQUE | CORRIELUS, JEAN M | 2159 | §103 | Non-Final OA | — | Pending | Dec 09, 2024 |
| 18611085 | MODIFYING SOFTWARE CODE | KAMRUZZAMAN, MD | 2191 | §101§103 | Non-Final OA | — | Pending | Mar 20, 2024 |
| 18605017 | MACHINE LEARNING MODELS THAT GENERATE DIVERSE EMBEDDED VECTORS, ACCORDING TO AN IMPLEMENTATION | ROSTAMI, MOHAMMAD S | — | §101§103 | Non-Final OA | — | Pending | Mar 14, 2024 |
| 18589738 | DETERMINING NATURAL LANGUAGE DESCRIPTION OF A SOFTWARE CODE | VO, TED T | 2191 | §103Other | Non-Final OA | — | Pending | Feb 28, 2024 |
| 18589818 | DETERMINING SOURCE CODE OF A SOFTWARE CODE | DUAN, VIVIAN WEIJIA | 2191 | §101§103§112 | Final Rejection | — | Pending | Feb 28, 2024 |
| 18482258 | DETERMINING SIMILARITY SAMPLES USING A MACHINE LEARNING OPERATION WITH CLUSTERING | LEE, TSU-CHANG | 2128 | §101§103 | Non-Final OA | — | Pending | Oct 06, 2023 |
| 18482251 | DETERMINING SIMILARITY SAMPLES USING A MACHINE LEARNING OPERATION | LI, LIANG Y | 2143 | §101§102§103 | Non-Final OA | — | Pending | Oct 06, 2023 |
| 18179248 | CLUSTERING ANALYSIS FOR DEDUPLICATION OF TRAINING SET SAMPLES FOR MACHINE LEARNING BASED COMPUTER THREAT ANALYSIS | SITIRICHE, LUIS A | — | §103Other | Non-Final OA | — | Pending | Mar 06, 2023 |
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