Prosecution Insights
Prosecution Insights
4 pending office actions • 2 art units • 4 examiners • 0 of 4 (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 | 3 (75%) |
| §103 only | 1 (25%) |
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 |
|---|---|---|---|
| MEYER, JACQUELINE CHRISTINE | 1 | 65.2% | +61.7% |
| ROHD, BENJAMIN MATTHEW | 1 | 25.0% | +100.0% |
| CAMPOS, ALFREDO | 1 | 76.9% | -11.9% |
| MENGISTU, TEWODROS E | 1 | 50.0% | +30.0% |
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 |
|---|---|---|---|
| 18524550 | METHOD AND DEVICES OF AN EFFICIENT GAUSSIAN MIXTURE MODEL (GMM) DISTRIBUTION BASED APPROXIMATION OF A DATA SET IN A COMPUTING ENVIRONMENT | ROHD, BENJAMIN MATTHEW | — |
| 18220215 | AUTOMATIC DETERMINATION OF DATA SAMPLES IN NEED OF HUMAN ANNOTATION FOR A MACHINE LEARNING MODEL IMPROVEMENT | CAMPOS, ALFREDO | — |
| 18217520 | AUTOMATIC GENERATION OF ATTRIBUTES BASED ON SEMANTIC CATEGORIZATION OF LARGE DATASETS IN ARTIFICIAL INTELLIGENCE MODELS AND APPLICATIONS | MENGISTU, TEWODROS E | — |
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 |
|---|---|---|---|
| 18544405 | METHOD AND DEVICES OF AN EFFICIENT GAUSSIAN MIXTURE MODEL (GMM) DISTRIBUTION BASED APPROXIMATION OF A COLLECTION OF MULTI-DIMENSIONAL NUMERIC ARRAYS IN A COMPUTING ENVIRONMENT | MEYER, JACQUELINE CHRISTINE | — |
| 18524550 | METHOD AND DEVICES OF AN EFFICIENT GAUSSIAN MIXTURE MODEL (GMM) DISTRIBUTION BASED APPROXIMATION OF A DATA SET IN A COMPUTING ENVIRONMENT | ROHD, BENJAMIN MATTHEW | — |
| 18217520 | AUTOMATIC GENERATION OF ATTRIBUTES BASED ON SEMANTIC CATEGORIZATION OF LARGE DATASETS IN ARTIFICIAL INTELLIGENCE MODELS AND APPLICATIONS | MENGISTU, TEWODROS E | — |
| Art Unit | Apps |
|---|---|
| 2129 | 1 |
| 2127 | 1 |
| App # | Title | Examiner | Art Unit | Statutes | Status | Due in | AI | Filed |
|---|---|---|---|---|---|---|---|---|
| 18544405 | METHOD AND DEVICES OF AN EFFICIENT GAUSSIAN MIXTURE MODEL (GMM) DISTRIBUTION BASED APPROXIMATION OF A COLLECTION OF MULTI-DIMENSIONAL NUMERIC ARRAYS IN A COMPUTING ENVIRONMENT | MEYER, JACQUELINE CHRISTINE | — | §103 | Non-Final OA | — | Pending | Dec 18, 2023 |
| 18524550 | METHOD AND DEVICES OF AN EFFICIENT GAUSSIAN MIXTURE MODEL (GMM) DISTRIBUTION BASED APPROXIMATION OF A DATA SET IN A COMPUTING ENVIRONMENT | ROHD, BENJAMIN MATTHEW | — | §101§102§103§112 | Non-Final OA | — | Pending | Nov 30, 2023 |
| 18220215 | AUTOMATIC DETERMINATION OF DATA SAMPLES IN NEED OF HUMAN ANNOTATION FOR A MACHINE LEARNING MODEL IMPROVEMENT | CAMPOS, ALFREDO | 2129 | §101§103 | Final Rejection | — | Pending | Jul 10, 2023 |
| 18217520 | AUTOMATIC GENERATION OF ATTRIBUTES BASED ON SEMANTIC CATEGORIZATION OF LARGE DATASETS IN ARTIFICIAL INTELLIGENCE MODELS AND APPLICATIONS | MENGISTU, TEWODROS E | 2127 | §101§102§103§112 | Non-Final OA | — | Pending | Jun 30, 2023 |
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