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 • 3 patents granted in the last 365 days
Cylance Inc. currently has 3 pending office actions in the Computing & Software technology area. These actions are being handled by 3 distinct examiners, which indicates that each pending application is receiving an independent review. This variety in examiners can lead to different prosecution experiences even within the same technology sector.
The 3 pending office actions are situated within 2 distinct art units. This suggests that while the examiners are all different, there is some overlap in the technical groupings where the applications are being processed. Managing 3 distinct examiners across 2 distinct art units requires a strategy that accounts for both the individual preferences of the examiners and the broader trends within the specific art units.
ROSTAMI, MOHAMMAD S is the busiest examiner for this portfolio, with 1 busiest examiner pending action. Since there are 3 pending office actions in total, the workload is spread thin, with no single examiner managing more than one of the active cases. This distribution provides a diversified approach to securing intellectual property in the Computing & Software field.
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 (100%) |
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 |
|---|---|---|---|
| ROSTAMI, MOHAMMAD S | 1 | 67.0% | +25.9% |
| DUAN, VIVIAN WEIJIA | 1 | 72.7% | +16.1% |
| LI, LIANG Y | 1 | 61.8% | +69.3% |
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 |
|---|---|---|---|
| 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 | — |
| 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 3 ordered by deadline are shown.
| App # | Title | Examiner | Due in |
|---|---|---|---|
| 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 | — |
| 18482251 | DETERMINING SIMILARITY SAMPLES USING A MACHINE LEARNING OPERATION | LI, LIANG Y | — |
| Art Unit | Apps |
|---|---|
| 2191 | 1 |
| 2143 | 1 |
| App # | Title | Examiner | Art Unit | Statutes | Status | Due in | AI | Filed |
|---|---|---|---|---|---|---|---|---|
| 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 |
| 18589818 | DETERMINING SOURCE CODE OF A SOFTWARE CODE | DUAN, VIVIAN WEIJIA | 2191 | §101§103§112 | Non-Final OA | — | Pending | Feb 28, 2024 |
| 18482251 | DETERMINING SIMILARITY SAMPLES USING A MACHINE LEARNING OPERATION | LI, LIANG Y | 2143 | §101§102§103 | Non-Final OA | — | Pending | Oct 06, 2023 |
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