Prosecution Insights
Prosecution Insights
6 pending office actions • 2 clients • 6 examiners • 6 art units • 0 of 6 (0%) have an AI response strategy ready • 12 patents granted in the last 365 days
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 only | 1 (17%) |
| §103 only | 5 (83%) |
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 |
|---|---|---|---|
| NGUYEN, THU N | 1 | 71.7% | +26.1% |
| MILIA, MARK R | 1 | 58.6% | +23.0% |
| KASSIM, IMAD MUTEE | 1 | 73.5% | +32.3% |
| CHUANG, SU-TING | 1 | 50.5% | +38.9% |
| RAHMAN, IBRAHIM | 1 | 6.3% | -9.1% |
| GIROUX, GEORGE | 1 | 65.5% | +26.7% |
Multi-statute / §101-driven matters, or cases in front of an examiner with an allow rate under 30%. The top 1 ordered by deadline are shown.
| App # | Title | Examiner | Due in |
|---|---|---|---|
| 18009980 | TRAINING OF DEPLOYED NEURAL NETWORKS | RAHMAN, IBRAHIM | — |
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 5 ordered by deadline are shown.
| App # | Title | Examiner | Due in |
|---|---|---|---|
| 17947963 | COOPERATIVE LEARNING OF LANGEVIN FLOW AND NORMALIZING FLOW TOWARD ENERGY-BASED MODEL | GIROUX, GEORGE | 60d overdue |
| 18327707 | GENERALIZED EVOLUTIONARY TRAINING FRAMEWORKS FOR DEEP NEURAL NETWORKS | CHUANG, SU-TING | 28d |
| 18933349 | Result generation method, generation model training method, electronic device, and storage medium | NGUYEN, THU N | — |
| 18391354 | LIGHT DETECTION AND CLASSIFICATION FOR AN AUTONOMOUS DRIVING SYSTEM | MILIA, MARK R | — |
| 18556619 | MULTIPLE-MODEL HETEROGENEOUS COMPUTING | KASSIM, IMAD MUTEE | — |
| Client (Assignee) | Pending OAs |
|---|---|
| Baidu | 4 |
| Apollo Autonomous Driving USA | 2 |
| Art Unit | Apps |
|---|---|
| 2154 | 1 |
| 2681 | 1 |
| 2486 | 1 |
| 2146 | 1 |
| 2122 | 1 |
| 2128 | 1 |
| App # | Title | Client | Examiner | Art Unit | Statutes | Status | Due in | AI | Filed |
|---|---|---|---|---|---|---|---|---|---|
| 18933349 | Result generation method, generation model training method, electronic device, and storage medium | Baidu.Com Times Technology (Beijing) Co., Ltd. | NGUYEN, THU N | 2154 | §103 | Final Rejection | — | Pending | Oct 31, 2024 |
| 18391354 | LIGHT DETECTION AND CLASSIFICATION FOR AN AUTONOMOUS DRIVING SYSTEM | Apollo Autonomous Driving USA LLC | MILIA, MARK R | 2681 | §103 | Final Rejection | — | Pending | Dec 20, 2023 |
| 18556619 | MULTIPLE-MODEL HETEROGENEOUS COMPUTING | Baidu.Com Times Technology (Beijing) Co., Ltd. | KASSIM, IMAD MUTEE | 2486 | §103 | Non-Final OA | — | Pending | Oct 20, 2023 |
| 18327707 | GENERALIZED EVOLUTIONARY TRAINING FRAMEWORKS FOR DEEP NEURAL NETWORKS | Apollo Autonomous Driving USA LLC | CHUANG, SU-TING | 2146 | §103 | Non-Final OA | 28d | Pending | Jun 01, 2023 |
| 18009980 | TRAINING OF DEPLOYED NEURAL NETWORKS | Baidu.Com Times Technology (Beijing) Co., Ltd. | RAHMAN, IBRAHIM | 2122 | §101 | Final Rejection | — | Pending | Dec 12, 2022 |
| 17947963 | COOPERATIVE LEARNING OF LANGEVIN FLOW AND NORMALIZING FLOW TOWARD ENERGY-BASED MODEL | Baidu Usa, LLC | GIROUX, GEORGE | 2128 | §103 | Non-Final OA | 60d overdue | Pending | Sep 19, 2022 |
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