Prosecution Insights
Prosecution Insights
Technology areas: Computing & Software • Semiconductors, Circuits & Optics • Transportation, E-Commerce & Mechanical Systems
5 pending office actions • 4 art units • 5 examiners • 0 of 5 (0%) have an AI response strategy ready
Camelot UK Bidco Limited is currently managing 5 pending office actions within the Transportation, E-Commerce & Mechanical Systems technology area. These 5 pending office actions indicate the level of ongoing activity in the company's patent portfolio. The prosecution work is distributed across 5 distinct examiners, which means the company interacts with 5 different officials. With 5 distinct examiners involved, the portfolio is subject to a variety of individual scrutiny.
The company's applications are situated within 4 distinct art units. This distribution across 4 distinct art units reflects the technical scope for the pending matters within the transportation and mechanical systems sectors. BHAT, VIBHA NARAYAN is identified as the busiest examiner for this portfolio, currently overseeing 1 pending office action. Because BHAT, VIBHA NARAYAN is responsible for the 1 pending office action, the workload is spread across the various examiners. This distribution means that the busiest examiner manages 1 of the active patent applications, preventing a high concentration of matters under a single official.
Based on the USPTO statutory response window for each pending office action. 1 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 | 2 (40%) |
| §101 + other | 2 (40%) |
| §103 only | 1 (20%) |
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 |
|---|---|---|---|
| BHAT, VIBHA NARAYAN | 1 | — | — |
| HENSON, MISCHITA L | 1 | 75.9% | +15.0% |
| SINGLETARY, TYRONE E | 1 | 30.4% | +28.2% |
| ALLEN, NICHOLAS E | 1 | 75.6% | +14.6% |
| CHEN, WENREN | 1 | 15.2% | +28.7% |
Multi-statute / §101-driven matters, or cases in front of an examiner with an allow rate under 30%. The top 4 ordered by deadline are shown.
| App # | Title | Examiner | Due in |
|---|---|---|---|
| 18047047 | METHODS AND SYSTEMS FOR RANKING TRADEMARK SEARCH RESULTS | ALLEN, NICHOLAS E | 27d |
| 18590188 | HYBRID ARTIFICIAL INTELLIGENCE CLASSIFIER | BHAT, VIBHA NARAYAN | — |
| 18421694 | SEARCHING A CHEMICAL STRUCTURE DATABASE BASED ON CENTROIDS | HENSON, MISCHITA L | — |
| 18046394 | MACHINE-LEARNING BASED TECHNIQUES FOR PREDICTING TRADEMARK SIMILARITY | CHEN, WENREN | — |
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 4 ordered by deadline are shown.
| App # | Title | Examiner | Due in |
|---|---|---|---|
| 18047047 | METHODS AND SYSTEMS FOR RANKING TRADEMARK SEARCH RESULTS | ALLEN, NICHOLAS E | 27d |
| 18421694 | SEARCHING A CHEMICAL STRUCTURE DATABASE BASED ON CENTROIDS | HENSON, MISCHITA L | — |
| 18068825 | SYSTEMS AND METHODS FOR DETERMINING ENTITIES BASED ON TRADEMARK PORTFOLIO SIMILARITY | SINGLETARY, TYRONE E | — |
| 18046394 | MACHINE-LEARNING BASED TECHNIQUES FOR PREDICTING TRADEMARK SIMILARITY | CHEN, WENREN | — |
| Art Unit | Apps |
|---|---|
| 2857 | 1 |
| 3625 | 1 |
| 2154 | 1 |
| 3626 | 1 |
| App # | Title | Examiner | Art Unit | Statutes | Status | Due in | AI | Filed |
|---|---|---|---|---|---|---|---|---|
| 18590188 | HYBRID ARTIFICIAL INTELLIGENCE CLASSIFIER | BHAT, VIBHA NARAYAN | — | §101§103§112 | Non-Final OA | — | Pending | Feb 28, 2024 |
| 18421694 | SEARCHING A CHEMICAL STRUCTURE DATABASE BASED ON CENTROIDS | HENSON, MISCHITA L | 2857 | §101 | Non-Final OA | — | Pending | Jan 24, 2024 |
| 18068825 | SYSTEMS AND METHODS FOR DETERMINING ENTITIES BASED ON TRADEMARK PORTFOLIO SIMILARITY | SINGLETARY, TYRONE E | 3625 | §103 | Non-Final OA | — | Pending | Dec 20, 2022 |
| 18047047 | METHODS AND SYSTEMS FOR RANKING TRADEMARK SEARCH RESULTS | ALLEN, NICHOLAS E | 2154 | §101§103 | Final Rejection | 27d | Pending | Oct 17, 2022 |
| 18046394 | MACHINE-LEARNING BASED TECHNIQUES FOR PREDICTING TRADEMARK SIMILARITY | CHEN, WENREN | 3626 | §101 | Final Rejection | — | Pending | Oct 13, 2022 |
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