Prosecution Insights
Prosecution Insights
Technology areas: Computing & Software • Communications • Medical Devices & Mechanical Engineering
3 pending office actions • 3 art units • 3 examiners • 0 of 3 (0%) have an AI response strategy ready
Bryte Labs Inc. currently has 3 pending office actions within the Communications technology area. These active matters are distributed across 3 distinct examiners and 3 distinct art units, showing a broad spread of review across the patent office. This distribution suggests that the company's intellectual property covers a variety of sub-specialties within the Communications field.
The busiest examiner for the company is AFRIFA-KYEI, ANTHONY D, who is managing 1 pending office action. Because the busiest examiner pending count is 1 and there are 3 pending office actions in total, the workload is perfectly balanced across the 3 distinct examiners. Each examiner is responsible for 1 case, which may result in different prosecution timelines for each application.
For a patent practitioner, this data indicates that Bryte Labs Inc. must manage 3 separate examiner relationships simultaneously. Since the 3 pending office actions are located in 3 distinct art units, there is no central point of contact for these cases. This requires a strategy that accounts for the specific nuances of 3 different examiners and their respective art units within the Communications sector.
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 | 1 (33%) |
| §102 only | 1 (33%) |
| §112 only | 1 (33%) |
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 |
|---|---|---|---|
| AFRIFA-KYEI, ANTHONY D | 1 | 65.2% | +13.7% |
| SURYAWANSHI, SURESH | 1 | 88.4% | +12.6% |
| LANNU, JOSHUA DARYL DEANON | 1 | 82.1% | +24.2% |
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 |
|---|---|---|---|
| 18750768 | Adaptive Sleep System Using Data Analytics and Learning Techniques to Improve Individual Sleep Conditions | SURYAWANSHI, SURESH | — |
| 17805429 | SLEEP PHASE DEPENDENT PRESSURE CONTROL AND LEARNING METHODS TO OPTIMIZE SLEEP QUALITY | LANNU, JOSHUA DARYL DEANON | — |
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 |
|---|---|---|---|
| 18814385 | Sleep Phase Dependent Temperature Control and Learning Methods to Optimize Sleep Quality | AFRIFA-KYEI, ANTHONY D | 80d overdue |
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 |
|---|---|---|---|
| 18814385 | Sleep Phase Dependent Temperature Control and Learning Methods to Optimize Sleep Quality | AFRIFA-KYEI, ANTHONY D | 80d overdue |
| 18750768 | Adaptive Sleep System Using Data Analytics and Learning Techniques to Improve Individual Sleep Conditions | SURYAWANSHI, SURESH | — |
| 17805429 | SLEEP PHASE DEPENDENT PRESSURE CONTROL AND LEARNING METHODS TO OPTIMIZE SLEEP QUALITY | LANNU, JOSHUA DARYL DEANON | — |
| Art Unit | Apps |
|---|---|
| 2686 | 1 |
| 2116 | 1 |
| 3791 | 1 |
| App # | Title | Examiner | Art Unit | Statutes | Status | Due in | AI | Filed |
|---|---|---|---|---|---|---|---|---|
| 18814385 | Sleep Phase Dependent Temperature Control and Learning Methods to Optimize Sleep Quality | AFRIFA-KYEI, ANTHONY D | 2686 | §101 | Final Rejection | 80d overdue | Pending | Aug 23, 2024 |
| 18750768 | Adaptive Sleep System Using Data Analytics and Learning Techniques to Improve Individual Sleep Conditions | SURYAWANSHI, SURESH | 2116 | §102Other | Non-Final OA | — | Pending | Jun 21, 2024 |
| 17805429 | SLEEP PHASE DEPENDENT PRESSURE CONTROL AND LEARNING METHODS TO OPTIMIZE SLEEP QUALITY | LANNU, JOSHUA DARYL DEANON | 3791 | §112Other | Non-Final OA | — | Pending | Jun 03, 2022 |
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