Prosecution Insights
Prosecution Insights
Technology area: Computing & Software
5 pending office actions • 3 art units • 4 examiners • 0 of 5 (0%) have an AI response strategy ready • 5 patents granted in the last 365 days
Polyn Technology Limited currently has 5 pending office actions within the Computing & Software technology area. These matters are being reviewed by 4 distinct examiners, which suggests some overlap in examiner assignments across the portfolio. The 5 pending actions are spread across 3 distinct art units, requiring the company to manage various technical standards within the computing sector. This distribution of cases across 3 distinct art units indicates a diverse set of software-related innovations currently under review.
TRAN, TAN H is the busiest examiner for the portfolio, overseeing 2 pending office actions. This examiner manages the largest share for any single examiner in this portfolio, making their specific examination style a critical factor for the company's success. The remaining cases are distributed among the remaining distinct examiners across the 3 distinct art units, necessitating a multi-faceted approach to prosecution. Practitioners should prioritize the relationship with the busiest examiner while remaining adaptable to the different requirements of the 4 distinct examiners involved.
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 + other | 2 (40%) |
| §103 only | 2 (40%) |
| Multi-statute (no §101) | 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 |
|---|---|---|---|
| TRAN, TAN H | 2 | 60.9% | +32.6% |
| TSAI, JAMES T | 1 | 63.4% | +56.9% |
| HOANG, AMY P | 1 | 72.0% | +65.3% |
| PHAKOUSONH, DARAVANH | 1 | 25.0% | +100.0% |
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 |
|---|---|---|---|
| 17744565 | Systems and Methods for Human Activity Recognition Using Analog Neuromorphic Computing Hardware | TRAN, TAN H | 13d |
| 18401256 | Layer-Based Analog Hardware Realization of Neural Networks | TSAI, JAMES T | — |
| 18467644 | Analog Hardware Realization of Neural Networks | HOANG, AMY P | — |
| 18135718 | Hardware Realization of Neural Networks Using Buffers | PHAKOUSONH, DARAVANH | — |
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 |
|---|---|---|---|
| 18196412 | Hybrid Fixed/Flexible Neural Network Architecture | TRAN, TAN H | 15d overdue |
| 17744565 | Systems and Methods for Human Activity Recognition Using Analog Neuromorphic Computing Hardware | TRAN, TAN H | 13d |
| 18401256 | Layer-Based Analog Hardware Realization of Neural Networks | TSAI, JAMES T | — |
| 18467644 | Analog Hardware Realization of Neural Networks | HOANG, AMY P | — |
| 18135718 | Hardware Realization of Neural Networks Using Buffers | PHAKOUSONH, DARAVANH | — |
| Art Unit | Apps |
|---|---|
| 2141 | 2 |
| 2143 | 1 |
| 2121 | 1 |
| App # | Title | Examiner | Art Unit | Statutes | Status | Due in | AI | Filed |
|---|---|---|---|---|---|---|---|---|
| 18401256 | Layer-Based Analog Hardware Realization of Neural Networks | TSAI, JAMES T | — | §101§103 | Non-Final OA | — | Pending | Dec 29, 2023 |
| 18467644 | Analog Hardware Realization of Neural Networks | HOANG, AMY P | 2143 | §101§102§103 | Non-Final OA | — | Pending | Sep 14, 2023 |
| 18196412 | Hybrid Fixed/Flexible Neural Network Architecture | TRAN, TAN H | 2141 | §103 | Final Rejection | 15d overdue | Pending | May 11, 2023 |
| 18135718 | Hardware Realization of Neural Networks Using Buffers | PHAKOUSONH, DARAVANH | 2121 | §103 | Final Rejection | — | Pending | Apr 17, 2023 |
| 17744565 | Systems and Methods for Human Activity Recognition Using Analog Neuromorphic Computing Hardware | TRAN, TAN H | 2141 | §103§112 | Final Rejection | 13d | Pending | May 13, 2022 |
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