Prosecution Insights
Prosecution Insights
Technology area: Computing & Software
6 pending office actions • 1 client • 6 examiners • 6 art units • 0 of 6 (0%) have an AI response strategy ready • 17 patents granted in the last 365 days
The Law Office of Tong Lee operates in Computing & Software. The firm has 146 career applications. Its resolved allowance rate is 88.68 percent. This rate shows the proportion of resolved applications that result in a patent. For a client, this percentage reflects the frequency of successful outcomes in completed cases.
The firm currently has 5 pending office actions. The average number of office actions per allowance is 2.79. This 2.79 office actions per allowance metric describes the typical interaction frequency with the USPTO. The 146 career applications and 5 pending office actions represent the firm's historical and current portfolio. These data points define the firm's prosecution cycle in its field.
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 | 4 (67%) |
| §103 only | 2 (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 |
|---|---|---|---|
| VO, TIM T | 1 | 57.3% | +24.2% |
| MARU, MATIYAS T | 1 | 61.0% | +5.9% |
| HWANG, MEGAN ELIZABETH | 1 | 54.5% | +57.5% |
| JONES, CHARLES JEFFREY | 1 | 26.1% | +36.7% |
| KAPOOR, DEVAN | 1 | 7.1% | +11.1% |
| KWON, JUN | 1 | 41.0% | +47.2% |
Multi-statute / §101-driven matters, or cases in front of an examiner with an allow rate under 30%. The top 5 ordered by deadline are shown.
| App # | Title | Examiner | Due in |
|---|---|---|---|
| 17701745 | PLATFORM-AWARE TRANSFORMER-BASED PERFORMANCE PREDICTION | KAPOOR, DEVAN | 2d overdue |
| 17846007 | Network Space Search for Pareto-Efficient Spaces | JONES, CHARLES JEFFREY | 6d |
| 18042439 | TRAINING A NEURAL NETWORK USING CONTRASTIVE SAMPLES FOR MACRO PLACEMENT | MARU, MATIYAS T | — |
| 18042423 | MACRO PLACEMENT USING AN ARTIFICIAL INTELLIGENCE APPROACH | HWANG, MEGAN ELIZABETH | — |
| 17505422 | NEURAL NETWORK PROCESSING UNIT FOR HYBRID AND MIXED PRECISION COMPUTING | KWON, JUN | — |
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 |
|---|---|---|---|
| 17701745 | PLATFORM-AWARE TRANSFORMER-BASED PERFORMANCE PREDICTION | KAPOOR, DEVAN | 2d overdue |
| 17846007 | Network Space Search for Pareto-Efficient Spaces | JONES, CHARLES JEFFREY | 6d |
| 19218112 | Compute-Near Memory on a Base Die with Access to Multi-Stack Memory | VO, TIM T | — |
| 18042423 | MACRO PLACEMENT USING AN ARTIFICIAL INTELLIGENCE APPROACH | HWANG, MEGAN ELIZABETH | — |
| 17505422 | NEURAL NETWORK PROCESSING UNIT FOR HYBRID AND MIXED PRECISION COMPUTING | KWON, JUN | — |
| Client (Assignee) | Pending OAs |
|---|---|
| MediaTek | 6 |
| Art Unit | Apps |
|---|---|
| 2138 | 1 |
| 2148 | 1 |
| 2143 | 1 |
| 2122 | 1 |
| 2126 | 1 |
| 2144 | 1 |
| App # | Title | Client | Examiner | Art Unit | Statutes | Status | Due in | AI | Filed |
|---|---|---|---|---|---|---|---|---|---|
| 19218112 | Compute-Near Memory on a Base Die with Access to Multi-Stack Memory | MediaTek Inc. | VO, TIM T | 2138 | §103 | Non-Final OA | — | Pending | May 23, 2025 |
| 18042439 | TRAINING A NEURAL NETWORK USING CONTRASTIVE SAMPLES FOR MACRO PLACEMENT | MediaTek Inc. | MARU, MATIYAS T | 2148 | §101§103 | Final Rejection | — | Pending | Feb 21, 2023 |
| 18042423 | MACRO PLACEMENT USING AN ARTIFICIAL INTELLIGENCE APPROACH | MediaTek Inc. | HWANG, MEGAN ELIZABETH | 2143 | §101§103 | Non-Final OA | — | Pending | Feb 21, 2023 |
| 17846007 | Network Space Search for Pareto-Efficient Spaces | MediaTek Inc. | JONES, CHARLES JEFFREY | 2122 | §101§103§112 | Non-Final OA | 6d | Pending | Jun 22, 2022 |
| 17701745 | PLATFORM-AWARE TRANSFORMER-BASED PERFORMANCE PREDICTION | MediaTek Inc. | KAPOOR, DEVAN | 2126 | §103 | Final Rejection | 2d overdue | Pending | Mar 23, 2022 |
| 17505422 | NEURAL NETWORK PROCESSING UNIT FOR HYBRID AND MIXED PRECISION COMPUTING | MediaTek Inc. | KWON, JUN | 2144 | §101§103 | Final Rejection | — | Pending | Oct 19, 2021 |
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