Prosecution Insights
Prosecution Insights
Technology areas: Computing & Software • Communications
8 pending office actions • 4 art units • 8 examiners • 0 of 8 (0%) have an AI response strategy ready • 2 patents granted in the last 365 days
VIANAI SYSTEMS, INC. maintains a patent portfolio in the Computing & Software technology area with 8 pending office actions. These actions are distributed among 8 distinct examiners, showing a broad engagement across the patent office. The portfolio spans 4 distinct art units, which suggests the company's software innovations cover multiple technical sub-domains within the computing field. This distribution requires a coordinated prosecution strategy to ensure consistency across different filings.
The busiest examiner for the company is WONG, LUT, who is responsible for 1 busiest examiner pending office action. Given the total volume of 8 pending office actions, the workload is distributed across many individuals. This variety of 8 distinct examiners and 4 distinct art units indicates that the company is receiving diverse perspectives from the patent office. For the practitioner, this necessitates a flexible approach to prosecution to navigate the specific tendencies of multiple examiners simultaneously.
Based on the USPTO statutory response window for each pending office action. 4 of the docket's apps have a known mailing date; the rest are excluded from the tile counts.
Every pending office action with a known statutory deadline, placed on a days-until-due axis. Dots left of Today are overdue; the further right, the more runway. Cases that share a deadline window stack vertically. 4 of the docket's apps have a known mailing date.
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 | 5 (62%) |
| §103 only | 2 (25%) |
| §102 only | 1 (12%) |
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 |
|---|---|---|---|
| WONG, LUT | 1 | 77.3% | +14.1% |
| WU, BENJAMIN C | 1 | 87.4% | +16.4% |
| SHARPLESS, SAMUEL | 1 | 81.4% | +26.9% |
| BYCER, ERIC J | 1 | 66.9% | +42.4% |
| WU, NICHOLAS S | 1 | 52.4% | +31.4% |
| RAHMAN, IBRAHIM | 1 | 11.1% | +5.2% |
| GEBRESLASSIE, WINTA | 1 | 77.1% | +23.6% |
| XIA, XUYANG | 1 | 72.5% | +52.3% |
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 |
|---|---|---|---|
| 18621619 | TECHNIQUES FOR GENERATING AND CORRECTING DATABASE QUERIES USING LANGUAGE MODELS | SHARPLESS, SAMUEL | 39d |
| 18634580 | TECHNIQUES FOR MIGRATING CLUSTER DATA | WU, BENJAMIN C | — |
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 |
|---|---|---|---|
| 17960049 | POSITIVITY VALIDATION AND EXPLAINABILITY FOR CAUSAL INFERENCE VIA ASYMMETRICALLY PRUNED DECISION TREES | RAHMAN, IBRAHIM | 89d overdue |
| 17517505 | DYNAMIC VARIABLE QUANTIZATION OF MACHINE LEARNING PARAMETERS | XIA, XUYANG | 70d |
| 18651433 | TECHNIQUES FOR MONITORING MACHINE LEARNING MODEL PERFORMANCE | WONG, LUT | — |
| 18621593 | TECHNIQUES FOR GENERATING AND CORRECTING LANGUAGE MODEL OUTPUTS | BYCER, ERIC J | — |
| 18529635 | TECHNIQUES FOR ACCELERATING MACHINE LEARNING MODELS | WU, NICHOLAS S | — |
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 7 ordered by deadline are shown.
| App # | Title | Examiner | Due in |
|---|---|---|---|
| 17831177 | PERFORMING INFERENCE USING SIMPLIFIED REPRESENTATIONS OF CONVOLUTIONAL NEURAL NETWORKS | GEBRESLASSIE, WINTA | 9d |
| 18621619 | TECHNIQUES FOR GENERATING AND CORRECTING DATABASE QUERIES USING LANGUAGE MODELS | SHARPLESS, SAMUEL | 39d |
| 17517505 | DYNAMIC VARIABLE QUANTIZATION OF MACHINE LEARNING PARAMETERS | XIA, XUYANG | 70d |
| 18651433 | TECHNIQUES FOR MONITORING MACHINE LEARNING MODEL PERFORMANCE | WONG, LUT | — |
| 18634580 | TECHNIQUES FOR MIGRATING CLUSTER DATA | WU, BENJAMIN C | — |
| 18621593 | TECHNIQUES FOR GENERATING AND CORRECTING LANGUAGE MODEL OUTPUTS | BYCER, ERIC J | — |
| 18529635 | TECHNIQUES FOR ACCELERATING MACHINE LEARNING MODELS | WU, NICHOLAS S | — |
| Art Unit | Apps |
|---|---|
| 2165 | 1 |
| 2122 | 1 |
| 2677 | 1 |
| 2143 | 1 |
| App # | Title | Examiner | Art Unit | Statutes | Status | Due in | AI | Filed |
|---|---|---|---|---|---|---|---|---|
| 18651433 | TECHNIQUES FOR MONITORING MACHINE LEARNING MODEL PERFORMANCE | WONG, LUT | — | §101§102§103 | Non-Final OA | — | Pending | Apr 30, 2024 |
| 18634580 | TECHNIQUES FOR MIGRATING CLUSTER DATA | WU, BENJAMIN C | — | §103 | Non-Final OA | — | Pending | Apr 12, 2024 |
| 18621619 | TECHNIQUES FOR GENERATING AND CORRECTING DATABASE QUERIES USING LANGUAGE MODELS | SHARPLESS, SAMUEL | 2165 | §102 | Final Rejection | 39d | Pending | Mar 29, 2024 |
| 18621593 | TECHNIQUES FOR GENERATING AND CORRECTING LANGUAGE MODEL OUTPUTS | BYCER, ERIC J | — | §101§103 | Non-Final OA | — | Pending | Mar 29, 2024 |
| 18529635 | TECHNIQUES FOR ACCELERATING MACHINE LEARNING MODELS | WU, NICHOLAS S | — | §101§102§103 | Non-Final OA | — | Pending | Dec 05, 2023 |
| 17960049 | POSITIVITY VALIDATION AND EXPLAINABILITY FOR CAUSAL INFERENCE VIA ASYMMETRICALLY PRUNED DECISION TREES | RAHMAN, IBRAHIM | 2122 | §101§102§103 | Final Rejection | 89d overdue | Pending | Oct 04, 2022 |
| 17831177 | PERFORMING INFERENCE USING SIMPLIFIED REPRESENTATIONS OF CONVOLUTIONAL NEURAL NETWORKS | GEBRESLASSIE, WINTA | 2677 | §103 | Non-Final OA | 9d | Pending | Jun 02, 2022 |
| 17517505 | DYNAMIC VARIABLE QUANTIZATION OF MACHINE LEARNING PARAMETERS | XIA, XUYANG | 2143 | §101§103§112 | Non-Final OA | 70d | Pending | Nov 02, 2021 |
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