Prosecution Insights
Prosecution Insights
Technology area: Computing & Software
3 pending office actions • 2 art units • 3 examiners • 0 of 3 (0%) have an AI response strategy ready • 1 patents granted in the last 365 days
Aondevices Inc. holds a company patent portfolio in the Computing & Software technology area. The portfolio currently features 3 pending office actions. These actions are distributed across 3 distinct examiners and 2 distinct art units. The busiest examiner for the company is SCHALLHORN, TYLER J, who is responsible for 1 pending office action. This means that each of the 3 distinct examiners is handling a portion of the pending matters.
The distribution across 2 distinct art units indicates that the company's computing innovations are being reviewed by more than one technical group. Because there are 3 pending office actions and 3 distinct examiners, the prosecution outcomes are subject to the interpretations of multiple reviewers. For a practitioner, this spread suggests that the company's current patenting efforts are not centralized under a single individual. The presence of SCHALLHORN, TYLER J as the busiest examiner with 1 pending office action shows how the current legal matters are divided. This data reflects the current state of the company's interactions with the patent office across 2 distinct art units.
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 + other | 1 (33%) |
| §103 only | 1 (33%) |
| Multi-statute (no §101) | 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 |
|---|---|---|---|
| SCHALLHORN, TYLER J | 1 | 35.6% | +14.8% |
| HWANG, MEGAN ELIZABETH | 1 | 54.5% | +57.5% |
| NGUYEN, MAIKHANH | 1 | 87.2% | +29.1% |
Multi-statute / §101-driven matters, or cases in front of an examiner with an allow rate under 30%. The top 2 ordered by deadline are shown.
| App # | Title | Examiner | Due in |
|---|---|---|---|
| 17450398 | ADAPTIVE TUNING PARAMETERS FOR A CLASSIFICATION NEURAL NETWORK | NGUYEN, MAIKHANH | 62d |
| 17703969 | END-TO-END ADAPTIVE DEEP LEARNING TRAINING AND INFERENCE METHOD AND TOOL CHAIN TO IMPROVE PERFORMANCE AND SHORTEN DEVELOPMENT CYCLES | HWANG, MEGAN ELIZABETH | — |
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 |
|---|---|---|---|
| 17450398 | ADAPTIVE TUNING PARAMETERS FOR A CLASSIFICATION NEURAL NETWORK | NGUYEN, MAIKHANH | 62d |
| 18477763 | RECOGNITION OF USER-DEFINED PATTERNS AT EDGE DEVICES WITH A HYBRID REMOTE-LOCAL PROCESSING | SCHALLHORN, TYLER J | — |
| 17703969 | END-TO-END ADAPTIVE DEEP LEARNING TRAINING AND INFERENCE METHOD AND TOOL CHAIN TO IMPROVE PERFORMANCE AND SHORTEN DEVELOPMENT CYCLES | HWANG, MEGAN ELIZABETH | — |
| Art Unit | Apps |
|---|---|
| 2144 | 2 |
| 2143 | 1 |
| App # | Title | Examiner | Art Unit | Statutes | Status | Due in | AI | Filed |
|---|---|---|---|---|---|---|---|---|
| 18477763 | RECOGNITION OF USER-DEFINED PATTERNS AT EDGE DEVICES WITH A HYBRID REMOTE-LOCAL PROCESSING | SCHALLHORN, TYLER J | 2144 | §103 | Non-Final OA | — | Pending | Sep 29, 2023 |
| 17703969 | END-TO-END ADAPTIVE DEEP LEARNING TRAINING AND INFERENCE METHOD AND TOOL CHAIN TO IMPROVE PERFORMANCE AND SHORTEN DEVELOPMENT CYCLES | HWANG, MEGAN ELIZABETH | 2143 | §103§112 | Non-Final OA | — | Pending | Mar 24, 2022 |
| 17450398 | ADAPTIVE TUNING PARAMETERS FOR A CLASSIFICATION NEURAL NETWORK | NGUYEN, MAIKHANH | 2144 | §101§102§103 | Final Rejection | 62d | Pending | Oct 08, 2021 |
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