Prosecution Insights
Prosecution Insights
6 pending office actions • 5 art units • 5 examiners • 0 of 6 (0%) have an AI response strategy ready • 4 patents granted in the last 365 days
Based on the USPTO statutory response window for each pending office action. 6 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. 6 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 |
|---|---|
| §103 only | 5 (83%) |
| §102 only | 1 (17%) |
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 |
|---|---|---|---|
| XIA, XUYANG | 2 | 71.5% | +53.4% |
| SHARPLESS, SAMUEL | 1 | 79.5% | +29.7% |
| RAHMAN, IBRAHIM | 1 | 0.0% | +0.0% |
| YUN, CARINA | 1 | 49.2% | +33.7% |
| GEBRESLASSIE, WINTA | 1 | 75.6% | +25.0% |
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 |
|---|---|---|---|
| 17960049 | POSITIVITY VALIDATION AND EXPLAINABILITY FOR CAUSAL INFERENCE VIA ASYMMETRICALLY PRUNED DECISION TREES | RAHMAN, IBRAHIM | 89d 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 5 ordered by deadline are shown.
| App # | Title | Examiner | Due in |
|---|---|---|---|
| 17517502 | DYNAMIC VARIABLE QUANTIZATION OF MACHINE LEARNING INPUTS | XIA, XUYANG | 110d overdue |
| 17867540 | TECHNIQUES FOR IMPLEMENTING CONTAINER-BASED SOFTWARE SERVICES | YUN, CARINA | 53d overdue |
| 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 |
| Art Unit | Apps |
|---|---|
| 2143 | 2 |
| 2165 | 1 |
| 2122 | 1 |
| 2194 | 1 |
| 2677 | 1 |
| App # | Title | Examiner | Art Unit | Statutes | Status | Due in | AI | Filed |
|---|---|---|---|---|---|---|---|---|
| 18621619 | TECHNIQUES FOR GENERATING AND CORRECTING DATABASE QUERIES USING LANGUAGE MODELS | SHARPLESS, SAMUEL | 2165 | §102 | Final Rejection | 39d | Pending | Mar 29, 2024 |
| 17960049 | POSITIVITY VALIDATION AND EXPLAINABILITY FOR CAUSAL INFERENCE VIA ASYMMETRICALLY PRUNED DECISION TREES | RAHMAN, IBRAHIM | 2122 | §103 | Non-Final OA | 89d overdue | Pending | Oct 04, 2022 |
| 17867540 | TECHNIQUES FOR IMPLEMENTING CONTAINER-BASED SOFTWARE SERVICES | YUN, CARINA | 2194 | §103 | Non-Final OA | 53d overdue | Pending | Jul 18, 2022 |
| 17831177 | PERFORMING INFERENCE USING SIMPLIFIED REPRESENTATIONS OF CONVOLUTIONAL NEURAL NETWORKS | GEBRESLASSIE, WINTA | 2677 | §103 | Non-Final OA | 9d | Pending | Jun 02, 2022 |
| 17517502 | DYNAMIC VARIABLE QUANTIZATION OF MACHINE LEARNING INPUTS | XIA, XUYANG | 2143 | §103 | Non-Final OA | 110d overdue | Pending | Nov 02, 2021 |
| 17517505 | DYNAMIC VARIABLE QUANTIZATION OF MACHINE LEARNING PARAMETERS | XIA, XUYANG | 2143 | §103 | Non-Final OA | 70d | Pending | Nov 02, 2021 |
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