Prosecution Insights
Prosecution Insights
7 pending office actions • 6 art units • 7 examiners • 0 of 7 (0%) have an AI response strategy ready • 3 patents granted in the last 365 days
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 only | 3 (43%) |
| §101 + other | 3 (43%) |
| Multi-statute (no §101) | 1 (14%) |
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 |
|---|---|---|---|
| BOROWSKI, MICHAEL | 1 | 32.0% | +61.5% |
| BECKER, TYLER JUSTIN | 1 | 73.9% | +6.3% |
| WALTON, CHESIREE A | 1 | 30.2% | +29.4% |
| KOLOSOWSKI-GAGER, KATHERINE | 1 | 26.3% | +30.7% |
| KHATTAR, RAJESH | 1 | 36.2% | +34.8% |
| MISIASZEK, AMBER ALTSCHUL | 1 | 47.0% | +24.5% |
| YOUNG, ASHLEY YA-SHEH | 1 | 30.3% | +17.4% |
Multi-statute / §101-driven matters, or cases in front of an examiner with an allow rate under 30%. The top 7 ordered by deadline are shown.
| App # | Title | Examiner | Due in |
|---|---|---|---|
| 18913944 | DISTRIBUTING ELECTRONIC SURVEYS THROUGH A MESSENGER PLATFORM | KOLOSOWSKI-GAGER, KATHERINE | 44d overdue |
| 18929363 | DETERMINING AND APPLYING ATTRIBUTE DEFINITIONS TO DIGITAL SURVEY DATA TO GENERATE SURVEY ANALYSES | WALTON, CHESIREE A | 3d overdue |
| 19059854 | RESPONDING TO QUERIES BY GENERATING A SYNTHETIC AUDIENCE OF RESPONDENTS | BOROWSKI, MICHAEL | — |
| 18962421 | INTELLIGENTLY SUMMARIZING AND PRESENTING TEXTUAL RESPONSES WITH MACHINE LEARNING | BECKER, TYLER JUSTIN | — |
| 18824067 | DETERMINING FRAUDULENT SURVEY RESPONSES TO DIGITAL SURVEYS USING RULE-BASED MODELS AND MACHINE-LEARNING MODELS | KHATTAR, RAJESH | — |
| 18793112 | DETERMINING INTERACTION CONTEXT FOR INTERACTIONS GENERATED FROM USER ENGAGEMENT EVENTS | MISIASZEK, AMBER ALTSCHUL | — |
| 18322180 | INTELLIGENTLY COMBINING RELEVANT DATA ITEMS OF REQUESTED DATA SETS DURING INGESTION | YOUNG, ASHLEY YA-SHEH | — |
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 6 ordered by deadline are shown.
| App # | Title | Examiner | Due in |
|---|---|---|---|
| 18913944 | DISTRIBUTING ELECTRONIC SURVEYS THROUGH A MESSENGER PLATFORM | KOLOSOWSKI-GAGER, KATHERINE | 44d overdue |
| 18929363 | DETERMINING AND APPLYING ATTRIBUTE DEFINITIONS TO DIGITAL SURVEY DATA TO GENERATE SURVEY ANALYSES | WALTON, CHESIREE A | 3d overdue |
| 19059854 | RESPONDING TO QUERIES BY GENERATING A SYNTHETIC AUDIENCE OF RESPONDENTS | BOROWSKI, MICHAEL | — |
| 18824067 | DETERMINING FRAUDULENT SURVEY RESPONSES TO DIGITAL SURVEYS USING RULE-BASED MODELS AND MACHINE-LEARNING MODELS | KHATTAR, RAJESH | — |
| 18793112 | DETERMINING INTERACTION CONTEXT FOR INTERACTIONS GENERATED FROM USER ENGAGEMENT EVENTS | MISIASZEK, AMBER ALTSCHUL | — |
| 18322180 | INTELLIGENTLY COMBINING RELEVANT DATA ITEMS OF REQUESTED DATA SETS DURING INGESTION | YOUNG, ASHLEY YA-SHEH | — |
| Art Unit | Apps |
|---|---|
| 3624 | 2 |
| 2657 | 1 |
| 3687 | 1 |
| 3684 | 1 |
| 3682 | 1 |
| 3625 | 1 |
| App # | Title | Examiner | Art Unit | Statutes | Status | Due in | AI | Filed |
|---|---|---|---|---|---|---|---|---|
| 19059854 | RESPONDING TO QUERIES BY GENERATING A SYNTHETIC AUDIENCE OF RESPONDENTS | BOROWSKI, MICHAEL | 3624 | §101§103 | Non-Final OA | — | Pending | Feb 21, 2025 |
| 18962421 | INTELLIGENTLY SUMMARIZING AND PRESENTING TEXTUAL RESPONSES WITH MACHINE LEARNING | BECKER, TYLER JUSTIN | 2657 | §102§103 | Non-Final OA | — | Pending | Nov 27, 2024 |
| 18929363 | DETERMINING AND APPLYING ATTRIBUTE DEFINITIONS TO DIGITAL SURVEY DATA TO GENERATE SURVEY ANALYSES | WALTON, CHESIREE A | 3624 | §101§103 | Final Rejection | 3d overdue | Pending | Oct 28, 2024 |
| 18913944 | DISTRIBUTING ELECTRONIC SURVEYS THROUGH A MESSENGER PLATFORM | KOLOSOWSKI-GAGER, KATHERINE | 3687 | §101 | Non-Final OA | 44d overdue | Pending | Oct 11, 2024 |
| 18824067 | DETERMINING FRAUDULENT SURVEY RESPONSES TO DIGITAL SURVEYS USING RULE-BASED MODELS AND MACHINE-LEARNING MODELS | KHATTAR, RAJESH | 3684 | §101 | Non-Final OA | — | Pending | Sep 04, 2024 |
| 18793112 | DETERMINING INTERACTION CONTEXT FOR INTERACTIONS GENERATED FROM USER ENGAGEMENT EVENTS | MISIASZEK, AMBER ALTSCHUL | 3682 | §101 | Non-Final OA | — | Pending | Aug 02, 2024 |
| 18322180 | INTELLIGENTLY COMBINING RELEVANT DATA ITEMS OF REQUESTED DATA SETS DURING INGESTION | YOUNG, ASHLEY YA-SHEH | 3625 | §101§103 | Final Rejection | — | Pending | May 23, 2023 |
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