Prosecution Insights
Prosecution Insights
Technology area: Transportation, E-Commerce & Mechanical Systems
7 pending office actions • 2 art units • 7 examiners • 0 of 7 (0%) have an AI response strategy ready • 3 patents granted in the last 365 days
UL LLC currently has 7 pending office actions within the Transportation, E-Commerce & Mechanical Systems technology area. These actions are distributed across 2 distinct art units, suggesting that the company's filings are focused on a few specific technical groups within the patent office. The prosecution is being handled by 7 distinct examiners, which means that every one of the 7 pending office actions is currently under review by a different official.
UBALE, GAUTAM is listed as the busiest examiner, though this individual is only responsible for 1 pending office action. This lack of concentration among the 7 distinct examiners indicates that UL LLC must manage seven different examination styles and interpretations. Despite the number of 2 distinct art units involved, the variety of examiners assigned to the 7 pending office actions suggests a decentralized approach to the company's current patent prosecution.
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 only | 1 (14%) |
| §101 + other | 3 (43%) |
| §103 only | 3 (43%) |
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 |
|---|---|---|---|
| UBALE, GAUTAM | 1 | 54.1% | +49.3% |
| PATEL, DIPEN M | 1 | 20.1% | +23.8% |
| ALLGOOD, ALESA M | 1 | 82.5% | +18.6% |
| REAGAN, JAMES A | 1 | 71.4% | +19.9% |
| NIU, JIAHE | 1 | — | — |
| GOLD, HENRY JOYNER | 1 | — | — |
| SENSENIG, SHAUN D | 1 | 14.2% | +16.3% |
Cases in front of an examiner with an allow rate of 80%+ where the difficulty is Easy or Medium. The top 1 ordered by deadline are shown.
| App # | Title | Examiner | Due in |
|---|---|---|---|
| 18940724 | DIGITAL CURRENT METER AND METHODS OF USE THEREOF FOR LEAKAGE/TOUCH CURRENT AND DEVICE INTEROPERABILITY TESTING | ALLGOOD, ALESA M | — |
Multi-statute / §101-driven matters, or cases in front of an examiner with an allow rate under 30%. The top 4 ordered by deadline are shown.
| App # | Title | Examiner | Due in |
|---|---|---|---|
| 19197452 | TECHNOLOGIES FOR USING MACHINE LEARNING TO DETERMINE PRODUCT CERTIFICATION ELIGIBILITY | UBALE, GAUTAM | — |
| 19073635 | USING MACHINE LEARNING TO VIRTUALIZE PRODUCT TESTS | PATEL, DIPEN M | — |
| 18418170 | MACHINE LEARNING TECHNOLOGIES FOR ASSESSING THERMAL ENDURANCE CHARACTERISTICS OF PHYSICAL MATERIALS | NIU, JIAHE | — |
| 18085519 | TECHNOLOGIES FOR USING MACHINE LEARNING TO ASSESS PRODUCTS AND PRODUCT SUPPLIERS | SENSENIG, SHAUN D | — |
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 |
|---|---|---|---|
| 18642347 | TECHNOLOGIES FOR ASSESSING BOUNDARIES OF INTENDED USE DOMAIN BASED ON VALIDATION SPACE | REAGAN, JAMES A | 91d overdue |
| 19197452 | TECHNOLOGIES FOR USING MACHINE LEARNING TO DETERMINE PRODUCT CERTIFICATION ELIGIBILITY | UBALE, GAUTAM | — |
| 19073635 | USING MACHINE LEARNING TO VIRTUALIZE PRODUCT TESTS | PATEL, DIPEN M | — |
| 18940724 | DIGITAL CURRENT METER AND METHODS OF USE THEREOF FOR LEAKAGE/TOUCH CURRENT AND DEVICE INTEROPERABILITY TESTING | ALLGOOD, ALESA M | — |
| 18085519 | TECHNOLOGIES FOR USING MACHINE LEARNING TO ASSESS PRODUCTS AND PRODUCT SUPPLIERS | SENSENIG, SHAUN D | — |
| Art Unit | Apps |
|---|---|
| 3697 | 1 |
| 3629 | 1 |
| App # | Title | Examiner | Art Unit | Statutes | Status | Due in | AI | Filed |
|---|---|---|---|---|---|---|---|---|
| 19197452 | TECHNOLOGIES FOR USING MACHINE LEARNING TO DETERMINE PRODUCT CERTIFICATION ELIGIBILITY | UBALE, GAUTAM | — | §101§103Other | Non-Final OA | — | Pending | May 02, 2025 |
| 19073635 | USING MACHINE LEARNING TO VIRTUALIZE PRODUCT TESTS | PATEL, DIPEN M | — | §101Other | Non-Final OA | — | Pending | Mar 07, 2025 |
| 18940724 | DIGITAL CURRENT METER AND METHODS OF USE THEREOF FOR LEAKAGE/TOUCH CURRENT AND DEVICE INTEROPERABILITY TESTING | ALLGOOD, ALESA M | — | §103 | Non-Final OA | — | Pending | Nov 07, 2024 |
| 18642347 | TECHNOLOGIES FOR ASSESSING BOUNDARIES OF INTENDED USE DOMAIN BASED ON VALIDATION SPACE | REAGAN, JAMES A | 3697 | §103 | Non-Final OA | 91d overdue | Pending | Apr 22, 2024 |
| 18418170 | MACHINE LEARNING TECHNOLOGIES FOR ASSESSING THERMAL ENDURANCE CHARACTERISTICS OF PHYSICAL MATERIALS | NIU, JIAHE | — | §101§103 | Non-Final OA | — | Pending | Jan 19, 2024 |
| 18209994 | TECHNOLOGIES FOR USING RESULTS OF PHYSICAL AND VIRTUAL PRODUCT TESTS TO ASSESS PRODUCT STANDARDS COMPLIANCE | GOLD, HENRY JOYNER | — | §103 | Non-Final OA | — | Pending | Jun 14, 2023 |
| 18085519 | TECHNOLOGIES FOR USING MACHINE LEARNING TO ASSESS PRODUCTS AND PRODUCT SUPPLIERS | SENSENIG, SHAUN D | 3629 | §101§103 | Final Rejection | — | Pending | Dec 20, 2022 |
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