Prosecution Insights
Prosecution Insights
Technology area: Transportation, E-Commerce & Mechanical Systems
8 pending office actions • 6 art units • 8 examiners • 0 of 8 (0%) have an AI response strategy ready • 11 patents granted in the last 365 days
Highradius Corporation manages 8 pending office actions within the Transportation, E-Commerce & Mechanical Systems technology area. These matters are distributed across 8 distinct examiners and 6 distinct art units. This distribution shows that the company's prosecution is handled by a wide variety of personnel across multiple technical divisions, reflecting a broad and diverse patent strategy.
Derick J Holzmacher is the busiest examiner for the portfolio, currently handling 1 pending office action. The presence of 8 distinct examiners for the pending actions indicates that no single examiner is managing more than 1 active matter for the company. This lack of concentration reduces the risk that a single difficult examiner could stall a large portion of the portfolio.
The 6 distinct art units involved suggest that the company's innovations touch on many different aspects of transportation and e-commerce. For the legal team, this means coordinating across different technical groups, each with its own set of supervisors and internal policies. Successfully navigating these active matters will require a highly organized approach to address the unique challenges posed by each art unit and examiner.
Based on the USPTO statutory response window for each pending office action. 3 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. 3 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 only | 5 (62%) |
| §101 + other | 3 (38%) |
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 |
|---|---|---|---|
| HOLZMACHER, DERICK J | 1 | 44.3% | +28.4% |
| PARK, YONG S | 1 | 26.0% | +11.5% |
| MUSTAFA, MOHAMMED H | 1 | 35.2% | +30.2% |
| KANERVO, VIRPI H | 1 | 47.5% | +47.6% |
| LEE, WILLIAM MICHAEL | 1 | — | — |
| OBAID, HAMZEH M | 1 | 38.3% | +22.4% |
| HENRY, MATTHEW D | 1 | 29.7% | +20.3% |
| SHORTER, RASHIDA R | 1 | 18.1% | +26.2% |
Multi-statute / §101-driven matters, or cases in front of an examiner with an allow rate under 30%. The top 8 ordered by deadline are shown.
| App # | Title | Examiner | Due in |
|---|---|---|---|
| 18089959 | SYSTEMS AND METHODS FOR COLLECTION CUSTOMER RANKING | SHORTER, RASHIDA R | 4d overdue |
| 18474429 | MACHINE LEARNING BASED (ML-BASED) COMPUTING METHOD AND SYSTEM FOR FORECASTING FINANCIAL TRANSACTIONS | OBAID, HAMZEH M | 40d |
| 18474423 | MACHINE LEARNING BASED (ML-BASED) COMPUTING METHOD AND SYSTEM FOR DISTRIBUTING FINANCIAL TRANSACTIONS | HENRY, MATTHEW D | 49d |
| 19001628 | MACHINE LEARNING BASED SYSTEM AND METHOD FOR FORECASTING CASH FLOW | HOLZMACHER, DERICK J | — |
| 18942821 | MACHINE LEARNING BASED SYSTEMS AND METHODS FOR IDENTIFYING EQUIVALENT ENTITIES | PARK, YONG S | — |
| 18399766 | MACHINE LEARNING BASED SYSTEMS AND METHODS FOR CREDIT RISK MANAGEMENT | MUSTAFA, MOHAMMED H | — |
| 18396759 | MACHINE LEARNING BASED SYSTEMS AND METHODS FOR IDENTIFICATION OF PAYMENT INFORMATION FROM ELECTRONIC MAILS | KANERVO, VIRPI H | — |
| 18396763 | MACHINE LEARNING BASED SYSTEMS AND METHODS FOR DATA MAPPING FOR REMITTANCE DOCUMENTS | LEE, WILLIAM MICHAEL | — |
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 |
|---|---|---|---|
| 18089959 | SYSTEMS AND METHODS FOR COLLECTION CUSTOMER RANKING | SHORTER, RASHIDA R | 4d overdue |
| 18474429 | MACHINE LEARNING BASED (ML-BASED) COMPUTING METHOD AND SYSTEM FOR FORECASTING FINANCIAL TRANSACTIONS | OBAID, HAMZEH M | 40d |
| 18474423 | MACHINE LEARNING BASED (ML-BASED) COMPUTING METHOD AND SYSTEM FOR DISTRIBUTING FINANCIAL TRANSACTIONS | HENRY, MATTHEW D | 49d |
| 19001628 | MACHINE LEARNING BASED SYSTEM AND METHOD FOR FORECASTING CASH FLOW | HOLZMACHER, DERICK J | — |
| 18942821 | MACHINE LEARNING BASED SYSTEMS AND METHODS FOR IDENTIFYING EQUIVALENT ENTITIES | PARK, YONG S | — |
| 18399766 | MACHINE LEARNING BASED SYSTEMS AND METHODS FOR CREDIT RISK MANAGEMENT | MUSTAFA, MOHAMMED H | — |
| 18396759 | MACHINE LEARNING BASED SYSTEMS AND METHODS FOR IDENTIFICATION OF PAYMENT INFORMATION FROM ELECTRONIC MAILS | KANERVO, VIRPI H | — |
| Art Unit | Apps |
|---|---|
| 3625 | 2 |
| 3694 | 1 |
| 3693 | 1 |
| 3691 | 1 |
| 3624 | 1 |
| 3626 | 1 |
| App # | Title | Examiner | Art Unit | Statutes | Status | Due in | AI | Filed |
|---|---|---|---|---|---|---|---|---|
| 19001628 | MACHINE LEARNING BASED SYSTEM AND METHOD FOR FORECASTING CASH FLOW | HOLZMACHER, DERICK J | 3625 | §101§103 | Non-Final OA | — | Pending | Dec 26, 2024 |
| 18942821 | MACHINE LEARNING BASED SYSTEMS AND METHODS FOR IDENTIFYING EQUIVALENT ENTITIES | PARK, YONG S | 3694 | §101 | Final Rejection | — | Pending | Nov 11, 2024 |
| 18399766 | MACHINE LEARNING BASED SYSTEMS AND METHODS FOR CREDIT RISK MANAGEMENT | MUSTAFA, MOHAMMED H | 3693 | §101 | Non-Final OA | — | Pending | Dec 29, 2023 |
| 18396759 | MACHINE LEARNING BASED SYSTEMS AND METHODS FOR IDENTIFICATION OF PAYMENT INFORMATION FROM ELECTRONIC MAILS | KANERVO, VIRPI H | 3691 | §101 | Non-Final OA | — | Pending | Dec 27, 2023 |
| 18396763 | MACHINE LEARNING BASED SYSTEMS AND METHODS FOR DATA MAPPING FOR REMITTANCE DOCUMENTS | LEE, WILLIAM MICHAEL | — | §101§103§112 | Non-Final OA | — | Pending | Dec 27, 2023 |
| 18474429 | MACHINE LEARNING BASED (ML-BASED) COMPUTING METHOD AND SYSTEM FOR FORECASTING FINANCIAL TRANSACTIONS | OBAID, HAMZEH M | 3624 | §101 | Final Rejection | 40d | Pending | Sep 26, 2023 |
| 18474423 | MACHINE LEARNING BASED (ML-BASED) COMPUTING METHOD AND SYSTEM FOR DISTRIBUTING FINANCIAL TRANSACTIONS | HENRY, MATTHEW D | 3625 | §101§112 | Final Rejection | 49d | Pending | Sep 26, 2023 |
| 18089959 | SYSTEMS AND METHODS FOR COLLECTION CUSTOMER RANKING | SHORTER, RASHIDA R | 3626 | §101 | Non-Final OA | 4d overdue | Pending | Dec 28, 2022 |
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