Prosecution Insights
Prosecution Insights
Technology area: Transportation, E-Commerce & Mechanical Systems
9 pending office actions • 6 art units • 9 examiners • 0 of 9 (0%) have an AI response strategy ready • 13 patents granted in the last 365 days
Chime Financial Inc. is managing a portfolio with 10 pending office actions in the Transportation, E-Commerce & Mechanical Systems technology area. These actions are distributed across 8 distinct art units, indicating a broad and diverse range of technical filings within this sector. The company is interacting with 10 distinct examiners, which suggests a highly fragmented prosecution landscape where each action is being reviewed by a different individual. This high degree of variation requires a robust management system to track the different timelines associated with each unique examiner.
TURCHEN, JAMES R is identified as the busiest examiner, although he is responsible for only 1 pending office action. This means that no single examiner holds a majority of the company's pending workload, as the busiest examiner pending count is just one-tenth of the total 10 pending actions. For practitioners, this requires a versatile strategy to address the varied requirements of 10 different examiners across 8 different art units in the Transportation, E-Commerce & Mechanical Systems field. The lack of examiner concentration suggests that the company's intellectual property strategy is not dependent on any single individual.
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 |
|---|---|
| §101 only | 2 (22%) |
| §101 + other | 3 (33%) |
| §103 only | 1 (11%) |
| Double-patenting only | 1 (11%) |
| Multi-statute (no §101) | 2 (22%) |
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 |
|---|---|---|---|
| TURCHEN, JAMES R | 1 | 82.4% | +33.6% |
| MALHOTRA, SANJEEV | 1 | 65.7% | +30.1% |
| MAGUIRE, LINDSAY M | 1 | 51.3% | +31.6% |
| POLLACK, MELVIN H | 1 | 86.0% | +5.0% |
| COBB, MATTHEW | 1 | 72.9% | +35.2% |
| MILLER, JAMES H | 1 | 40.9% | +36.5% |
| POPHAM, JEFFREY D | 1 | 37.8% | +24.0% |
| NGUYEN, TAN D | 1 | 24.3% | +19.7% |
| PARK, YONG S | 1 | 26.0% | +11.5% |
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 |
|---|---|---|---|
| 19067419 | DETECTING SYNTHETIC USER ACCOUNTS USING SYNTHETIC PATTERNS LEARNED VIA MACHINE LEARNING | TURCHEN, JAMES R | — |
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 |
|---|---|---|---|
| 18363312 | DIGITAL SECURITY PLATFORM FOR ELEVATING COMPUTING DEVICE SECURITY CHALLENGES FOR NETWORK EVENTS | POPHAM, JEFFREY D | 71d overdue |
| 18814225 | GENERATING A MULTI-TRANSACTION DISPUTE PACKAGE | MAGUIRE, LINDSAY M | 43d overdue |
| 18520144 | MACHINE LEARNING SYSTEM FOR ROUTING PAYMENT REQUESTS TO DIFFERENT CUSTOMER ACCOUNTS | MILLER, JAMES H | 23d overdue |
| 17664776 | GRANTING PROVISIONAL CREDIT BASED ON A LIKELIHOOD OF APPROVAL SCORE GENERATED FROM A DISPUTE-EVALUATOR MACHINE-LEARNING MODEL | PARK, YONG S | 9d overdue |
| 18052423 | PREVENTING DIGITAL FRAUD UTILIZING A FRAUD RISK TIERING SYSTEM FOR INITIAL AND ONGOING ASSESSMENT OF RISK | NGUYEN, TAN D | 28d |
| 18827355 | MULTI-STAGE MOBILE DEPOSIT CHECK VERIFICATION AND FRAUD PREVENTION SYSTEM | MALHOTRA, SANJEEV | — |
| 18773121 | NETWORK EVENT DATA STREAMING PLATFORM FOR BATCH DISTRIBUTION AND STREAMING OF NETWORK EVENT DATA | POLLACK, MELVIN H | — |
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 8 ordered by deadline are shown.
| App # | Title | Examiner | Due in |
|---|---|---|---|
| 18752310 | GENERATING A FRAUD PREDICTION UTILIZING A FRAUD-PREDICTION MACHINE-LEARNING MODEL | COBB, MATTHEW | 105d overdue |
| 18363312 | DIGITAL SECURITY PLATFORM FOR ELEVATING COMPUTING DEVICE SECURITY CHALLENGES FOR NETWORK EVENTS | POPHAM, JEFFREY D | 71d overdue |
| 18814225 | GENERATING A MULTI-TRANSACTION DISPUTE PACKAGE | MAGUIRE, LINDSAY M | 43d overdue |
| 18520144 | MACHINE LEARNING SYSTEM FOR ROUTING PAYMENT REQUESTS TO DIFFERENT CUSTOMER ACCOUNTS | MILLER, JAMES H | 23d overdue |
| 17664776 | GRANTING PROVISIONAL CREDIT BASED ON A LIKELIHOOD OF APPROVAL SCORE GENERATED FROM A DISPUTE-EVALUATOR MACHINE-LEARNING MODEL | PARK, YONG S | 9d overdue |
| 18052423 | PREVENTING DIGITAL FRAUD UTILIZING A FRAUD RISK TIERING SYSTEM FOR INITIAL AND ONGOING ASSESSMENT OF RISK | NGUYEN, TAN D | 28d |
| 19067419 | DETECTING SYNTHETIC USER ACCOUNTS USING SYNTHETIC PATTERNS LEARNED VIA MACHINE LEARNING | TURCHEN, JAMES R | — |
| 18827355 | MULTI-STAGE MOBILE DEPOSIT CHECK VERIFICATION AND FRAUD PREVENTION SYSTEM | MALHOTRA, SANJEEV | — |
| Art Unit | Apps |
|---|---|
| 3694 | 2 |
| 3691 | 1 |
| 3619 | 1 |
| 3661 | 1 |
| 2432 | 1 |
| 3629 | 1 |
| App # | Title | Examiner | Art Unit | Statutes | Status | Due in | AI | Filed |
|---|---|---|---|---|---|---|---|---|
| 19067419 | DETECTING SYNTHETIC USER ACCOUNTS USING SYNTHETIC PATTERNS LEARNED VIA MACHINE LEARNING | TURCHEN, JAMES R | — | §103 | Non-Final OA | — | Pending | Feb 28, 2025 |
| 18827355 | MULTI-STAGE MOBILE DEPOSIT CHECK VERIFICATION AND FRAUD PREVENTION SYSTEM | MALHOTRA, SANJEEV | 3691 | §101§103 | Non-Final OA | — | Pending | Sep 06, 2024 |
| 18814225 | GENERATING A MULTI-TRANSACTION DISPUTE PACKAGE | MAGUIRE, LINDSAY M | 3619 | §101§112 | Non-Final OA | 43d overdue | Pending | Aug 23, 2024 |
| 18773121 | NETWORK EVENT DATA STREAMING PLATFORM FOR BATCH DISTRIBUTION AND STREAMING OF NETWORK EVENT DATA | POLLACK, MELVIN H | — | §103§112 | Non-Final OA | — | Pending | Jul 15, 2024 |
| 18752310 | GENERATING A FRAUD PREDICTION UTILIZING A FRAUD-PREDICTION MACHINE-LEARNING MODEL | COBB, MATTHEW | 3661 | DP | Non-Final OA | 105d overdue | Pending | Jun 24, 2024 |
| 18520144 | MACHINE LEARNING SYSTEM FOR ROUTING PAYMENT REQUESTS TO DIFFERENT CUSTOMER ACCOUNTS | MILLER, JAMES H | 3694 | §101 | Non-Final OA | 23d overdue | Pending | Nov 27, 2023 |
| 18363312 | DIGITAL SECURITY PLATFORM FOR ELEVATING COMPUTING DEVICE SECURITY CHALLENGES FOR NETWORK EVENTS | POPHAM, JEFFREY D | 2432 | §102§103§112 | Non-Final OA | 71d overdue | Pending | Aug 01, 2023 |
| 18052423 | PREVENTING DIGITAL FRAUD UTILIZING A FRAUD RISK TIERING SYSTEM FOR INITIAL AND ONGOING ASSESSMENT OF RISK | NGUYEN, TAN D | 3629 | §101§103 | Non-Final OA | 28d | Pending | Nov 03, 2022 |
| 17664776 | GRANTING PROVISIONAL CREDIT BASED ON A LIKELIHOOD OF APPROVAL SCORE GENERATED FROM A DISPUTE-EVALUATOR MACHINE-LEARNING MODEL | PARK, YONG S | 3694 | §101 | Final Rejection | 9d overdue | Pending | May 24, 2022 |
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