Prosecution Insights
Prosecution Insights
Technology area: Computing & Software
10 pending office actions • 6 art units • 10 examiners • 0 of 10 (0%) have an AI response strategy ready • 22 patents granted in the last 365 days
The Bank of New York Mellon maintains a portfolio with 10 pending office actions, all within the Computing & Software technology area. These actions are distributed across 10 distinct examiners, meaning every single pending matter is being reviewed by a different individual. This distribution of examiners is paired with 6 distinct art units.
The busiest examiner, LIN, KATHERINE Y, is responsible for 1 busiest examiner pending action. Given that there are 10 pending office actions and 10 distinct examiners, no single examiner holds more than one case. This indicates a variety of technical sub-specialties within the Computing & Software field, requiring the applicant to manage 10 separate examiner relationships.
Navigating 6 distinct art units requires an adaptable prosecution strategy. Since each of the 10 pending office actions is with a different examiner, practitioners cannot rely on a single examiner's past behavior to predict outcomes across the whole portfolio. Each of the 10 actions must be handled as a unique interaction within its respective art unit.
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 | 1 (10%) |
| §101 + other | 5 (50%) |
| §103 only | 2 (20%) |
| §112 only | 1 (10%) |
| Multi-statute (no §101) | 1 (10%) |
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 |
|---|---|---|---|
| LIN, KATHERINE Y | 1 | 90.8% | +6.5% |
| SETH, MANAV | 1 | 90.7% | +8.0% |
| APONTE, FRANCISCO JAVIER | 1 | 88.3% | +24.2% |
| ALAM, SHIHAB | 1 | — | — |
| SHELTON, GABRIELLA KANANI | 1 | 73.9% | +24.6% |
| YU, ARIEL J | 1 | 40.3% | +27.4% |
| WU, NICHOLAS S | 1 | 52.4% | +31.4% |
| MULLINAX, CLINT LEE | 1 | 47.3% | +37.3% |
| BRACERO, ANDREW ANGEL | 1 | 92.3% | +20.0% |
| KWON, JUN | 1 | 41.0% | +47.2% |
Cases in front of an examiner with an allow rate of 80%+ where the difficulty is Easy or Medium. The top 2 ordered by deadline are shown.
| App # | Title | Examiner | Due in |
|---|---|---|---|
| 18979911 | METHODS AND SYSTEMS FOR ADAPTIVE, TEMPLATE-INDEPENDENT HANDWRITING EXTRACTION FROM IMAGES USING MACHINE LEARNING MODELS | SETH, MANAV | — |
| 18790613 | SYSTEM AND METHOD FOR TRANSLATING A FIRST CODING LANGUAGE INTO A SECOND CODING LANGUAGE | APONTE, FRANCISCO JAVIER | — |
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 |
|---|---|---|---|
| 17159868 | METHODS AND SYSTEMS FOR USING MACHINE LEARNING MODELS THAT GENERATE CLUSTER-SPECIFIC TEMPORAL REPRESENTATIONS FOR TIME SERIES DATA IN COMPUTER NETWORKS | KWON, JUN | 13d |
| 18344541 | SYSTEM AND METHOD FOR BREAK RESOLUTION AUTOMATION | YU, ARIEL J | 14d |
| 19017203 | SYSTEM AND METHODS FOR APPLICATION FAILOVER AUTOMATION | LIN, KATHERINE Y | 15d |
| 18623521 | SYSTEMS AND METHODS FOR FACILITATING DECOUPLED DISTRIBUTION FROM A MAINFRAME TO DISTRIBUTED PLATFORMS | ALAM, SHIHAB | — |
| 18601183 | SELF-HEALING AUTOMATIONS WITH SELF-SERVICE ARCHITECTURE | SHELTON, GABRIELLA KANANI | — |
| 18096243 | SEGMENTED MACHINE LEARNING-BASED MODELING WITH PERIOD-OVER-PERIOD ANALYSIS | MULLINAX, CLINT LEE | — |
| 17940159 | MAPPING ACTIVATION FUNCTIONS TO DATA FOR DEEP LEARNING | BRACERO, ANDREW ANGEL | — |
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 |
|---|---|---|---|
| 17159868 | METHODS AND SYSTEMS FOR USING MACHINE LEARNING MODELS THAT GENERATE CLUSTER-SPECIFIC TEMPORAL REPRESENTATIONS FOR TIME SERIES DATA IN COMPUTER NETWORKS | KWON, JUN | 13d |
| 18344541 | SYSTEM AND METHOD FOR BREAK RESOLUTION AUTOMATION | YU, ARIEL J | 14d |
| 18790613 | SYSTEM AND METHOD FOR TRANSLATING A FIRST CODING LANGUAGE INTO A SECOND CODING LANGUAGE | APONTE, FRANCISCO JAVIER | — |
| 18601183 | SELF-HEALING AUTOMATIONS WITH SELF-SERVICE ARCHITECTURE | SHELTON, GABRIELLA KANANI | — |
| 18166696 | DIRECTIONAL DRIVERS OF DEEP LEARNING MODELS BASED ON MODEL GRADIENTS | WU, NICHOLAS S | — |
| 18096243 | SEGMENTED MACHINE LEARNING-BASED MODELING WITH PERIOD-OVER-PERIOD ANALYSIS | MULLINAX, CLINT LEE | — |
| 17940159 | MAPPING ACTIVATION FUNCTIONS TO DATA FOR DEEP LEARNING | BRACERO, ANDREW ANGEL | — |
| Art Unit | Apps |
|---|---|
| 2113 | 2 |
| 3627 | 1 |
| 2148 | 1 |
| 2123 | 1 |
| 2126 | 1 |
| 2127 | 1 |
| App # | Title | Examiner | Art Unit | Statutes | Status | Due in | AI | Filed |
|---|---|---|---|---|---|---|---|---|
| 19017203 | SYSTEM AND METHODS FOR APPLICATION FAILOVER AUTOMATION | LIN, KATHERINE Y | 2113 | §101Other | Final Rejection | 15d | Pending | Jan 10, 2025 |
| 18979911 | METHODS AND SYSTEMS FOR ADAPTIVE, TEMPLATE-INDEPENDENT HANDWRITING EXTRACTION FROM IMAGES USING MACHINE LEARNING MODELS | SETH, MANAV | — | §112Other | Non-Final OA | — | Pending | Dec 13, 2024 |
| 18790613 | SYSTEM AND METHOD FOR TRANSLATING A FIRST CODING LANGUAGE INTO A SECOND CODING LANGUAGE | APONTE, FRANCISCO JAVIER | — | §103 | Non-Final OA | — | Pending | Jul 31, 2024 |
| 18623521 | SYSTEMS AND METHODS FOR FACILITATING DECOUPLED DISTRIBUTION FROM A MAINFRAME TO DISTRIBUTED PLATFORMS | ALAM, SHIHAB | — | §101§103 | Non-Final OA | — | Pending | Apr 01, 2024 |
| 18601183 | SELF-HEALING AUTOMATIONS WITH SELF-SERVICE ARCHITECTURE | SHELTON, GABRIELLA KANANI | 2113 | §101§102§103 | Final Rejection | — | Pending | Mar 11, 2024 |
| 18344541 | SYSTEM AND METHOD FOR BREAK RESOLUTION AUTOMATION | YU, ARIEL J | 3627 | §101§103§112 | Final Rejection | 14d | Pending | Jun 29, 2023 |
| 18166696 | DIRECTIONAL DRIVERS OF DEEP LEARNING MODELS BASED ON MODEL GRADIENTS | WU, NICHOLAS S | 2148 | §103 | Final Rejection | — | Pending | Feb 09, 2023 |
| 18096243 | SEGMENTED MACHINE LEARNING-BASED MODELING WITH PERIOD-OVER-PERIOD ANALYSIS | MULLINAX, CLINT LEE | 2123 | §101§103 | Non-Final OA | — | Pending | Jan 12, 2023 |
| 17940159 | MAPPING ACTIVATION FUNCTIONS TO DATA FOR DEEP LEARNING | BRACERO, ANDREW ANGEL | 2126 | §101§102§103 | Non-Final OA | — | Pending | Sep 08, 2022 |
| 17159868 | METHODS AND SYSTEMS FOR USING MACHINE LEARNING MODELS THAT GENERATE CLUSTER-SPECIFIC TEMPORAL REPRESENTATIONS FOR TIME SERIES DATA IN COMPUTER NETWORKS | KWON, JUN | 2127 | §103§112 | Final Rejection | 13d | Pending | Jan 27, 2021 |
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