Technology areas: Computing & Software • Transportation, E-Commerce & Mechanical Systems
10 pending office actions • 7 art units • 9 examiners • 0 of 10 (0%) have an AI response strategy ready • 12 patents granted in the last 365 days
Actimize Ltd. has 11 pending office actions in Transportation, E-Commerce & Mechanical Systems. Those matters involve 10 examiners across 8 art units, showing a workload distributed among both examiner assignments and technical examination groups.
BUNKER, WILLIAM B is the busiest examiner, with 2 pending matters. The remaining workload is not concentrated solely with that examiner, so docket management should preserve matter-specific tracking while using recurring issues identified across assignments where appropriate.
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 (20%) |
| §101 + other | 3 (30%) |
| §103 only | 4 (40%) |
| §112 only | 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 |
|---|---|---|---|
| BUNKER, WILLIAM B | 2 | 79.8% | +94.4% |
| WASAFF, JOHN S. | 1 | 33.8% | +44.0% |
| SHAH, BHAVIN D | 1 | 41.8% | +23.0% |
| DASGUPTA, SHOURJO | 1 | 65.2% | +39.3% |
| HASBROUCK, MERRITT J | 1 | 9.7% | +7.0% |
| PARK, YONG S | 1 | 25.8% | +11.4% |
| ALABI, OLUWATOSIN O | 1 | 61.2% | +21.7% |
| MEYER, JACQUELINE CHRISTINE | 1 | 66.7% | +62.5% |
| MILLER, ALEXANDRIA JOSEPHINE | 1 | 23.5% | +72.7% |
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 |
|---|---|---|---|
| 18075445 | COMPUTERIZED-METHOD AND COMPUTERIZED-SYSTEM FOR GENERATING A CLASSIFICATION MACHINE LEARNING MODEL FOR IMPLEMENTATION WITH NO TRAINING REQUIREMENT | HASBROUCK, MERRITT J | 70d overdue |
| 18630468 | FRAUD ANALYSIS USING MACHINE LEARNING AND GENERATIVE ARTIFICIAL INTELLIGENCE | BUNKER, WILLIAM B | 8d overdue |
| 18758683 | SYSTEM AND METHOD FOR DETERMINING TRUST INDICATORS | WASAFF, JOHN S. | 7d |
| 17494176 | TRAINING A MACHINE LEARNING MODEL USING INCREMENTAL LEARNING WITHOUT FORGETTING | MILLER, ALEXANDRIA JOSEPHINE | 8d |
| 18665327 | SYSTEM AND METHOD FOR EVALUATING A FINANCIAL CRIME ALERT | SHAH, BHAVIN D | — |
| 18585203 | SYSTEM AND METHOD FOR EMPIRICAL PROMPT TUNING | DASGUPTA, SHOURJO | — |
| 18046195 | USAGE OF EMOTION RECOGNITION IN TRADE MONITORING | PARK, YONG S | — |
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 |
|---|---|---|---|
| 17831159 | LTP-INDUCED ONLINE INCREMENTAL DEEP LEARNING | ALABI, OLUWATOSIN O | 15d overdue |
| 18630468 | FRAUD ANALYSIS USING MACHINE LEARNING AND GENERATIVE ARTIFICIAL INTELLIGENCE | BUNKER, WILLIAM B | 8d overdue |
| 18758683 | SYSTEM AND METHOD FOR DETERMINING TRUST INDICATORS | WASAFF, JOHN S. | 7d |
| 17494176 | TRAINING A MACHINE LEARNING MODEL USING INCREMENTAL LEARNING WITHOUT FORGETTING | MILLER, ALEXANDRIA JOSEPHINE | 8d |
| 17585744 | SYSTEMS AND METHODS FOR AUTOMATICALLY CREATING MACHINE LEARNED FRAUD DETECTION MODELS | MEYER, JACQUELINE CHRISTINE | 72d |
| 18888931 | MULTI-STAGE UNSUPERVISED LEARNING FOR EXTREME LOW-FRAUD SCENARIOS | BUNKER, WILLIAM B | — |
| 18665327 | SYSTEM AND METHOD FOR EVALUATING A FINANCIAL CRIME ALERT | SHAH, BHAVIN D | — |
| 18585203 | SYSTEM AND METHOD FOR EMPIRICAL PROMPT TUNING | DASGUPTA, SHOURJO | — |
| Art Unit | Apps |
|---|---|
| 3691 | 2 |
| 3694 | 2 |
| 2144 | 2 |
| 3629 | 1 |
| 3695 | 1 |
| 2129 | 1 |
| 2142 | 1 |
| App # | Title | Examiner | Art Unit | Statutes | Status | Due in | AI | Filed |
|---|---|---|---|---|---|---|---|---|
| 18888931 | MULTI-STAGE UNSUPERVISED LEARNING FOR EXTREME LOW-FRAUD SCENARIOS | BUNKER, WILLIAM B | 3691 | §103 | Final Rejection | — | Pending | Sep 18, 2024 |
| 18758683 | SYSTEM AND METHOD FOR DETERMINING TRUST INDICATORS | WASAFF, JOHN S. | 3629 | §101 | Non-Final OA | 7d | Pending | Jun 28, 2024 |
| 18665327 | SYSTEM AND METHOD FOR EVALUATING A FINANCIAL CRIME ALERT | SHAH, BHAVIN D | 3694 | §101 | Final Rejection | — | Pending | May 15, 2024 |
| 18630468 | FRAUD ANALYSIS USING MACHINE LEARNING AND GENERATIVE ARTIFICIAL INTELLIGENCE | BUNKER, WILLIAM B | 3691 | §101§103 | Non-Final OA | 8d overdue | Pending | Apr 09, 2024 |
| 18585203 | SYSTEM AND METHOD FOR EMPIRICAL PROMPT TUNING | DASGUPTA, SHOURJO | 2144 | §101§103 | Non-Final OA | — | Pending | Feb 23, 2024 |
| 18075445 | COMPUTERIZED-METHOD AND COMPUTERIZED-SYSTEM FOR GENERATING A CLASSIFICATION MACHINE LEARNING MODEL FOR IMPLEMENTATION WITH NO TRAINING REQUIREMENT | HASBROUCK, MERRITT J | 3695 | §101§103 | Non-Final OA | 70d overdue | Pending | Dec 06, 2022 |
| 18046195 | USAGE OF EMOTION RECOGNITION IN TRADE MONITORING | PARK, YONG S | 3694 | §112 | Final Rejection | — | Pending | Oct 13, 2022 |
| 17831159 | LTP-INDUCED ONLINE INCREMENTAL DEEP LEARNING | ALABI, OLUWATOSIN O | 2129 | §103 | Final Rejection | 15d overdue | Pending | Jun 02, 2022 |
| 17585744 | SYSTEMS AND METHODS FOR AUTOMATICALLY CREATING MACHINE LEARNED FRAUD DETECTION MODELS | MEYER, JACQUELINE CHRISTINE | 2144 | §103 | Final Rejection | 72d | Pending | Jan 27, 2022 |
| 17494176 | TRAINING A MACHINE LEARNING MODEL USING INCREMENTAL LEARNING WITHOUT FORGETTING | MILLER, ALEXANDRIA JOSEPHINE | 2142 | §103 | Final Rejection | 8d | Pending | Oct 05, 2021 |
IP Author helps IP teams respond to office actions faster with AI-generated responses, examiner analytics, and prosecution intelligence.
Start Free TrialActimize Ltd. has 10 pending office actions as of October 2026, spread across 7 art units and 9 examiners.
William B Bunker has the most, with 2 of Actimize Ltd.'s pending office actions.
Mostly Transportation, E-Commerce & Mechanical Systems (USPTO Tech Center 3600), based on where its pending office actions are examined.
