Prosecution Insights
Prosecution Insights
Technology area: Computing & Software
13 pending office actions • 1 client • 13 examiners • 6 art units • 0 of 13 (0%) have an AI response strategy ready
Hopewell IP, P.C. specializes in Computing & Software with 96 career applications. The firm currently has 6 pending office actions. The resolved allowance rate pct is 100.0, which represents the success rate for its resolved applications in the computing sector. This career volume of 96 applications indicates a highly specialized practice in the software sector.
The average office actions per allowance is 2.47. This figure shows the typical amount of examiner interaction needed to achieve an allowance. With 96 career applications and a 100.0 resolved allowance rate pct, the firm has a documented history of performance. The average of 2.47 office actions per allowance is a useful metric for estimating the complexity of the prosecution process. The 6 pending office actions reflect the firm's current workload in its primary technology area.
Based on the USPTO statutory response window for each pending office action. 2 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 (8%) |
| §101 + other | 6 (46%) |
| §103 only | 1 (8%) |
| §102 only | 1 (8%) |
| Multi-statute (no §101) | 4 (31%) |
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 |
|---|---|---|---|
| LEMMA, SAMSON B | 1 | 88.2% | +11.2% |
| MORRIS, JOHN J | 1 | 61.4% | +20.4% |
| BHATNAGAR, ANAND P | 1 | 91.6% | +2.1% |
| TONG, JUSTIN CHE-CHUN | 1 | 45.5% | +32.7% |
| RAWLINGS, ZANE ALEXANDER | 1 | — | — |
| FIGUEROA, KEVIN W | 1 | 70.2% | +20.7% |
| RUTTEN, JAMES D | 1 | 63.3% | +37.7% |
| KIM, JONATHAN J | 1 | 63.6% | +50.0% |
| WONG, WILLIAM | 1 | 30.7% | +27.8% |
| SHALU, ZELALEM W | 1 | 31.6% | +20.4% |
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 |
|---|---|---|---|
| 17405939 | TWO-HEADED ATTENTION FUSED AUTOENCODER FOR CONTEXT-AWARE RECOMMENDATION | ALSHAHARI, SADIK AHMED | 5d |
| 19209181 | IDENTIFYING AND MITIGATING DISPARATE GROUP IMPACT IN DIFFERENTIAL-PRIVACY MACHINE-LEARNED MODELS | LEMMA, SAMSON B | — |
| 18988381 | WEAKLY SUPERVISED ACTION SELECTION LEARNING IN VIDEO | BHATNAGAR, ANAND P | — |
| 18738557 | MODEL EVALUATION METRICS AND EFFECTIVE MODEL SELECTION | RAWLINGS, ZANE ALEXANDER | — |
| 18672874 | CONTEXT OPTIMIZATION FOR CONTEXT-BASED TABULAR CLASSIFICATION | FIGUEROA, KEVIN W | — |
| 18618757 | CALIBRATED MODEL INTERVENTION WITH CONFORMAL THRESHOLD | RUTTEN, JAMES D | — |
| 18425822 | SEMI-LOCAL MODEL IMPORTANCE IN FEATURE SPACE | KIM, JONATHAN J | — |
| 18206395 | MODEL DISTILLATION FOR REDUCING ITERATIONS OF NON-AUTOREGRESSIVE DECODERS | WONG, WILLIAM | — |
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 |
|---|---|---|---|
| 19058668 | MULTI-TABLE DATA STORAGE WITH AUDITABLE DATA CHANGES | MORRIS, JOHN J | 73d overdue |
| 19209181 | IDENTIFYING AND MITIGATING DISPARATE GROUP IMPACT IN DIFFERENTIAL-PRIVACY MACHINE-LEARNED MODELS | LEMMA, SAMSON B | — |
| 18903771 | OPTIMIZING THROUGHPUT OF MACHINE-LEARNING APPLICATIONS | TONG, JUSTIN CHE-CHUN | — |
| 18672874 | CONTEXT OPTIMIZATION FOR CONTEXT-BASED TABULAR CLASSIFICATION | FIGUEROA, KEVIN W | — |
| 18618757 | CALIBRATED MODEL INTERVENTION WITH CONFORMAL THRESHOLD | RUTTEN, JAMES D | — |
| 18425822 | SEMI-LOCAL MODEL IMPORTANCE IN FEATURE SPACE | KIM, JONATHAN J | — |
| 18206395 | MODEL DISTILLATION FOR REDUCING ITERATIONS OF NON-AUTOREGRESSIVE DECODERS | WONG, WILLIAM | — |
| 17969238 | DISTANCE-BASED PAIR LOSS FOR COLLABORATIVE FILTERING | SHALU, ZELALEM W | — |
| Client (Assignee) | Pending OAs |
|---|---|
| THE TORONTO-DOMINION BANK | 13 |
| Art Unit | Apps |
|---|---|
| 2152 | 1 |
| 2144 | 1 |
| 2145 | 1 |
| 2100 | 1 |
| 2126 | 1 |
| 2121 | 1 |
| App # | Title | Client | Examiner | Art Unit | Statutes | Status | Due in | AI | Filed |
|---|---|---|---|---|---|---|---|---|---|
| 19209181 | IDENTIFYING AND MITIGATING DISPARATE GROUP IMPACT IN DIFFERENTIAL-PRIVACY MACHINE-LEARNED MODELS | The Toronto-Dominion Bank | LEMMA, SAMSON B | §102§103 | Non-Final OA | — | Pending | May 15, 2025 | |
| 19058668 | MULTI-TABLE DATA STORAGE WITH AUDITABLE DATA CHANGES | The Toronto-Dominion Bank | MORRIS, JOHN J | 2152 | §103 | Final Rejection | 73d overdue | Pending | Feb 20, 2025 |
| 18988381 | WEAKLY SUPERVISED ACTION SELECTION LEARNING IN VIDEO | The Toronto-Dominion Bank | BHATNAGAR, ANAND P | §101DP | Non-Final OA | — | Pending | Dec 19, 2024 | |
| 18903771 | OPTIMIZING THROUGHPUT OF MACHINE-LEARNING APPLICATIONS | The Toronto-Dominion Bank | TONG, JUSTIN CHE-CHUN | §102 | Non-Final OA | — | Pending | Oct 01, 2024 | |
| 18738557 | MODEL EVALUATION METRICS AND EFFECTIVE MODEL SELECTION | The Toronto-Dominion Bank | RAWLINGS, ZANE ALEXANDER | §101§103 | Non-Final OA | — | Pending | Jun 10, 2024 | |
| 18672874 | CONTEXT OPTIMIZATION FOR CONTEXT-BASED TABULAR CLASSIFICATION | The Toronto-Dominion Bank | FIGUEROA, KEVIN W | §101§103 | Non-Final OA | — | Pending | May 23, 2024 | |
| 18618757 | CALIBRATED MODEL INTERVENTION WITH CONFORMAL THRESHOLD | The Toronto-Dominion Bank | RUTTEN, JAMES D | §103§112 | Non-Final OA | — | Pending | Mar 27, 2024 | |
| 18425822 | SEMI-LOCAL MODEL IMPORTANCE IN FEATURE SPACE | The Toronto-Dominion Bank | KIM, JONATHAN J | §101§103§112 | Non-Final OA | — | Pending | Jan 29, 2024 | |
| 18206395 | MODEL DISTILLATION FOR REDUCING ITERATIONS OF NON-AUTOREGRESSIVE DECODERS | The Toronto-Dominion Bank | WONG, WILLIAM | 2144 | §103§112 | Non-Final OA | — | Pending | Jun 06, 2023 |
| 17969238 | DISTANCE-BASED PAIR LOSS FOR COLLABORATIVE FILTERING | The Toronto-Dominion Bank | SHALU, ZELALEM W | 2145 | §101§103§112 | Final Rejection | — | Pending | Oct 19, 2022 |
| 17968653 | TRANSLATION MODEL WITH LEARNED POSITION AND CORRECTIVE LOSS | The Toronto-Dominion Bank | KIM, SEHWAN | 2100 | §101§102§103 | Final Rejection | — | Pending | Oct 18, 2022 |
| 17485837 | HORIZON-AWARE CUMULATIVE ACCESSIBILITY ESTIMATION | The Toronto-Dominion Bank | MANG, VAN C | 2126 | §101 | Non-Final OA | — | Pending | Sep 27, 2021 |
| 17405939 | TWO-HEADED ATTENTION FUSED AUTOENCODER FOR CONTEXT-AWARE RECOMMENDATION | The Toronto-Dominion Bank | ALSHAHARI, SADIK AHMED | 2121 | §103§112 | Final Rejection | 5d | Pending | Aug 18, 2021 |
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