Technology area: Computing & Software
13 pending office actions • 2 clients • 13 examiners • 11 art units • 0 of 13 (0%) have an AI response strategy ready
Hopewell IP, P.C. manages patent prosecution in the Computing & Software technology area. The firm has handled 96 career applications with a resolved allowance rate of 100.0 percent. This allowance rate indicates that every application reaching a final resolution has been successfully granted. This success is documented across its entire career history for this profile.
The firm currently handles 12 pending office actions, showing an active and ongoing workload. On average, the firm requires 2.47 office actions per allowance. This average of 2.47 actions is achieved while maintaining the 100.0 percent allowance rate across its 96 career applications. Practitioners can view these 12 pending actions as the current stage of the firm's prosecution efforts. The 2.47 average office actions per allowance provides a benchmark for the typical path to a patent grant.
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 | 7 (54%) |
| §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% | +11.1% |
| 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 | 70.1% | +20.4% |
| RUTTEN, JAMES D | 1 | 63.4% | +37.7% |
| WONG, WILLIAM | 1 | 31.7% | +26.9% |
| CHIUSANO, ANDREW TSUTOMU | 1 | 56.2% | +27.3% |
| UDDIN, MD I | 1 | 77.0% | +73.5% |
| SHALU, ZELALEM W | 1 | 31.9% | +21.7% |
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 |
| 17987761 | SHARED MODEL TRAINING WITH PRIVACY PROTECTIONS | UDDIN, MD I | 55d |
| 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 | — |
| 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 |
|---|---|---|---|
| 17987761 | SHARED MODEL TRAINING WITH PRIVACY PROTECTIONS | UDDIN, MD I | 55d |
| 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 | — |
| 18206395 | MODEL DISTILLATION FOR REDUCING ITERATIONS OF NON-AUTOREGRESSIVE DECODERS | WONG, WILLIAM | — |
| 18202450 | LEARNED DENSITY ESTIMATION WITH IMPLICIT MANIFOLDS | CHIUSANO, ANDREW TSUTOMU | — |
| 17969238 | DISTANCE-BASED PAIR LOSS FOR COLLABORATIVE FILTERING | SHALU, ZELALEM W | — |
| Client (Assignee) | Pending OAs |
|---|---|
| THE TORONTO-DOMINION BANK | 12 |
| Hamid R. Tizhoosh | 1 |
| Art Unit | Apps |
|---|---|
| 2121 | 2 |
| 2144 | 2 |
| 2498 | 1 |
| 2668 | 1 |
| 2196 | 1 |
| 2123 | 1 |
| 2124 | 1 |
| 2169 | 1 |
| 2145 | 1 |
| 2100 | 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 | 2498 | §102§103 | Non-Final OA | — | Pending | May 15, 2025 |
| 18988381 | WEAKLY SUPERVISED ACTION SELECTION LEARNING IN VIDEO | The Toronto-Dominion Bank | BHATNAGAR, ANAND P | 2668 | §101DP | Non-Final OA | — | Pending | Dec 19, 2024 |
| 18903771 | OPTIMIZING THROUGHPUT OF MACHINE-LEARNING APPLICATIONS | The Toronto-Dominion Bank | TONG, JUSTIN CHE-CHUN | 2196 | §102 | Non-Final OA | — | Pending | Oct 01, 2024 |
| 18738557 | MODEL EVALUATION METRICS AND EFFECTIVE MODEL SELECTION | The Toronto-Dominion Bank | RAWLINGS, ZANE ALEXANDER | 2123 | §101§103 | Non-Final OA | — | Pending | Jun 10, 2024 |
| 18672874 | CONTEXT OPTIMIZATION FOR CONTEXT-BASED TABULAR CLASSIFICATION | The Toronto-Dominion Bank | FIGUEROA, KEVIN W | 2124 | §101§103 | Non-Final OA | — | Pending | May 23, 2024 |
| 18618757 | CALIBRATED MODEL INTERVENTION WITH CONFORMAL THRESHOLD | The Toronto-Dominion Bank | RUTTEN, JAMES D | 2121 | §103§112 | Non-Final OA | — | Pending | Mar 27, 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 |
| 18202450 | LEARNED DENSITY ESTIMATION WITH IMPLICIT MANIFOLDS | The Toronto-Dominion Bank | CHIUSANO, ANDREW TSUTOMU | 2144 | §101§103§112 | Final Rejection | — | Pending | May 26, 2023 |
| 17987761 | SHARED MODEL TRAINING WITH PRIVACY PROTECTIONS | Hamid R. Tizhoosh | UDDIN, MD I | 2169 | §101§103 | Non-Final OA | 55d | Pending | Nov 15, 2022 |
| 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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Start Free Trial100.0% of resolved applications prosecuted by Hopewell Ip, P.C. were allowed, across 96 applications in USPTO records.
2.47 office actions per allowed application on average.
13 pending office actions as of October 2026, for 2 clients, before 13 examiners in 11 art units.
