Prosecution Insights
Prosecution Insights
Technology area: Computing & Software
10 pending office actions • 5 art units • 10 examiners • 0 of 10 (0%) have an AI response strategy ready • 77 patents granted in the last 365 days
GDM Holding LLC is managing 10 pending office actions within the Computing & Software technology area. These matters are assigned to 10 distinct examiners, resulting in a unique examiner for every pending action. This distribution shows no consolidation of cases under any single examiner for the 10 active matters. This decentralized approach means that the company's software innovations are subject to the individual interpretations of 10 different examiners, requiring a flexible prosecution strategy for each application.
The company's prosecution is active in 5 distinct art units. With 10 pending office actions, this concentration suggests that each of the 5 art units is handling multiple cases for the company. This focus within 5 units may provide a more specialized examination environment for the company's software-related filings. The ratio of 10 actions to 5 art units indicates that the company's technology is concentrated within specific technical silos, potentially leading to more consistent outcomes within those specialized groups.
MATTHEW MORRIS CLOTHIER is the busiest examiner for the portfolio, currently handling 1 pending office action. Since all 10 distinct examiners are managing 1 action each, the workload is evenly distributed. The management of 10 pending actions across 5 art units highlights the company's current patent prosecution efforts. This structure allows the company to maintain a focused presence in key technical sectors while managing its active patent applications across a diverse group of examiners.
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 | 1 (10%) |
| §103 only | 2 (20%) |
| §102 only | 1 (10%) |
| Double-patenting only | 2 (20%) |
| Double-patenting + other | 2 (20%) |
| 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 |
|---|---|---|---|
| CLOTHIER, MATTHEW MORRIS | 1 | 83.3% | +20.0% |
| JUNG, JAEWOOK | 1 | 81.8% | +33.3% |
| JACKSON, DANIELLE MARIE | 1 | 80.7% | +27.4% |
| JEANGLAUDE, JEAN BRUNER | 1 | 93.7% | +5.6% |
| SALTARELLI, DOMINIC D | 1 | 79.1% | +14.8% |
| SHANKAR, VIJAY | 1 | 90.9% | +8.6% |
| GOEBEL, EMMA ROSE | 1 | 52.2% | +33.5% |
| BLUST, JASON W | 1 | 79.3% | +14.9% |
| DAY, ROBERT N | 1 | 24.1% | +22.4% |
| CHUANG, SU-TING | 1 | 51.3% | +39.4% |
Cases in front of an examiner with an allow rate of 80%+ where the difficulty is Easy or Medium. The top 4 ordered by deadline are shown.
| App # | Title | Examiner | Due in |
|---|---|---|---|
| 19421916 | VIDEO GENERATION NEURAL NETWORKS WITH CAMERA AND SUBJECT MOTION INPUTS | CLOTHIER, MATTHEW MORRIS | — |
| 19200502 | DATA-DRIVEN ROBOT CONTROL | JACKSON, DANIELLE MARIE | — |
| 19193756 | POPULATION BASED TRAINING OF NEURAL NETWORKS | JEANGLAUDE, JEAN BRUNER | — |
| 19041971 | GENERATIVE INTERACTIVE ENVIRONMENTS | SHANKAR, VIJAY | — |
Multi-statute / §101-driven matters, or cases in front of an examiner with an allow rate under 30%. The top 4 ordered by deadline are shown.
| App # | Title | Examiner | Due in |
|---|---|---|---|
| 17918365 | LEARNING OPTIONS FOR ACTION SELECTION WITH META-GRADIENTS IN MULTI-TASK REINFORCEMENT LEARNING | DAY, ROBERT N | 103d overdue |
| 18102053 | USING EMBEDDINGS, GENERATED USING ROBOT ACTION MODELS, IN CONTROLLING ROBOT TO PERFORM ROBOTIC TASK | BLUST, JASON W | 3d overdue |
| 17337376 | STABLE AND EFFICIENT TRAINING OF ADVERSARIAL MODELS BY AN ITERATED UPDATE OPERATION OF SECOND ORDER OR HIGHER | CHUANG, SU-TING | 41d |
| 19206894 | SEMI-SUPERVISED LEARNING OF ROBOT CONTROL POLICIES | JUNG, JAEWOOK | — |
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 |
|---|---|---|---|
| 17918365 | LEARNING OPTIONS FOR ACTION SELECTION WITH META-GRADIENTS IN MULTI-TASK REINFORCEMENT LEARNING | DAY, ROBERT N | 103d overdue |
| 18102053 | USING EMBEDDINGS, GENERATED USING ROBOT ACTION MODELS, IN CONTROLLING ROBOT TO PERFORM ROBOTIC TASK | BLUST, JASON W | 3d overdue |
| 17337376 | STABLE AND EFFICIENT TRAINING OF ADVERSARIAL MODELS BY AN ITERATED UPDATE OPERATION OF SECOND ORDER OR HIGHER | CHUANG, SU-TING | 41d |
| 19421916 | VIDEO GENERATION NEURAL NETWORKS WITH CAMERA AND SUBJECT MOTION INPUTS | CLOTHIER, MATTHEW MORRIS | — |
| 19206894 | SEMI-SUPERVISED LEARNING OF ROBOT CONTROL POLICIES | JUNG, JAEWOOK | — |
| 19200502 | DATA-DRIVEN ROBOT CONTROL | JACKSON, DANIELLE MARIE | — |
| 19059132 | GENERATING IMAGES USING NEURAL NETWORKS | SALTARELLI, DOMINIC D | — |
| 18899829 | GENERATING A MODEL FOR AN OBJECT ENCOUNTERED BY A ROBOT | GOEBEL, EMMA ROSE | — |
| Art Unit | Apps |
|---|---|
| 2122 | 2 |
| 2614 | 1 |
| 3657 | 1 |
| 2132 | 1 |
| 2146 | 1 |
| App # | Title | Examiner | Art Unit | Statutes | Status | Due in | AI | Filed |
|---|---|---|---|---|---|---|---|---|
| 19421916 | VIDEO GENERATION NEURAL NETWORKS WITH CAMERA AND SUBJECT MOTION INPUTS | CLOTHIER, MATTHEW MORRIS | 2614 | §103 | Final Rejection | — | Pending | Dec 16, 2025 |
| 19206894 | SEMI-SUPERVISED LEARNING OF ROBOT CONTROL POLICIES | JUNG, JAEWOOK | — | §101§102§103 | Non-Final OA | — | Pending | May 13, 2025 |
| 19200502 | DATA-DRIVEN ROBOT CONTROL | JACKSON, DANIELLE MARIE | 3657 | §102DP | Non-Final OA | — | Pending | May 06, 2025 |
| 19193756 | POPULATION BASED TRAINING OF NEURAL NETWORKS | JEANGLAUDE, JEAN BRUNER | 2122 | DP | Non-Final OA | — | Pending | Apr 29, 2025 |
| 19059132 | GENERATING IMAGES USING NEURAL NETWORKS | SALTARELLI, DOMINIC D | — | DP | Non-Final OA | — | Pending | Feb 20, 2025 |
| 19041971 | GENERATIVE INTERACTIVE ENVIRONMENTS | SHANKAR, VIJAY | — | §102 | Non-Final OA | — | Pending | Jan 30, 2025 |
| 18899829 | GENERATING A MODEL FOR AN OBJECT ENCOUNTERED BY A ROBOT | GOEBEL, EMMA ROSE | — | §103§112DP | Non-Final OA | — | Pending | Sep 27, 2024 |
| 18102053 | USING EMBEDDINGS, GENERATED USING ROBOT ACTION MODELS, IN CONTROLLING ROBOT TO PERFORM ROBOTIC TASK | BLUST, JASON W | 2132 | §102§103 | Final Rejection | 3d overdue | Pending | Jan 26, 2023 |
| 17918365 | LEARNING OPTIONS FOR ACTION SELECTION WITH META-GRADIENTS IN MULTI-TASK REINFORCEMENT LEARNING | DAY, ROBERT N | 2122 | §103 | Final Rejection | 103d overdue | Pending | Oct 12, 2022 |
| 17337376 | STABLE AND EFFICIENT TRAINING OF ADVERSARIAL MODELS BY AN ITERATED UPDATE OPERATION OF SECOND ORDER OR HIGHER | CHUANG, SU-TING | 2146 | §101 | Non-Final OA | 41d | Pending | Jun 02, 2021 |
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