Prosecution Insights
Prosecution Insights
Technology areas: Computing & Software • Transportation, E-Commerce & Mechanical Systems
6 pending office actions • 4 art units • 6 examiners • 0 of 6 (0%) have an AI response strategy ready • 3 patents granted in the last 365 days
Gusto Inc. is managing 6 pending office actions in the Transportation, E-Commerce & Mechanical Systems technology area. These actions are distributed among 6 distinct examiners and 4 distinct art units. This indicates that every pending action is being reviewed by a different individual, though some examiners share the same art unit. This level of distribution requires the company to adapt to 6 different examiner styles while navigating the procedures of 4 different technical departments.
PHAM, KHANH B is the busiest examiner for Gusto Inc., with 1 pending office action. With 6 distinct examiners each handling 1 action, the prosecution workload is fully decentralized. The involvement of 4 distinct art units suggests a variety of technical sub-specialties within the company's pending portfolio. Because there are 6 pending office actions and 6 distinct examiners, no single examiner has an outsized influence on the company's current patenting outcomes, necessitating a broad and flexible prosecution strategy across the Transportation, E-Commerce & Mechanical Systems sector.
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 | 2 (33%) |
| §101 + other | 1 (17%) |
| §103 only | 1 (17%) |
| Double-patenting only | 2 (33%) |
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 |
|---|---|---|---|
| PHAM, KHANH B | 1 | 72.6% | +15.2% |
| LWIN, MAUNG T | 1 | 88.9% | +21.8% |
| CUNNINGHAM II, GREGORY S | 1 | 64.6% | +30.6% |
| GOYEA, OLUSEGUN | 1 | 64.9% | +33.3% |
| VELEZ-LOPEZ, MARIO M | 1 | 74.6% | +4.9% |
| LOFTIS, JOHNNA RONEE | 1 | 43.1% | +4.5% |
Cases in front of an examiner with an allow rate of 80%+ where the difficulty is Easy or Medium. The top 1 ordered by deadline are shown.
| App # | Title | Examiner | Due in |
|---|---|---|---|
| 19066094 | TAGGING AND AUDITING SENSITIVE INFORMATION IN A DATABASE ENVIRONMENT | LWIN, MAUNG T | — |
Multi-statute / §101-driven matters, or cases in front of an examiner with an allow rate under 30%. The top 3 ordered by deadline are shown.
| App # | Title | Examiner | Due in |
|---|---|---|---|
| 18820890 | WORK TRACKING AND ADVANCES IN AN EMPLOYEE DATABASE SYSTEM | GOYEA, OLUSEGUN | 17d overdue |
| 18599918 | Machine-Learned Action Prediction in a Database Environment | LOFTIS, JOHNNA RONEE | 26d |
| 18829119 | USER BEHAVIOR-BASED MACHINE LEARNING IN ENTITY ACCOUNT CONFIGURATION | CUNNINGHAM II, GREGORY 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 4 ordered by deadline are shown.
| App # | Title | Examiner | Due in |
|---|---|---|---|
| 18820890 | WORK TRACKING AND ADVANCES IN AN EMPLOYEE DATABASE SYSTEM | GOYEA, OLUSEGUN | 17d overdue |
| 19216728 | Navigation Goal Identification Using Clustering | PHAM, KHANH B | — |
| 19066094 | TAGGING AND AUDITING SENSITIVE INFORMATION IN A DATABASE ENVIRONMENT | LWIN, MAUNG T | — |
| 18829119 | USER BEHAVIOR-BASED MACHINE LEARNING IN ENTITY ACCOUNT CONFIGURATION | CUNNINGHAM II, GREGORY S | — |
| Art Unit | Apps |
|---|---|
| 2166 | 1 |
| 3694 | 1 |
| 3627 | 1 |
| 3625 | 1 |
| App # | Title | Examiner | Art Unit | Statutes | Status | Due in | AI | Filed |
|---|---|---|---|---|---|---|---|---|
| 19216728 | Navigation Goal Identification Using Clustering | PHAM, KHANH B | 2166 | DP | Non-Final OA | — | Pending | May 23, 2025 |
| 19066094 | TAGGING AND AUDITING SENSITIVE INFORMATION IN A DATABASE ENVIRONMENT | LWIN, MAUNG T | — | DP | Final Rejection | — | Pending | Feb 27, 2025 |
| 18829119 | USER BEHAVIOR-BASED MACHINE LEARNING IN ENTITY ACCOUNT CONFIGURATION | CUNNINGHAM II, GREGORY S | 3694 | §101 | Final Rejection | — | Pending | Sep 09, 2024 |
| 18820890 | WORK TRACKING AND ADVANCES IN AN EMPLOYEE DATABASE SYSTEM | GOYEA, OLUSEGUN | 3627 | §101 | Final Rejection | 17d overdue | Pending | Aug 30, 2024 |
| 18815704 | Automated Field Placement For Uploaded Documents | VELEZ-LOPEZ, MARIO M | — | §103Other | Non-Final OA | — | Pending | Aug 26, 2024 |
| 18599918 | Machine-Learned Action Prediction in a Database Environment | LOFTIS, JOHNNA RONEE | 3625 | §101§103 | Non-Final OA | 26d | Pending | Mar 08, 2024 |
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