Prosecution Insights
Prosecution Insights
Technology areas: Communications • Transportation, E-Commerce & Mechanical Systems
3 pending office actions • 3 art units • 3 examiners • 0 of 3 (0%) have an AI response strategy ready
Onc AI Inc. currently has 3 pending office actions within the Transportation, E-Commerce & Mechanical Systems technology area. These 3 actions are distributed among 3 distinct examiners. Each of these examiners is located in a different art unit, resulting in 3 distinct art units for the portfolio. This one-to-one-to-one ratio between actions, examiners, and units indicates a highly diverse set of filings within the technology area.
SASS, KIMBERLY A. is the busiest examiner, responsible for 1 busiest examiner pending office action. With 3 pending office actions and 3 distinct examiners, the prosecution is entirely decentralized. The presence of 3 distinct art units for only 3 cases suggests that each filing covers a unique aspect of the Transportation, E-Commerce & Mechanical Systems sector. Consequently, the applicant must manage three separate prosecution tracks with no overlap in examiner or art unit expertise to streamline the process across the 3 pending office actions.
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 + other | 2 (67%) |
| §103 only | 1 (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 |
|---|---|---|---|
| SASS, KIMBERLY A. | 1 | 53.4% | +51.7% |
| HOANG, HAN DINH | 1 | 75.0% | +17.7% |
| CHOI, PETER H | 1 | 26.5% | +18.2% |
Multi-statute / §101-driven matters, or cases in front of an examiner with an allow rate under 30%. The top 2 ordered by deadline are shown.
| App # | Title | Examiner | Due in |
|---|---|---|---|
| 18787858 | PREDICTING RESPONSES TO BIOLOGIC THERAPIES USING DEEP LEARNING ANALYSIS OF IMAGING AND CLINICAL DATA | SASS, KIMBERLY A. | 44d overdue |
| 18657383 | PREDICTIVE MODELING OF THERAPEUTIC AGENT RESPONSE USING DEEP LEARNING ANALYSIS OF PRE-TREATMENT AND INTRA-TREATMENT SERIAL IMAGING | CHOI, PETER H | — |
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 3 ordered by deadline are shown.
| App # | Title | Examiner | Due in |
|---|---|---|---|
| 18787858 | PREDICTING RESPONSES TO BIOLOGIC THERAPIES USING DEEP LEARNING ANALYSIS OF IMAGING AND CLINICAL DATA | SASS, KIMBERLY A. | 44d overdue |
| 18657080 | AUTOMATED SEGMENTATION OF A CT SCAN FOR PREDICTIVE MODELING OF THERAPEUTIC AGENT RESPONSE USING DEEP LEARNING ANALYSIS | HOANG, HAN DINH | 3d overdue |
| 18657383 | PREDICTIVE MODELING OF THERAPEUTIC AGENT RESPONSE USING DEEP LEARNING ANALYSIS OF PRE-TREATMENT AND INTRA-TREATMENT SERIAL IMAGING | CHOI, PETER H | — |
| Art Unit | Apps |
|---|---|
| 3686 | 1 |
| 2661 | 1 |
| 3681 | 1 |
| App # | Title | Examiner | Art Unit | Statutes | Status | Due in | AI | Filed |
|---|---|---|---|---|---|---|---|---|
| 18787858 | PREDICTING RESPONSES TO BIOLOGIC THERAPIES USING DEEP LEARNING ANALYSIS OF IMAGING AND CLINICAL DATA | SASS, KIMBERLY A. | 3686 | §101§112Other | Final Rejection | 44d overdue | Pending | Jul 29, 2024 |
| 18657080 | AUTOMATED SEGMENTATION OF A CT SCAN FOR PREDICTIVE MODELING OF THERAPEUTIC AGENT RESPONSE USING DEEP LEARNING ANALYSIS | HOANG, HAN DINH | 2661 | §103 | Final Rejection | 3d overdue | Pending | May 07, 2024 |
| 18657383 | PREDICTIVE MODELING OF THERAPEUTIC AGENT RESPONSE USING DEEP LEARNING ANALYSIS OF PRE-TREATMENT AND INTRA-TREATMENT SERIAL IMAGING | CHOI, PETER H | 3681 | §101§102§103 | Non-Final OA | — | Pending | May 07, 2024 |
IP Author helps IP teams respond to office actions faster with AI-generated responses, examiner analytics, and prosecution intelligence.
Start Free Trial