Prosecution Insights
Prosecution Insights
Technology areas: Biotechnology & Pharmaceuticals • Computing & Software • Communications
5 pending office actions • 3 art units • 5 examiners • 0 of 5 (0%) have an AI response strategy ready • 3 patents granted in the last 365 days
BostonGene Corporation currently has 5 pending office actions in the Communications technology area. These matters are handled by 5 distinct examiners, creating a distribution where each case is reviewed by a different individual. This suggests that the company's prosecution is spread across several different individuals, with no two cases sharing the same examiner.
The 5 pending office actions are spread across 3 distinct art units. This indicates that the technical scope of the portfolio is relatively focused, even though the examiners are all different. Practitioners should be aware that while the 5 distinct examiners are working within 3 distinct art units, each will bring their own individual examination style to the pending matters.
PAULS, JOHN A is the busiest examiner for the portfolio, responsible for 1 pending office action. This reflects the overall structure of the portfolio where each distinct examiner manages a single case. The lack of examiner overlap means that prosecution strategies must be developed independently for each matter, as no single examiner's decisions will directly affect multiple pending cases.
Based on the USPTO statutory response window for each pending office action. 1 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 | 3 (60%) |
| §103 only | 2 (40%) |
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 |
|---|---|---|---|
| PAULS, JOHN A | 1 | 49.1% | +27.4% |
| KAUR, JASPREET | 1 | 77.8% | +37.5% |
| CORRIELUS, JEAN M | 1 | 84.0% | +12.5% |
| BEVERIDGE, CONNOR HAMMOND | 1 | 0.0% | +0.0% |
| KRIANGCHAIVECH, KETTIP | 1 | 19.3% | +28.7% |
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 |
|---|---|---|---|
| 19224303 | MACHINE LEARNING MODEL TRAINED USING ARTIFICIAL CELL-FREE RNA (CFRNA) EXPRESSION DATA | PAULS, JOHN A | — |
| 18039954 | HIERARCHICAL MACHINE LEARNING TECHNIQUES FOR IDENTIFYING MOLECULAR CATEGORIES FROM EXPRESSION DATA | CORRIELUS, JEAN M | — |
| 18082157 | SYSTEMS AND METHODS FOR DECONVOLUTION OF EXPRESSION DATA | BEVERIDGE, CONNOR HAMMOND | — |
| 17699018 | SYSTEMS AND METHODS FOR GENERATING, VISUALIZING AND CLASSIFYING MOLECULAR FUNCTIONAL PROFILES | KRIANGCHAIVECH, KETTIP | — |
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 |
|---|---|---|---|
| 18711053 | MACHINE LEARNING TECHNIQUES FOR TERTIARY LYMPHOID STRUCTURE (TLS) DETECTION | KAUR, JASPREET | 27d |
| 19224303 | MACHINE LEARNING MODEL TRAINED USING ARTIFICIAL CELL-FREE RNA (CFRNA) EXPRESSION DATA | PAULS, JOHN A | — |
| 18039954 | HIERARCHICAL MACHINE LEARNING TECHNIQUES FOR IDENTIFYING MOLECULAR CATEGORIES FROM EXPRESSION DATA | CORRIELUS, JEAN M | — |
| 17699018 | SYSTEMS AND METHODS FOR GENERATING, VISUALIZING AND CLASSIFYING MOLECULAR FUNCTIONAL PROFILES | KRIANGCHAIVECH, KETTIP | — |
| Art Unit | Apps |
|---|---|
| 2662 | 1 |
| 2159 | 1 |
| 1686 | 1 |
| App # | Title | Examiner | Art Unit | Statutes | Status | Due in | AI | Filed |
|---|---|---|---|---|---|---|---|---|
| 19224303 | MACHINE LEARNING MODEL TRAINED USING ARTIFICIAL CELL-FREE RNA (CFRNA) EXPRESSION DATA | PAULS, JOHN A | — | §101§103 | Non-Final OA | — | Pending | May 30, 2025 |
| 18711053 | MACHINE LEARNING TECHNIQUES FOR TERTIARY LYMPHOID STRUCTURE (TLS) DETECTION | KAUR, JASPREET | 2662 | §103 | Final Rejection | 27d | Pending | May 16, 2024 |
| 18039954 | HIERARCHICAL MACHINE LEARNING TECHNIQUES FOR IDENTIFYING MOLECULAR CATEGORIES FROM EXPRESSION DATA | CORRIELUS, JEAN M | 2159 | §101§112 | Final Rejection | — | Pending | Jun 01, 2023 |
| 18082157 | SYSTEMS AND METHODS FOR DECONVOLUTION OF EXPRESSION DATA | BEVERIDGE, CONNOR HAMMOND | — | §101§103DP | Non-Final OA | — | Pending | Dec 15, 2022 |
| 17699018 | SYSTEMS AND METHODS FOR GENERATING, VISUALIZING AND CLASSIFYING MOLECULAR FUNCTIONAL PROFILES | KRIANGCHAIVECH, KETTIP | 1686 | §103 | Final Rejection | — | Pending | Mar 18, 2022 |
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