Prosecution Insights
Last updated: May 29, 2026

Haynes And Boone, LLP (59868)

6 pending office actions • 2 clients • 6 examiners • 6 art units • 0 of 6 (0%) have an AI response strategy ready • 1 patents granted in the last 365 days

Portfolio Summary

6
Total Pending OAs
3
Non-Final OAs
3
Final Rejections
0
Advisory / Quayle

Response Deadline Pressure

Based on the USPTO statutory response window for each pending office action. 6 of the docket's apps have a known mailing date; the rest are excluded from the tile counts.

4
Overdue
0
Due this week
1
Due this month
1
Due in next 60 days
0
Due later

Deadline Fire Line

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. 6 of the docket's apps have a known mailing date.

-30dToday30d60d90d120d
Overdue (4)Due ≤ 30 days (1)Due ≤ 60 days (1)

Case Difficulty Mix

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.

0
Hard (0%)
6
Medium (100%)
0
Easy (0%)
0
Unknown (0%)

Rejection Statute Mix

BucketCases
§103 only6 (100%)

Industry Mix

How the docket's pending cases split across USPTO tech-center bands.

0
Life Sciences
0% of docket
0
Information Tech
0% of docket
4
Communications
67% of docket
1
Semiconductors
17% of docket
1
Mechanical / Eng
17% of docket
0
Business / Other
0% of docket

Time-on-OA Estimate

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.

60 h
Manual time on pending OAs
12 h
Time saved (low, 20%)
21 h
Time saved (mid, 35%)
0.5 wks
FTE-weeks freed (mid)

Top Examiners on this docket

ExaminerApps on this docketAllow rateInterview lift
JACKSON, JORDAN L 1 40.8% +38.5%
TSAI, TSUNG YIN 1 81.7% +11.0%
SORRIN, AARON JOSEPH 1 75.8% +45.5%
WINDSOR, COURTNEY J 1 86.5% +8.7%
GEBRESLASSIE, WINTA 1 75.6% +25.0%
RUDDIE, ELLIOT S 1 65.5% +42.5%

Quick Wins (2)

Cases in front of an examiner with an allow rate of 80%+ where the difficulty is Easy or Medium. The top 2 ordered by deadline are shown.

App #TitleExaminerDue in
18536174 HIERARCHICAL WORKFLOW FOR GENERATING ANNOTATED TRAINING DATA FOR MACHINE LEARNING ENABLED IMAGE SEGMENTATION TSAI, TSUNG YIN 107d overdue
18482237 TREATMENT OUTCOME PREDICTION FOR NEOVASCULAR AGE-RELATED MACULAR DEGENERATION USING BASELINE CHARACTERISTICS WINDSOR, COURTNEY J 31d overdue

Interview Candidates (5)

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 5 ordered by deadline are shown.

App #TitleExaminerDue in
18536174 HIERARCHICAL WORKFLOW FOR GENERATING ANNOTATED TRAINING DATA FOR MACHINE LEARNING ENABLED IMAGE SEGMENTATION TSAI, TSUNG YIN 107d overdue
17806252 INHALER DEVICES, MEDICATION FORMULATIONS USED THEREWITH AND METHODS OF MANUFACTURE RUDDIE, ELLIOT S 44d overdue
18482264 MACHINE LEARNING-BASED PREDICTION OF TREATMENT REQUIREMENTS FOR NEOVASCULAR AGE-RELATED MACULAR DEGENERATION (NAMD) SORRIN, AARON JOSEPH 32d overdue
18744107 PREDICTING OPTIMAL TREATMENT REGIMEN FOR NEOVASCULAR AGE-RELATED MACULAR DEGENERATION (NAMD) PATIENTS USING MACHINE LEARNING JACKSON, JORDAN L 27d
18328278 AUTOMATED SCREENING FOR DIABETIC RETINOPATHY SEVERITY USING COLOR FUNDUS IMAGE DATA GEBRESLASSIE, WINTA 33d

Client Portfolio (2 clients)

Client (Assignee)Pending OAs
Roche 3
GENENTECH, INC. 3

Top Art Units

Art UnitApps
28571
26561
26721
26611
26771
37851

Pending Office Actions

App #TitleClientExaminerArt UnitStatutesStatusDue inAIFiled
18744107 PREDICTING OPTIMAL TREATMENT REGIMEN FOR NEOVASCULAR AGE-RELATED MACULAR DEGENERATION (NAMD) PATIENTS USING MACHINE LEARNING Hoffmann-La Roche, Inc. JACKSON, JORDAN L 2857 §103 Final Rejection 27d Pending Jun 14, 2024
18536174 HIERARCHICAL WORKFLOW FOR GENERATING ANNOTATED TRAINING DATA FOR MACHINE LEARNING ENABLED IMAGE SEGMENTATION Genentech, Inc. TSAI, TSUNG YIN 2656 §103 Non-Final OA 107d overdue Pending Dec 11, 2023
18482264 MACHINE LEARNING-BASED PREDICTION OF TREATMENT REQUIREMENTS FOR NEOVASCULAR AGE-RELATED MACULAR DEGENERATION (NAMD) Hoffmann-La Roche, Inc. SORRIN, AARON JOSEPH 2672 §103 Non-Final OA 32d overdue Pending Oct 06, 2023
18482237 TREATMENT OUTCOME PREDICTION FOR NEOVASCULAR AGE-RELATED MACULAR DEGENERATION USING BASELINE CHARACTERISTICS Genentech, Inc. WINDSOR, COURTNEY J 2661 §103 Final Rejection 31d overdue Pending Oct 06, 2023
18328278 AUTOMATED SCREENING FOR DIABETIC RETINOPATHY SEVERITY USING COLOR FUNDUS IMAGE DATA Hoffmann La-Roche Inc. GEBRESLASSIE, WINTA 2677 §103 Final Rejection 33d Pending Jun 02, 2023
17806252 INHALER DEVICES, MEDICATION FORMULATIONS USED THEREWITH AND METHODS OF MANUFACTURE Genentech, Inc. RUDDIE, ELLIOT S 3785 §103 Non-Final OA 44d overdue Pending Jun 09, 2022

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