Technology area: Transportation, E-Commerce & Mechanical Systems
8 pending office actions • 6 art units • 8 examiners • 0 of 8 (0%) have an AI response strategy ready • 9 patents granted in the last 365 days
Matrixcare Inc. has 10 pending office actions in Transportation, E-Commerce & Mechanical Systems. The docket spans 10 distinct examiners and 7 distinct art units, showing broad examiner distribution across a smaller number of technical groupings.
HRANEK, KAREN AMANDA is the busiest examiner and handles 1 pending matter. No examiner carries a concentrated share of the docket, so counsel should emphasize consistent internal processes and matter-specific prosecution review.
The difference between examiner and art-unit coverage indicates that some technical groups include multiple examiner assignments. Art-unit organization can help identify shared mechanical or systems issues, while separate examiner histories should guide the wording of amendments and arguments.
Based on the USPTO statutory response window for each pending office action. 4 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. 4 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 | 2 (25%) |
| §101 + other | 4 (50%) |
| §103 only | 2 (25%) |
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 |
|---|---|---|---|
| HRANEK, KAREN AMANDA | 1 | 34.7% | +40.4% |
| LE, LINH GIANG | 1 | 66.3% | -4.5% |
| OBEID, MAMON A | 1 | 46.1% | +33.1% |
| SPRATT, BEAU D | 1 | 78.9% | +23.8% |
| LAGOY, KYRA RAND | 1 | 9.5% | -11.1% |
| REYES, REGINALD R | 1 | 40.9% | +31.5% |
| HULBERT, AMANDA K | 1 | 84.7% | +4.2% |
| WASEEM, HUMA | 1 | 17.5% | +20.2% |
Multi-statute / §101-driven matters, or cases in front of an examiner with an allow rate under 30%. The top 7 ordered by deadline are shown.
| App # | Title | Examiner | Due in |
|---|---|---|---|
| 17772909 | SYSTEMS AND METHODS FOR QUANTIFYING HAZARDS IN LIVING SPACES | WASEEM, HUMA | 182d overdue |
| 18618327 | MACHINE LEARNING-BASED SUMMARIZATION AND EVALUATION OF CLINICAL DATA | OBEID, MAMON A | 79d overdue |
| 19020616 | MACHINE LEARNING-BASED PREDICTIVE ANALYTICS FOR REFERRAL DIAGNOSES | LE, LINH GIANG | 9d |
| 18061178 | MACHINE LEARNING FOR RESOURCE ALLOCATION | REYES, REGINALD R | 54d |
| 19169272 | MACHINE LEARNING TO PREDICT PATIENT OUTCOMES BASED ON POSITIONING | HRANEK, KAREN AMANDA | — |
| 18158909 | MODELS TO PREDICT MEDICATION EFFECTIVENESS | LAGOY, KYRA RAND | — |
| 17785262 | METHOD AND SYSTEM FOR DETECTING MOOD | HULBERT, AMANDA K | — |
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 # | Title | Examiner | Due in |
|---|---|---|---|
| 17772909 | SYSTEMS AND METHODS FOR QUANTIFYING HAZARDS IN LIVING SPACES | WASEEM, HUMA | 182d overdue |
| 18618327 | MACHINE LEARNING-BASED SUMMARIZATION AND EVALUATION OF CLINICAL DATA | OBEID, MAMON A | 79d overdue |
| 18061178 | MACHINE LEARNING FOR RESOURCE ALLOCATION | REYES, REGINALD R | 54d |
| 19169272 | MACHINE LEARNING TO PREDICT PATIENT OUTCOMES BASED ON POSITIONING | HRANEK, KAREN AMANDA | — |
| 18465473 | MACHINE LEARNING TO GENERATE SERVICE RECOMMENDATIONS | SPRATT, BEAU D | — |
| Art Unit | Apps |
|---|---|
| 3684 | 2 |
| 3686 | 2 |
| 3687 | 1 |
| 3629 | 1 |
| 3685 | 1 |
| 3792 | 1 |
| App # | Title | Examiner | Art Unit | Statutes | Status | Due in | AI | Filed |
|---|---|---|---|---|---|---|---|---|
| 19169272 | MACHINE LEARNING TO PREDICT PATIENT OUTCOMES BASED ON POSITIONING | HRANEK, KAREN AMANDA | 3684 | §101§103§112 | Final Rejection | — | Pending | Apr 03, 2025 |
| 19020616 | MACHINE LEARNING-BASED PREDICTIVE ANALYTICS FOR REFERRAL DIAGNOSES | LE, LINH GIANG | 3686 | §101 | Final Rejection | 9d | Pending | Jan 14, 2025 |
| 18618327 | MACHINE LEARNING-BASED SUMMARIZATION AND EVALUATION OF CLINICAL DATA | OBEID, MAMON A | 3687 | §101§103 | Non-Final OA | 79d overdue | Pending | Mar 27, 2024 |
| 18465473 | MACHINE LEARNING TO GENERATE SERVICE RECOMMENDATIONS | SPRATT, BEAU D | 3629 | §103 | Final Rejection | — | Pending | Sep 12, 2023 |
| 18158909 | MODELS TO PREDICT MEDICATION EFFECTIVENESS | LAGOY, KYRA RAND | 3685 | §101§103 | Non-Final OA | — | Pending | Jan 24, 2023 |
| 18061178 | MACHINE LEARNING FOR RESOURCE ALLOCATION | REYES, REGINALD R | 3684 | §101 | Final Rejection | 54d | Pending | Dec 02, 2022 |
| 17785262 | METHOD AND SYSTEM FOR DETECTING MOOD | HULBERT, AMANDA K | 3792 | §101§103 | Non-Final OA | — | Pending | Jun 14, 2022 |
| 17772909 | SYSTEMS AND METHODS FOR QUANTIFYING HAZARDS IN LIVING SPACES | WASEEM, HUMA | 3686 | §103 | Non-Final OA | 182d overdue | Pending | Apr 28, 2022 |
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Start Free TrialMatrixcare Inc. has 8 pending office actions as of October 2026, spread across 6 art units and 8 examiners.
Karen Amanda Hranek has the most, with 1 of Matrixcare Inc.'s pending office actions.
Mostly Transportation, E-Commerce & Mechanical Systems (USPTO Tech Center 3600), based on where its pending office actions are examined.
