Prosecution Insights
Prosecution Insights
10 pending office actions • 8 art units • 10 examiners • 0 of 10 (0%) have an AI response strategy ready • 21 patents granted in the last 365 days
Based on the USPTO statutory response window for each pending office action. 3 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. 3 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 + other | 5 (50%) |
| §103 only | 3 (30%) |
| Multi-statute (no §101) | 2 (20%) |
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 |
|---|---|---|---|
| NGUYEN, TIEN C | 1 | 67.9% | +18.3% |
| APPLE, KIRSTEN SACHWITZ | 1 | 60.6% | +4.5% |
| HALE, BROOKS T | 1 | 49.4% | +33.0% |
| NIGH, JAMES D | 1 | 59.0% | +30.4% |
| NILSSON, ERIC | 1 | 82.8% | +17.2% |
| AGAHI, PUYA | 1 | 49.1% | +23.7% |
| KAPOOR, DEVAN | 1 | 7.1% | +11.1% |
| SPRATT, BEAU D | 1 | 78.9% | +24.2% |
| HADDAD, MAJD MAHER | 1 | 100.0% | +0.0% |
| SPRAUL III, VINCENT ANTON | 1 | 56.5% | +27.4% |
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 |
|---|---|---|---|
| 18189039 | MACHINE LEARNING MODEL TRAINING FOR IMPROVING ANOMALY DETECTION | KAPOOR, DEVAN | 2d overdue |
| 18155228 | MACHINE LEARNING TRAINING APPROACH FOR A MULTITASK PREDICTIVE DOMAIN | HADDAD, MAJD MAHER | 20d |
| 19022202 | SYSTEMS AND METHODS FOR MEDICAL FRAUD DETECTION | NGUYEN, TIEN C | — |
| 19007160 | Supervised and Transferred Learning Techniques for Detecting Fraud or Abuse Relating to Service Events | APPLE, KIRSTEN SACHWITZ | — |
| 19007326 | RADIO FREQUENCY BASED SELF CALIBRATION TECHNIQUES | HALE, BROOKS T | — |
| 18975137 | SECURE AND AUTONOMOUS DATA ENCRYPTION AND SELECTIVE DE-IDENTIFICATION | NIGH, JAMES D | — |
| 18532510 | MACHINE LEARNING TECHNIQUES FOR PREDICTING AND RANKING SUGGESTIONS BASED ON USER ACTIVITY DATA | NILSSON, ERIC | — |
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 8 ordered by deadline are shown.
| App # | Title | Examiner | Due in |
|---|---|---|---|
| 17486272 | PREDICTIVE ANOMALY DETECTION USING DEFINED INTERACTION LEVEL ANOMALY SCORES | SPRAUL III, VINCENT ANTON | 32d overdue |
| 18189039 | MACHINE LEARNING MODEL TRAINING FOR IMPROVING ANOMALY DETECTION | KAPOOR, DEVAN | 2d overdue |
| 19022202 | SYSTEMS AND METHODS FOR MEDICAL FRAUD DETECTION | NGUYEN, TIEN C | — |
| 19007326 | RADIO FREQUENCY BASED SELF CALIBRATION TECHNIQUES | HALE, BROOKS T | — |
| 18975137 | SECURE AND AUTONOMOUS DATA ENCRYPTION AND SELECTIVE DE-IDENTIFICATION | NIGH, JAMES D | — |
| 18532510 | MACHINE LEARNING TECHNIQUES FOR PREDICTING AND RANKING SUGGESTIONS BASED ON USER ACTIVITY DATA | NILSSON, ERIC | — |
| 18300451 | ADAPTIVE PREDICTIONS BASED ON CONTINUOUS SENSOR MEASUREMENTS | AGAHI, PUYA | — |
| 18172521 | INDIVIDUALIZED CLASSIFICATION THRESHOLDS FOR MACHINE LEARNING MODELS | SPRATT, BEAU D | — |
| Art Unit | Apps |
|---|---|
| 3693 | 1 |
| 2166 | 1 |
| 2400 | 1 |
| 3791 | 1 |
| 2126 | 1 |
| 2143 | 1 |
| 2125 | 1 |
| 2129 | 1 |
| App # | Title | Examiner | Art Unit | Statutes | Status | Due in | AI | Filed |
|---|---|---|---|---|---|---|---|---|
| 19022202 | SYSTEMS AND METHODS FOR MEDICAL FRAUD DETECTION | NGUYEN, TIEN C | — | §101DP | Non-Final OA | — | Pending | Jan 15, 2025 |
| 19007160 | Supervised and Transferred Learning Techniques for Detecting Fraud or Abuse Relating to Service Events | APPLE, KIRSTEN SACHWITZ | 3693 | §101§103 | Final Rejection | — | Pending | Dec 31, 2024 |
| 19007326 | RADIO FREQUENCY BASED SELF CALIBRATION TECHNIQUES | HALE, BROOKS T | 2166 | §103§112 | Final Rejection | — | Pending | Dec 31, 2024 |
| 18975137 | SECURE AND AUTONOMOUS DATA ENCRYPTION AND SELECTIVE DE-IDENTIFICATION | NIGH, JAMES D | 2400 | §102§103 | Non-Final OA | — | Pending | Dec 10, 2024 |
| 18532510 | MACHINE LEARNING TECHNIQUES FOR PREDICTING AND RANKING SUGGESTIONS BASED ON USER ACTIVITY DATA | NILSSON, ERIC | — | §101§103 | Non-Final OA | — | Pending | Dec 07, 2023 |
| 18300451 | ADAPTIVE PREDICTIONS BASED ON CONTINUOUS SENSOR MEASUREMENTS | AGAHI, PUYA | 3791 | §103 | Non-Final OA | — | Pending | Apr 14, 2023 |
| 18189039 | MACHINE LEARNING MODEL TRAINING FOR IMPROVING ANOMALY DETECTION | KAPOOR, DEVAN | 2126 | §101§103 | Final Rejection | 2d overdue | Pending | Mar 23, 2023 |
| 18172521 | INDIVIDUALIZED CLASSIFICATION THRESHOLDS FOR MACHINE LEARNING MODELS | SPRATT, BEAU D | 2143 | §103 | Non-Final OA | — | Pending | Feb 22, 2023 |
| 18155228 | MACHINE LEARNING TRAINING APPROACH FOR A MULTITASK PREDICTIVE DOMAIN | HADDAD, MAJD MAHER | 2125 | §101§103 | Non-Final OA | 20d | Pending | Jan 17, 2023 |
| 17486272 | PREDICTIVE ANOMALY DETECTION USING DEFINED INTERACTION LEVEL ANOMALY SCORES | SPRAUL III, VINCENT ANTON | 2129 | §103 | Final Rejection | 32d overdue | Pending | Sep 27, 2021 |
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