Prosecution Insights
Prosecution Insights
Technology areas: Computing & Software • Transportation, E-Commerce & Mechanical Systems
12 pending office actions • 8 art units • 12 examiners • 0 of 12 (0%) have an AI response strategy ready • 23 patents granted in the last 365 days
UnitedHealth Group Incorporated is currently managing 11 pending office actions in the Computing & Software technology area. These 11 actions are handled by 11 distinct examiners, which means every pending matter is assigned to a different individual. This high level of examiner diversity suggests that the company must adapt to many different prosecution styles across its software portfolio.
The 11 pending actions are spread across 7 distinct art units, indicating a broad range of technical subject matter within the computing field. TIEN C NGUYEN is the busiest examiner for the portfolio, although they are only responsible for 1 pending office action. This distribution shows that no single examiner dominates the company's pending workload, requiring a broad and varied approach to patent prosecution across multiple specialized art units.
With 11 pending office actions and 11 examiners, the company cannot rely on a single point of contact to streamline its prosecution. Each of the 11 matters requires a distinct strategy tailored to the individual examiner. The 7 art units involved further suggest that the computing and software applications cover a wide array of technical classifications, adding another layer of complexity to the management of these pending actions.
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.
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.
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 | 6 (50%) |
| §103 only | 4 (33%) |
| §112 only | 1 (8%) |
| Multi-statute (no §101) | 1 (8%) |
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 | 68.1% | +18.5% |
| FRAZIER, BRADY W | 1 | 78.4% | +27.3% |
| APPLE, KIRSTEN SACHWITZ | 1 | 60.4% | +4.2% |
| HEALY, NOAH MICHAEL | 1 | 57.8% | +34.8% |
| LEE, ANDREW ELDRIDGE | 1 | 16.8% | +31.5% |
| NILSSON, ERIC | 1 | 82.7% | +17.6% |
| BOSTWICK, SIDNEY VINCENT | 1 | 51.3% | +35.1% |
| KAPOOR, DEVAN | 1 | 7.1% | +11.1% |
| SPRATT, BEAU D | 1 | 78.8% | +24.3% |
| HADDAD, MAJD MAHER | 1 | 100.0% | +0.0% |
Multi-statute / §101-driven matters, or cases in front of an examiner with an allow rate under 30%. 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 |
| 18756923 | Techniques for Dynamic Data Validation | LEE, ANDREW ELDRIDGE | 14d |
| 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 | — |
| 18967920 | PREDICTIVE MONITORING OF THE GLUCOSE-INSULIN ENDOCRINE METABOLIC REGULATORY SYSTEM | HEALY, NOAH MICHAEL | — |
| 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 |
|---|---|---|---|
| 18189039 | MACHINE LEARNING MODEL TRAINING FOR IMPROVING ANOMALY DETECTION | KAPOOR, DEVAN | 2d overdue |
| 18756923 | Techniques for Dynamic Data Validation | LEE, ANDREW ELDRIDGE | 14d |
| 18309092 | SYSTEMS AND METHODS FOR TRAINING AND LEVERAGING A MULTI-HEADED MACHINE LEARNING MODEL FOR PREDICTIVE ACTIONS IN A COMPLEX PREDICTION DOMAIN | BOSTWICK, SIDNEY VINCENT | 76d |
| 19022202 | SYSTEMS AND METHODS FOR MEDICAL FRAUD DETECTION | NGUYEN, TIEN C | — |
| 19007307 | LOCAL SENSOR DATA FILTERING AND ANONYMOUS TRACKING FOR MONITORED ENVIRONMENTS | FRAZIER, BRADY W | — |
| 18967920 | PREDICTIVE MONITORING OF THE GLUCOSE-INSULIN ENDOCRINE METABOLIC REGULATORY SYSTEM | HEALY, NOAH MICHAEL | — |
| 18532510 | MACHINE LEARNING TECHNIQUES FOR PREDICTING AND RANKING SUGGESTIONS BASED ON USER ACTIVITY DATA | NILSSON, ERIC | — |
| 18172521 | INDIVIDUALIZED CLASSIFICATION THRESHOLDS FOR MACHINE LEARNING MODELS | SPRATT, BEAU D | — |
| Art Unit | Apps |
|---|---|
| 3693 | 1 |
| 3684 | 1 |
| 2124 | 1 |
| 2126 | 1 |
| 2143 | 1 |
| 2125 | 1 |
| 2128 | 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 |
| 19007307 | LOCAL SENSOR DATA FILTERING AND ANONYMOUS TRACKING FOR MONITORED ENVIRONMENTS | FRAZIER, BRADY W | — | §112 | Non-Final OA | — | Pending | Dec 31, 2024 |
| 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 |
| 18967920 | PREDICTIVE MONITORING OF THE GLUCOSE-INSULIN ENDOCRINE METABOLIC REGULATORY SYSTEM | HEALY, NOAH MICHAEL | — | §101§103 | Non-Final OA | — | Pending | Dec 04, 2024 |
| 18756923 | Techniques for Dynamic Data Validation | LEE, ANDREW ELDRIDGE | 3684 | §101§102§103 | Final Rejection | 14d | Pending | Jun 27, 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 |
| 18309092 | SYSTEMS AND METHODS FOR TRAINING AND LEVERAGING A MULTI-HEADED MACHINE LEARNING MODEL FOR PREDICTIVE ACTIONS IN A COMPLEX PREDICTION DOMAIN | BOSTWICK, SIDNEY VINCENT | 2124 | §103 | Non-Final OA | 76d | Pending | Apr 28, 2023 |
| 18189039 | MACHINE LEARNING MODEL TRAINING FOR IMPROVING ANOMALY DETECTION | KAPOOR, DEVAN | 2126 | §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 |
| 17578028 | MACHINE LEARNING TECHNIQUES FOR COMPOSITE CLASSIFICATION | STORK, KYLE R | 2128 | §103 | Non-Final OA | 20d | Pending | Jan 18, 2022 |
| 17486272 | PREDICTIVE ANOMALY DETECTION USING DEFINED INTERACTION LEVEL ANOMALY SCORES | SPRAUL III, VINCENT ANTON | 2129 | §103§112 | Non-Final OA | 32d overdue | Pending | Sep 27, 2021 |
IP Author helps IP teams respond to office actions faster with AI-generated responses, examiner analytics, and prosecution intelligence.
Start Free Trial