Prosecution Insights
Prosecution Insights
26 pending office actions • 9 art units • 20 examiners • 0 of 26 (0%) have an AI response strategy ready • 62 patents granted in the last 365 days
Based on the USPTO statutory response window for each pending office action. 13 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. 13 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 | 15 (58%) |
| §101 + other | 9 (35%) |
| §103 only | 2 (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 |
|---|---|---|---|
| WEINER, ARIELLE E | 3 | 43.9% | +53.1% |
| FRUNZI, VICTORIA E. | 2 | 25.2% | +24.7% |
| BAGGOT, BREFFNI | 2 | 35.1% | +25.2% |
| BARGEON, BRITTANY E | 2 | 45.0% | +33.7% |
| LUDWIG, PETER L | 2 | 35.2% | +23.2% |
| SPRATT, BEAU D | 1 | 78.9% | +24.2% |
| JARRETT, SCOTT L | 1 | 52.0% | +47.9% |
| KANG, TIMOTHY J | 1 | 45.6% | +25.2% |
| GARG, YOGESH C | 1 | 61.6% | +33.2% |
| ROSEN, NICHOLAS D | 1 | 70.2% | +22.9% |
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 |
|---|---|---|---|
| 18113868 | DYNAMIC REPLENISHMENT OF ITEMS STAGED TO A RAPID FULFILLMENT AREA IN ASSOCIATION WITH AN ONLINE CONCIERGE SYSTEM | MUTSCHLER, JOSEPH M | 52d overdue |
| 18079317 | SELECTING PICKERS FOR SERVICE REQUESTS BASED ON OUTPUT OF COMPUTER MODEL TRAINED TO PREDICT ACCEPTANCES | LUDWIG, PETER L | 38d overdue |
| 18140210 | Feature Recommendations for Machine Learning Models Using Trained Feature Prediction Model | WU, NICHOLAS S | 25d overdue |
| 17955407 | MACHINE LEARNING BASED RESOURCE ALLOCATION OPTIMIZATION | LUDWIG, PETER L | 23d overdue |
| 18213764 | SUGGESTING FULFILLMENT SOURCES FOR A USER AT A NEW LOCATION BASED ON USER'S HISTORICAL ACTIVITY | KANG, TIMOTHY J | 9d overdue |
| 17478411 | RANKING SUGGESTIONS FOR COMPLETING A SEARCH QUERY BASED ON LIKELIHOOD OF A USER INCLUDING ITEMS CORRESPONDING TO THE SUGGESTIONS IN AN ORDER | WEINER, ARIELLE E | 4d overdue |
| 18129464 | PREDICTIVE PICKING OF ITEMS FOR PREPOPULATING A SHOPPING CART FOR A SHOPPER | WEINER, ARIELLE E | 15d |
| 18326900 | Predicting Replacement Items using a Machine-Learning Replacement Model | GARG, YOGESH C | 16d |
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 |
|---|---|---|---|
| 18079317 | SELECTING PICKERS FOR SERVICE REQUESTS BASED ON OUTPUT OF COMPUTER MODEL TRAINED TO PREDICT ACCEPTANCES | LUDWIG, PETER L | 38d overdue |
| 17955407 | MACHINE LEARNING BASED RESOURCE ALLOCATION OPTIMIZATION | LUDWIG, PETER L | 23d overdue |
| 18213764 | SUGGESTING FULFILLMENT SOURCES FOR A USER AT A NEW LOCATION BASED ON USER'S HISTORICAL ACTIVITY | KANG, TIMOTHY J | 9d overdue |
| 17478411 | RANKING SUGGESTIONS FOR COMPLETING A SEARCH QUERY BASED ON LIKELIHOOD OF A USER INCLUDING ITEMS CORRESPONDING TO THE SUGGESTIONS IN AN ORDER | WEINER, ARIELLE E | 4d overdue |
| 18129464 | PREDICTIVE PICKING OF ITEMS FOR PREPOPULATING A SHOPPING CART FOR A SHOPPER | WEINER, ARIELLE E | 15d |
| 18326900 | Predicting Replacement Items using a Machine-Learning Replacement Model | GARG, YOGESH C | 16d |
| 18113874 | DETERMINING ITEM DESIRABILITY TO USERS BASED ON ITEM ATTRIBUTES AND ITEM EXPIRATION DATE | WEINER, ARIELLE E | 22d |
| 18214316 | TRAINED MODELS FOR PREDICTING TIMES FOR COMPLETION OF TASKS FOR AN ORDER PLACED WITH AN ONLINE SYSTEM AND DETERMINING REMEDIAL ACTIONS | JARRETT, SCOTT L | 29d |
| Art Unit | Apps |
|---|---|
| 3689 | 7 |
| 3627 | 7 |
| 3688 | 4 |
| 3621 | 2 |
| 3624 | 2 |
| 2143 | 1 |
| 3625 | 1 |
| 2148 | 1 |
| 3600 | 1 |
| App # | Title | Examiner | Art Unit | Statutes | Status | Due in | AI | Filed |
|---|---|---|---|---|---|---|---|---|
| 18217356 | Machine Learning Model for Predicting Likelihoods of Events on Multiple Different Surfaces of an Online System | SPRATT, BEAU D | 2143 | §103 | Final Rejection | — | Pending | Jun 30, 2023 |
| 18214150 | MACHINE-LEARNED MODEL FOR PERSONALIZING SERVICE OPTIONS IN AN ONLINE CONCIERGE SYSTEM USING LOCATION FEATURES | FRUNZI, VICTORIA E. | 3689 | §101 | Non-Final OA | 61d | Pending | Jun 26, 2023 |
| 18214316 | TRAINED MODELS FOR PREDICTING TIMES FOR COMPLETION OF TASKS FOR AN ORDER PLACED WITH AN ONLINE SYSTEM AND DETERMINING REMEDIAL ACTIONS | JARRETT, SCOTT L | 3625 | §101 | Non-Final OA | 29d | Pending | Jun 26, 2023 |
| 18213764 | SUGGESTING FULFILLMENT SOURCES FOR A USER AT A NEW LOCATION BASED ON USER'S HISTORICAL ACTIVITY | KANG, TIMOTHY J | 3689 | §101 | Non-Final OA | 9d overdue | Pending | Jun 23, 2023 |
| 18213761 | OFFLINE SIMULATION TO TEST AN EFFECT OF A CONFIGURABLE PARAMETER USED BY A CONTENT DELIVERY SYSTEM | BAGGOT, BREFFNI | 3621 | §101 | Non-Final OA | — | Pending | Jun 23, 2023 |
| 18326900 | Predicting Replacement Items using a Machine-Learning Replacement Model | GARG, YOGESH C | 3688 | §101§102 | Final Rejection | 16d | Pending | May 31, 2023 |
| 18199938 | User Interface for Obtaining Picker Intent Signals for Training Machine Learning Models | ROSEN, NICHOLAS D | 3689 | §101 | Non-Final OA | — | Pending | May 20, 2023 |
| 18141394 | WAREHOUSE ITEM ASSORTMENT COMPARISON AND DISPLAY CUSTOMIZATION | MOORE, REVA R | 3627 | §101§103 | Non-Final OA | — | Pending | Apr 29, 2023 |
| 18140210 | Feature Recommendations for Machine Learning Models Using Trained Feature Prediction Model | WU, NICHOLAS S | 2148 | §101§103§112 | Final Rejection | 25d overdue | Pending | Apr 27, 2023 |
| 18138002 | Selecting an Attribute of an Item for Display in an Interface Based on Information Gain Determined for the Attribute by a Trained Machine-Learned Model | BARGEON, BRITTANY E | 3688 | §101§103 | Final Rejection | — | Pending | Apr 21, 2023 |
| 18129464 | PREDICTIVE PICKING OF ITEMS FOR PREPOPULATING A SHOPPING CART FOR A SHOPPER | WEINER, ARIELLE E | 3689 | §101 | Final Rejection | 15d | Pending | Mar 31, 2023 |
| 18129447 | AUTOMATIC KEYWORD GROUPING FOR CAMPAIGN BID CUSTOMIZATION | ASHRAF, WASEEM | 3600 | §101 | Non-Final OA | — | Pending | Mar 31, 2023 |
| 18113874 | DETERMINING ITEM DESIRABILITY TO USERS BASED ON ITEM ATTRIBUTES AND ITEM EXPIRATION DATE | WEINER, ARIELLE E | 3689 | §101§103 | Final Rejection | 22d | Pending | Feb 24, 2023 |
| 18113868 | DYNAMIC REPLENISHMENT OF ITEMS STAGED TO A RAPID FULFILLMENT AREA IN ASSOCIATION WITH AN ONLINE CONCIERGE SYSTEM | MUTSCHLER, JOSEPH M | 3627 | §101 | Final Rejection | 52d overdue | Pending | Feb 24, 2023 |
| 18084938 | OFFLINE SIMULATION OF MULTIPLE EXPERIMENTS WITH VARIANT ADJUSTMENTS | BAGGOT, BREFFNI | 3621 | §101 | Non-Final OA | — | Pending | Dec 20, 2022 |
| 18079317 | SELECTING PICKERS FOR SERVICE REQUESTS BASED ON OUTPUT OF COMPUTER MODEL TRAINED TO PREDICT ACCEPTANCES | LUDWIG, PETER L | 3627 | §101§103 | Final Rejection | 38d overdue | Pending | Dec 12, 2022 |
| 18079544 | GENERATING AN ORDER INCLUDING MULTIPLE ITEMS FROM A USER INTENT DETERMINED FROM UNSTRUCTURED DATA RECEIVED VIA A CHAT INTERFACE | LADONI, AHOORA | 3688 | §101 | Non-Final OA | — | Pending | Dec 12, 2022 |
| 17977759 | GENERATING A SCHEDULE FOR A PICKER OF AN ONLINE CONCIERGE SYSTEM BASED ON AN EARNINGS GOAL AND AVAILABILITY INFORMATION | GAVIN, KRISTIN ELIZABETH | 3624 | §101 | Final Rejection | — | Pending | Oct 31, 2022 |
| 17955407 | MACHINE LEARNING BASED RESOURCE ALLOCATION OPTIMIZATION | LUDWIG, PETER L | 3627 | §101§103 | Final Rejection | 23d overdue | Pending | Sep 28, 2022 |
| 17935091 | GENERATING ORDER BATCHES FOR AN ONLINE CONCIERGE SYSTEM | BURSUM, KIMBERLY SUZANNE | 3627 | §101 | Final Rejection | 26d | Pending | Sep 24, 2022 |
| 17900533 | SIMULATING AN APPLICATION OF A TREATMENT ON A DEMAND SIDE AND A SUPPLY SIDE ASSOCIATED WITH AN ONLINE SYSTEM | KOESTER, MICHAEL RICHARD | 3624 | §103 | Non-Final OA | 45d overdue | Pending | Aug 31, 2022 |
| 17900744 | ITEM AVAILABILITY MODEL PRODUCING ITEM VERIFICATION NOTIFICATIONS | FRUNZI, VICTORIA E. | 3689 | §101§102§103 | Final Rejection | — | Pending | Aug 31, 2022 |
| 17855793 | DETERMINING EFFICIENT ROUTES IN A COMPLEX SPACE USING HIERARCHICAL INFORMATION AND SPARSE DATA | WERONSKI, MATTHEW S | 3627 | §101§103§112 | Final Rejection | — | Pending | Jul 01, 2022 |
| 17846887 | MACHINE LEARNED MODEL FOR MANAGING FOUNDATIONAL ITEMS IN CONCIERGE SYSTEM | BARGEON, BRITTANY E | 3688 | §101 | Final Rejection | — | Pending | Jun 22, 2022 |
| 17524491 | DIRECTLY IDENTIFYING ITEMS FROM AN ITEM CATALOG SATISFYING A RECEIVED QUERY USING A MODEL DETERMINING MEASURES OF SIMILARITY BETWEEN ITEMS IN THE ITEM CATALOG AND THE QUERY | GOYEA, OLUSEGUN | 3627 | §101 | Final Rejection | — | Pending | Nov 11, 2021 |
| 17478411 | RANKING SUGGESTIONS FOR COMPLETING A SEARCH QUERY BASED ON LIKELIHOOD OF A USER INCLUDING ITEMS CORRESPONDING TO THE SUGGESTIONS IN AN ORDER | WEINER, ARIELLE E | 3689 | §101 | Non-Final OA | 4d overdue | Pending | Sep 17, 2021 |
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