Technology area: Transportation, E-Commerce & Mechanical Systems
19 pending office actions • 8 art units • 17 examiners • 0 of 19 (0%) have an AI response strategy ready • 59 patents granted in the last 365 days
Maplebear Inc. (dba Instacart) has 19 pending office actions in Transportation, E-Commerce & Mechanical Systems. The matters span 17 distinct examiners and 8 distinct art units, indicating a broad portfolio requiring coordination across multiple examination settings.
BARGEON, BRITTANY E is the busiest examiner, with 2 pending office actions. Portfolio management should combine centralized tracking with examiner-specific response planning across the 8 art units.
Based on the USPTO statutory response window for each pending office action. 9 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. 9 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 | 12 (63%) |
| §101 + other | 4 (21%) |
| §103 only | 2 (11%) |
| No statute on record | 1 (5%) |
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 |
|---|---|---|---|
| BARGEON, BRITTANY E | 2 | 44.9% | +33.8% |
| WEINER, ARIELLE E | 2 | 43.4% | +53.6% |
| SMITH, LINDSEY B | 1 | 51.3% | +54.4% |
| FRUNZI, VICTORIA E. | 1 | 25.3% | +24.5% |
| KANG, TIMOTHY J | 1 | 45.5% | +27.0% |
| BAGGOT, BREFFNI | 1 | 35.7% | +25.8% |
| GARG, YOGESH C | 1 | 61.7% | +33.4% |
| HEFLIN, BRIAN ADAMS | 1 | 39.8% | +32.8% |
| MOORE, REVA R | 1 | 52.9% | +50.0% |
| ASHRAF, WASEEM | 1 | 49.6% | +8.9% |
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 |
|---|---|---|---|
| 18079317 | SELECTING PICKERS FOR SERVICE REQUESTS BASED ON OUTPUT OF COMPUTER MODEL TRAINED TO PREDICT ACCEPTANCES | LUDWIG, PETER L | 38d overdue |
| 18141397 | DETERMINING LIMITS FOR ATTRIBUTES OF AN ORDER FOR FULFILLMENT BY A PICKER USING A MACHINE-LEARNING MODEL | MOORE, REVA R | 30d 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 |
| 18214150 | MACHINE-LEARNED MODEL FOR PERSONALIZING SERVICE OPTIONS IN AN ONLINE CONCIERGE SYSTEM USING LOCATION FEATURES | FRUNZI, VICTORIA E. | 61d |
| 18217329 | MODIFYING RANKINGS OF ITEMS IN SEARCH RESULTS BASED ON ITEM AVAILABILITIES AND SEARCH QUERY ATTRIBUTES | SMITH, LINDSEY B | 70d |
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 |
|---|---|---|---|
| 18141397 | DETERMINING LIMITS FOR ATTRIBUTES OF AN ORDER FOR FULFILLMENT BY A PICKER USING A MACHINE-LEARNING MODEL | MOORE, REVA R | 30d 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 |
| 18214150 | MACHINE-LEARNED MODEL FOR PERSONALIZING SERVICE OPTIONS IN AN ONLINE CONCIERGE SYSTEM USING LOCATION FEATURES | FRUNZI, VICTORIA E. | 61d |
| 18217329 | MODIFYING RANKINGS OF ITEMS IN SEARCH RESULTS BASED ON ITEM AVAILABILITIES AND SEARCH QUERY ATTRIBUTES | SMITH, LINDSEY B | 70d |
| 18213761 | OFFLINE SIMULATION TO TEST AN EFFECT OF A CONFIGURABLE PARAMETER USED BY A CONTENT DELIVERY SYSTEM | BAGGOT, BREFFNI | — |
| Art Unit | Apps |
|---|---|
| 3688 | 4 |
| 3689 | 4 |
| 3627 | 4 |
| 3624 | 3 |
| 3621 | 1 |
| 3628 | 1 |
| 3600 | 1 |
| 3692 | 1 |
| App # | Title | Examiner | Art Unit | Statutes | Status | Due in | AI | Filed |
|---|---|---|---|---|---|---|---|---|
| 18217329 | MODIFYING RANKINGS OF ITEMS IN SEARCH RESULTS BASED ON ITEM AVAILABILITIES AND SEARCH QUERY ATTRIBUTES | SMITH, LINDSEY B | 3688 | §101§102 | Final Rejection | 70d | 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 |
| 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 |
| 18203578 | ESTIMATED TIME OF ARRIVAL DETERMINATIONS IN AN ONLINE CONCIERGE SYSTEM | HEFLIN, BRIAN ADAMS | 3628 | §101 | Non-Final OA | — | Pending | May 30, 2023 |
| 18141397 | DETERMINING LIMITS FOR ATTRIBUTES OF AN ORDER FOR FULFILLMENT BY A PICKER USING A MACHINE-LEARNING MODEL | MOORE, REVA R | 3627 | §101 | Non-Final OA | 30d overdue | Pending | Apr 29, 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 | Non-Final OA | — | 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 |
| 18172956 | VERIFYING ITEMS IN A SHOPPING CART BASED ON WEIGHTS MEASURED FOR THE ITEMS | CHISM, STEVEN R | 3692 | §103 | Non-Final OA | 18d overdue | Pending | Feb 22, 2023 |
| 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 |
| 17982941 | ASSIGNING TEST PERIODS OF GEOGRAPHIC REGIONS TO TREATMENT OR CONTROL GROUPS FOR A/B TESTING | EL-BATHY, MOHAMED N | 3624 | §101 | Final Rejection | — | Pending | Nov 08, 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 |
| 17935916 | ITEM ATTRIBUTE DETERMINATION USING A CO-ENGAGEMENT GRAPH | BYRD, UCHE SOWANDE | 3624 | — | Non-Final OA | — | Pending | Sep 27, 2022 |
| 17855793 | DETERMINING EFFICIENT ROUTES IN A COMPLEX SPACE USING HIERARCHICAL INFORMATION AND SPARSE DATA | WERONSKI, MATTHEW S | 3627 | §103 | Non-Final OA | — | 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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Start Free TrialMaplebear Inc. (dba Instacart) has 19 pending office actions as of October 2026, spread across 8 art units and 17 examiners.
Brittany E Bargeon has the most, with 2 of Maplebear Inc. (dba Instacart)'s pending office actions.
Mostly Transportation, E-Commerce & Mechanical Systems (USPTO Tech Center 3600), based on where its pending office actions are examined.
