公开(公告)号：US20250124238A1

公开(公告)日：2025-04-17

申请号：US18912395

申请日：2024-10-10

Applicant: Maplebear Inc.

Inventor： Benjamin Knight ,  Kenneth Jason Sanchez ,  Matthew Negrin ,  Licheng Yin ,  Christopher Billman ,  Rebecca Riso

IPC: G06F40/40 ,  G06F40/205

Abstract: An online system generates text-based representations of various types of data for processing using a large language model. The online system extracts location data from a map of a source location and converts the location data into a text-based representation of the location data. The online system receives a set of item identifiers from a client device of a user and generates an LLM prompt based on the set of item identifiers and the text-based representations of the location data. The online system receives a response from the LLM and parses the response for a text-based description of related items. The online system maps the text-based description of the related items to item identifiers and transmits a notification to the client device that includes item data associated with the related items.

公开(公告)号：US20250086435A1

公开(公告)日：2025-03-13

申请号：US18885294

申请日：2024-09-13

Applicant: Maplebear Inc.

Inventor： Benjamin Knight ,  Kenneth Jason Sanchez ,  Christopher Billman ,  Rebecca Riso ,  Matthew Negrin ,  Licheng Yin

IPC: G06N3/0455 ,  G06N3/09 ,  G06Q10/087

Abstract: An online system detects an anomaly associated with an item selection made by a picker for fulfilling an order of a user of an online system. The system generates a prompt for execution by a machine-learned model trained as a large language model. The prompt comprises a chat log between the picker and the user. The system provides the prompt to the machine-learned model for execution. The system receives, as output from the machine-learned model and based on the chat log, a description indicating whether the anomaly is attributable to the user. The system determines, based on the output from the machine-learned model, that the item selection is not attributable to the user. Responsive to determining that the item selection is not attributable to the user, the system provides a notification to a client device of the user to confirm whether the item selection is approved by the user.

公开(公告)号：US20250147954A1

公开(公告)日：2025-05-08

申请号：US18936854

申请日：2024-11-04

Applicant: Maplebear Inc.

Inventor： Christopher Billman ,  Benjamin Knight ,  Kenneth Jason Sanchez ,  Matthew Negrin ,  Licheng Yin ,  Rebecca Riso

IPC: G06F16/242 ,  G06F16/2455

Abstract: An online system receives information describing a set of items requested by a user and an indication via a chat interface that a particular item needs replacement. The online system generates one or more prompts configured to request a machine learned language model to identify the particular item that needs replacement and to identify one or more replacement items for the particular item. The online system receives a set of item identifiers from the machine learned language model and selects a replacement item from a database based on the set of item identifiers. The online system may also receive an order and a communication history associated with a user including a message with a request to modify the a. The online uses the machine-learning language model to map the request type to the set of API requests for updating the order to reflect the request from the user.

公开(公告)号：US20240177108A1

公开(公告)日：2024-05-30

申请号：US18072311

申请日：2022-11-30

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Youdan Xu ,  Krishna Kumar Selvam ,  Michael Chen ,  Radhika Anand ,  Rebecca Riso ,  Ajay Sampat

IPC: G06Q10/087 ,  G06Q30/0202

CPC classification number: G06Q10/087 ,  G06Q30/0202

Abstract: An online concierge system receives location information associated with pickers and actual orders associated with a geographical zone. A model trained to predict a likelihood an actual order associated with the zone will be available for servicing within a timeframe is accessed and applied to forecasted orders. Each picker is matched to an order for servicing by minimizing a value of a function that is based on a difference between a location associated with each picker matched to an actual order and an associated retailer location, a difference between the location associated with each picker matched to a forecasted order and an associated retailer location, and the predicted likelihood. Recommendations for accepting an actual order, moving to a retailer location associated with a forecasted order, or checking back later with the system are generated based on the matches and sent for display to a client device associated with each picker.

公开(公告)号：US20250086939A1

公开(公告)日：2025-03-13

申请号：US18885173

申请日：2024-09-13

Applicant: Maplebear Inc.

Inventor： Benjamin Knight ,  Kenneth Jason Sanchez ,  Christopher Billman ,  Rebecca Riso ,  Matthew Negrin ,  Licheng Yin

IPC: G06V10/764 ,  G06Q30/0601 ,  G06V10/74 ,  G06V10/774 ,  G06V10/82 ,  G06V20/68

Abstract: An online system may prompt a shopper to capture one or more images of items on a checkout belt of a retailer, wherein the items are for fulfilling orders for one or more users of an online service. An online system may provide the one or more images to a machine learning model configured to classify an item as a product. An online system may classify the items to one or more products by applying the machine learning model to the images. An online system may for each user, matching the classified products to the user's order. An online system may obtain an annotated image of the items highlighting classified products which do not match the user's order. An online system may provide to the shopper the annotated image with a notification of a potential discrepancy.

公开(公告)号：US20240428320A1

公开(公告)日：2024-12-26

申请号：US18213756

申请日：2023-06-23

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Rebecca Riso ,  Bo Xu ,  Kenneth Jason Sanchez ,  Ashish Sinha ,  Chencheng Wu

IPC: G06Q30/0601 ,  G06Q20/40

Abstract: An online system receives a request to confirm a transaction that is associated with an order. The system accepts or declines the transaction based on whether an amount associated with the pending transaction is likely to exceed an expected amount of the order by more than a threshold value. To determine the threshold, the system trains a first model to predict an overspend for an order and then trains a second model to predict an amount of error associated with the predictions from the first model. The outputs of the first model and the second model provide a mean and a variance for an expected distribution of the overspend. If the actual overspend amount for the transaction exists in too high of a percentile of the distribution, the transaction may be flagged for review or declined.