公开(公告)号：US20250139728A1

公开(公告)日：2025-05-01

申请号：US18498445

申请日：2023-10-31

Applicant: Maplebear Inc.

Inventor： Roman Havran ,  Satish Kumar Reddy Kancherla ,  Chen Zhang ,  Sai Kannan Sampath ,  Youdan Xu ,  Yuan Gao

IPC: G06Q50/30 ,  G01C21/00 ,  G06Q30/0601

Abstract: A concierge system identifies retail locations within a distance of a picker client device of a picker. This distance defines a zone and the system provides a map of the zone for display within a picker client application. For each retail location in the zone, the system determines a batch volume for the retail location and an average batch volume for the zone and generates a batch availability score using a model trained on batch volumes for the retail location and batch volume for the zone. The batch availability score can be a value reflecting batch availability or busyness of the retail location relative to other retail locations or can be a wait time prediction in minutes until the picker receives a batch at the retail location. The system modifies how the retail locations are displayed on the map to emphasize those with batch availability scores above a threshold value.

公开(公告)号：US20250139137A1

公开(公告)日：2025-05-01

申请号：US18495581

申请日：2023-10-26

Applicant: Maplebear Inc.

Inventor： Brent Scheibelhut ,  Charles Wesley ,  Siby Alappatt ,  Benjamin Chevoor ,  Viswa Mani Kiran Peddinti

IPC: G06F16/332 ,  G06F16/335 ,  G06T7/00

Abstract: Use of a language model to automatically perform visual assessment of quality of an item being fulfilled by a picker. The online system receives an image of the item and identifies a set of potential problems associated with the item. The online system generates a plurality of prompts for input into the language model including the image and one or more questions each corresponding to a respective potential problem of the set potential problems. The online system requests the language model to generate, based on the plurality of prompts, a feedback response for each potential problem. The online system generates an aggregated output by aggregating the feedback response for each potential problem, and based on the aggregated output, a second message that identifies one or more relevant problems associated with the item. The online system causes a device of the picker to display the second message.

公开(公告)号：US20250139106A1

公开(公告)日：2025-05-01

申请号：US18933807

申请日：2024-10-31

Applicant: Maplebear Inc.

Inventor： Sharath Rao Karikurve ,  Shrikar Archak ,  Shishir Kumar Prasad

IPC: G06F16/2457 ,  G06F16/248

Abstract: An online system performs an atypical replacement recommendation task in conjunction with a model serving system or the interface system to make recommendations to a user for replacing a target item with an atypical replacement item. The online system receives a search query from a user and identifies a target item based on the search query. The online system identifies a set of candidate items for replacing the target item. The online system may select one or more atypical replacement items in the set of candidate items, and generate an explanation for each atypical replacement item. The explanation provides a reason for using the atypical replacement item to replace the target item. The online system provides the atypical replacement items and the corresponding explanations as a response to the search query.

公开(公告)号：US20250131355A1

公开(公告)日：2025-04-24

申请号：US19000089

申请日：2024-12-23

Applicant: Maplebear Inc.

Inventor： Xinyu Li ,  Haixun Wang ,  Ruoming Jin

IPC: G06Q10/0631 ,  G06F16/901 ,  G06Q10/047 ,  G06Q10/087 ,  G06Q30/0601

Abstract: An online system receives an order containing a list of items from a user's client device and tracks the current locations of a client device of a shopper within a warehouse. The system applies a trained item sequence model to generate a suggested picking sequence, minimizing time required for the shopper to pick the items. The item sequence model is trained using historical order data, including durations between picking items from different aisles and pairwise distances between aisle locations. The system transmits the suggested picking sequence to the shopper's client device for display. Responsive to determining that the client device of the shopper's location deviates from the suggested sequence, the system dynamically updates the sequence by applying the model to remain items and the shopper's current location.

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：US20250124019A1

公开(公告)日：2025-04-17

申请号：US18488734

申请日：2023-10-17

Applicant: Maplebear Inc.

Inventor： Bruno Caminada ,  Peter Lambe

IPC: G06F16/23 ,  G06F9/54

Abstract: A system performs incremental updates to an event data store based on hydration and stateful validation of events to allow efficient generation of reports based on event data. The system stores data describing events received in a raw event data store. An event represents user interactions associated with a content item. Each event is classified as a thin event, or a thick event based on the type of user interaction associated with the event. The system monitors changes to the raw event data store, for example, using a listener process. The system stores event data in a hydrated event data store that includes records, each record storing information describing thin events associated with a thick event along with additional attributes describing each thin event.

公开(公告)号：US20250111140A1

公开(公告)日：2025-04-03

申请号：US18374443

申请日：2023-09-28

Applicant: Maplebear Inc.

Inventor： Anant Agarwal

IPC: G06F40/20 ,  G06F11/34

Abstract: An online system manages various internal services using network resources or computing resources. Managing the internal services involves generating executable instructions for provisioning new services or for changing or monitoring existing services. To generate executable instructions for allocating or for monitoring network resources, the online system maintains a database of previously generated executable instructions for provisioning resources along with information about various previously generated instructions, such as comments on the executable instructions or past performance information for the previously generated instructions. To generate instructions for a new internal service, the online system tunes a large language model (LLM) with the database and provides prompts to LLM to generate executable instructions for the internal service based the prompts.

公开(公告)号：US12266005B2

公开(公告)日：2025-04-01

申请号：US18072316

申请日：2022-11-30

Applicant: Maplebear Inc.

Inventor： Ramasubramanian Balasubramanian ,  Lynn Fink ,  Alexandra Hart ,  Sanam Alavizadeh ,  Lauren Scully ,  Samuel Lederer ,  Anna Vitti ,  Lukasz Czekaj ,  Joseph Olivier ,  Michael Prescott ,  Jeong Eun Woo ,  Nicole Yin Chuen Lee Altman

IPC: G06Q30/06 ,  G06Q30/0601 ,  G06Q30/08

Abstract: An online concierge system suggests replacement items when an ordered item may be unavailable. To promote similarity of sources between the replacement item with the ordered item, candidate replacement items are scored, in part, based on a source similarity score based on a source of the candidate replacement item and a source of the ordered item. The source similarity score may be determined by a computer model based on user interactions with item sources. The similarity score may be based on source embeddings that may be determined based on respective item embeddings or may be determined by training source embeddings directly from user-source interactions. The similarity score for a candidate replacement item may be combined with a replacement score indicating the user's likelihood of selecting the candidate replacement item as a replacement to yield a total score for selection as suggestion as a replacement for the ordered item.

公开(公告)号：US20250104040A1

公开(公告)日：2025-03-27

申请号：US18974543

申请日：2024-12-09

Applicant: Maplebear Inc.

Inventor： Yilin Huang ,  Ganglu Wu ,  Xiao Zhou ,  Youming Luo ,  Shiyuan Yang

IPC: G06Q20/20 ,  G01G19/12 ,  G01G19/40 ,  G06V10/25 ,  G06V10/778 ,  G06V10/80 ,  G06V20/50

Abstract: A smart shopping cart includes internally facing cameras and an integrated scale to identify objects that are placed in the cart. To avoid unnecessary processing of images that are irrelevant, and thereby save battery life, the cart uses the scale to detect when an object is placed in the cart. The cart obtains images from a cache and sends those to an object detection machine learning model. The cart captures and sends a load curve as input to the trained model for object detection. Labeled load data and labeled image data are used by a model training system to train the machine learning model to identify an item when it is added to the shopping cart. The shopping cart also uses weight data and the image data from a timeframe associated with the addition of the item to the cart as inputs.

公开(公告)号：US20250095046A1

公开(公告)日：2025-03-20

申请号：US18888607

申请日：2024-09-18

Applicant: Maplebear Inc.

Inventor： Shih-Ting Lin ,  Saurav Manchanda ,  Prithvishankar Srinivasan ,  Shishir Kumar Prasad ,  Min Xie ,  Benwen Sun ,  Axel Mange ,  Wenjie Tang ,  Sanchit Gupta

IPC: G06Q30/0601 ,  G06Q10/0833 ,  G06Q30/0201

Abstract: An online system obtains a target food from an order for a user and alcohol preferences from an order purchase history. The online system generates a prompt for a machine learning model to request alcohol candidates based on the target food category. The prompt includes the alcohol preferences, and requests for each alcohol candidate, a pairing score indicating how well the target food category pairs with the alcohol candidate and a user preference score indicating how well the alcohol candidate aligns with the alcohol preferences. The online system receives as output the candidate alcohol items. Each alcohol candidate has the pairing score, the user preference score, and a textual reason for scores. The online system matches at least one alcohol item from a catalog with each alcohol candidate. A subset of alcohol items is presented to the user as a carousel.