公开(公告)号：US20230056148A1

公开(公告)日：2023-02-23

申请号：US17406027

申请日：2021-08-18

Applicant: Maplebear Inc.(dba Instacart)

Inventor： Negin Entezari ,  Sharath Rao Karikurve ,  Shishir Kumar Prasad ,  Haixun Wang

IPC: G06Q30/06 ,  G06Q10/08

Abstract: An online concierge shopping system identifies candidate items to a user for inclusion in an order based on prior user inclusion of items in orders and items currently included in the order. From a multi-dimensional tensor generated from cooccurrences of items in orders from various users, the online concierge system generates item embeddings and user embeddings in a common latent space by decomposing the multi-dimensional tensor. From items included in an order, the online concierge system generates an order embedding from item embeddings of the items included in the order. Scores for candidate items are determined based on similarity of item embeddings for the candidate items to the order embedding. Candidate items are selected based on their scores, with the selected candidate items identified to the user.

公开(公告)号：US12287819B2

公开(公告)日：2025-04-29

申请号：US18415551

申请日：2024-01-17

Applicant: Maplebear Inc.

Inventor： Haixun Wang ,  Taesik Na ,  Li Tan ,  Jian Li ,  Xiao Xiao

IPC: G06F16/33 ,  G06F16/334 ,  G06F16/338 ,  G06N20/00

Abstract: A system may generate a prompt based in part on a search query from a customer client device. The prompt instructs a machine learned model to provide item predictions. And the model was trained by: converting structured data describing items of an online catalog to annotated text data (unstructured data), generating training examples based in part on the annotated text data, and training the model using the training examples. The system may receive item predictions generated by the prompt being applied to the machine learned model, the item predictions may have corresponding item identifiers. The item predictions are processed to identify a recommended item from the item predictions. The processing includes determining item information for the recommended item using an item identifier associated with the recommended item. The item information is provided to the customer client device.

公开(公告)号：US20250124498A1

公开(公告)日：2025-04-17

申请号：US18917136

申请日：2024-10-16

Applicant: Maplebear Inc.

Inventor： Prithvishankar Srinivasan ,  Shishir Kumar Prasad ,  Min Xie ,  Shrikar Archak ,  Shih-Ting Lin ,  Haixun Wang

IPC: G06Q30/08 ,  G06Q30/0601

Abstract: An online system presents a sponsored content page to a user in conjunction with a model serving system. The online system accesses a content page for a food item and identifies one or more sponsorship opportunities at the content page. The online system identifies one or more candidate sponsors for each sponsorship opportunity. The online system selects a bidding sponsor for the sponsorship opportunity from the one or more candidate sponsors and a candidate item associated with the bidding sponsor as a sponsored item. The online system provides a content page, a description of the sponsored item, and a request to generate a sponsored content page for the sponsorship opportunity to a model serving system. The online system receives a sponsored content page generated by a machine-learning language model at the model serving system and presents the sponsored content page to a user.

公开(公告)号：US20250117442A1

公开(公告)日：2025-04-10

申请号：US18987482

申请日：2024-12-19

Applicant: Maplebear Inc.

Inventor： Shih-Ting Lin ,  Jonathan Newman ,  Min Xie ,  Haixun Wang

IPC: G06F18/23 ,  G06F18/21 ,  G06F18/214 ,  G06F18/22 ,  G06Q30/0601

Abstract: An online concierge system receives unstructured data describing items offered for purchase by various warehouses. To generate attributes for products from the unstructured data, the online concierge system extracts candidate values for attributes from the unstructured data through natural language processing. One or more users associate a subset candidate values with corresponding attributes, and the online concierge system clusters the remaining candidate values with the candidate values of the subset associated with attributes. One or more users provide input on the accuracy of the generated clusters. The candidate values are applied as labels to items by the online concierge system, which uses the labeled items as training data for an attribute extraction model to predict values for one or more attributes from unstructured data about an item.

公开(公告)号：US12259894B2

公开(公告)日：2025-03-25

申请号：US17666531

申请日：2022-02-07

Applicant: Maplebear Inc.

Inventor： Taesik Na ,  Zhihong Xu ,  Guanghua Shu ,  Tejaswi Tenneti ,  Haixun Wang

IPC: G06F16/2457 ,  G06F16/242

Abstract: An online system maintains various items and maintains values for different attributes of the items, as well as an item embedding for each item. When the online system receives a query for retrieving one or more items, the online system generates an embedding for the query. Based on measures of similarity between the embedding for the query and item embeddings, the online system selects a set of items. The online system identifies a specific attribute of items and generates a whitelist of values for the specific attribute based on measures of similarity between item embeddings for items in the selected set and the embedding for the query. The online system removes items having values for the selected attribute outside of the whitelist of values from the selected set of items to identify items more likely to be relevant to the query.

公开(公告)号：US12204614B2

公开(公告)日：2025-01-21

申请号：US18436611

申请日：2024-02-08

Applicant: Maplebear Inc.

Inventor： Saurav Manchanda ,  Krishnakumar Subramanian ,  Haixun Wang ,  Min Xie

IPC: G06F18/2411 ,  G06F18/214 ,  G06F18/22 ,  G06N3/084

Abstract: An online concierge system trains a classification model as a domain adversarial neural network from training data labeled with source classes from a source domain that do not overlap with target classes from a target domain output by the classification model. The online concierge system maps one or more source classes to a target class. The classification model extracts features from an image, classifies whether an image is from the source domain or the target domain, and predicts a target class for an image from the extracted features. The classification model includes a gradient reversal layer between feature extraction layers and the domain classifier that is used during training, so the feature extraction layers extract domain invariant features from an image.

公开(公告)号：US12033172B2

公开(公告)日：2024-07-09

申请号：US17572450

申请日：2022-01-10

Applicant: Maplebear Inc.

Inventor： Ze He ,  Asif Haque ,  Allan Stewart ,  Haixun Wang ,  Xinyu Li

IPC: G06Q30/0204 ,  G06N3/049 ,  G06N3/084 ,  G06Q30/0282 ,  G06Q30/0601

CPC classification number: G06Q30/0205 ,  G06N3/049 ,  G06N3/084 ,  G06Q30/0282 ,  G06Q30/0635 ,  G06Q30/0639 ,  G06Q30/0641

Abstract: An online concierge system allows users to order items from a warehouse, which may have multiple warehouse locations. The online concierge system provides a user interface to users for ordering the items, with the user interface providing an indication of whether an item is predicted to be available at the warehouse at different times. To predict availability of an item model at different times, the online concierge system selects data from historical information about availability of items at one or more warehouses based on temporal, geospatial, and socioeconomic information about observations of historical availability of items at warehouses. The online concierge system accounts for distances between observations and a time and geographic location in a feature space to select observations for predicting item availability at the time and the geographic location.

公开(公告)号：US12026180B2

公开(公告)日：2024-07-02

申请号：US17736716

申请日：2022-05-04

Applicant: Maplebear Inc.

Inventor： Taesik Na ,  Tejaswi Tenneti ,  Haixun Wang ,  Xiao Xiao

IPC: G06F16/28 ,  G06F16/2457 ,  G06F16/248 ,  G06F18/2413

CPC classification number: G06F16/285 ,  G06F16/24573 ,  G06F16/24575 ,  G06F16/248 ,  G06F18/24147

Abstract: An online system leverages stored interactions with items made by users after the online system received queries to determine display of items satisfying the query. For example, the online system trains a model to predict a likelihood of a user performing an interaction with an item displayed after a query was received. As different items receive different amounts of interaction from users, limited historical interaction with certain items may limit accuracy of the model. The online system generates embeddings for previously received queries and uses measures of similarity between embeddings for queries to generate clusters of queries. Previous interactions with queries in a cluster are combined, with the combined data being used for determining display of items in response to a query.

公开(公告)号：US20230394404A1

公开(公告)日：2023-12-07

申请号：US18235230

申请日：2023-08-17

Applicant: Maplebear Inc.

Inventor： Xinyu Li ,  Haixun Wang ,  Ruoming Jin

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

CPC classification number: G06Q10/06316 ,  G06Q30/0633 ,  G06F16/9024 ,  G06Q10/087 ,  G06Q10/047

Abstract: An online concierge system receives a delivery order containing a list of items, generates a suggested picking sequence for picking the delivery order in a warehouse, and transmits the suggested picking sequence to a mobile device of the shopper. Generating the suggested sequence includes applying a trained item sequence model to the delivery order. Training the item sequence model includes accessing data about a set of historical orders, determining a pairwise distance between each pair of aisles in the warehouse based on the data about the set of historical orders, and generating a distance graph based on the pairwise distance between each pair of aisles in the warehouse. The plurality of nodes represent a plurality of aisles in the warehouse, and the plurality of edges represent pairwise distances between pairs of aisles.

公开(公告)号：US11763229B2

公开(公告)日：2023-09-19

申请号：US17458127

申请日：2021-08-26

Applicant: Maplebear Inc.

Inventor： Xinyu Li ,  Haixun Wang ,  Ruoming Jin

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

CPC classification number: G06Q10/06316 ,  G06F16/9024 ,  G06Q10/047 ,  G06Q10/087 ,  G06Q30/0633

Abstract: An online concierge system generates a suggested picking sequence to reduce the amount of time for a shopper to fulfill an online order of items from a warehouse. The online concierge system determines an average amount of time to sequentially pick items between different aisle pairs for a warehouse based on timestamps from item fulfillment in historical orders. The system generates a distance graph including aisle nodes connected by edges representing the pairwise distance between aisles. The system solves a traveling salesperson problem to generate a ranked order of aisle nodes for each of the historical orders. The system generates a ranked global sequence of aisle nodes based on the plurality of ranked orders of aisle nodes. The system applies the ranked global sequence to new delivery orders to generate the suggested picking sequence for a shopper.