公开(公告)号：US11989770B2

公开(公告)日：2024-05-21

申请号：US17406027

申请日：2021-08-18

Applicant: Maplebear Inc.

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

IPC: G06Q30/00 ,  G06Q10/087 ,  G06Q30/0601

CPC classification number: G06Q30/0633 ,  G06Q10/087

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：US20230252032A1

公开(公告)日：2023-08-10

申请号：US17666531

申请日：2022-02-07

Applicant: Maplebear Inc. (dba Instacart)

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

IPC: G06F16/2457 ,  G06F16/242

CPC classification number: G06F16/24578 ,  G06F16/2438

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：US20230222529A1

公开(公告)日：2023-07-13

申请号：US17572450

申请日：2022-01-10

Applicant: Maplebear Inc. (dba Instacart)

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

IPC: G06Q30/02 ,  G06Q30/06 ,  G06N3/04 ,  G06N3/08

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

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：US20230058829A1

公开(公告)日：2023-02-23

申请号：US17407158

申请日：2021-08-19

Applicant: Maplebear Inc. (dba Instacart)

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

IPC: G06K9/62 ,  G06Q30/06

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：US20230055760A1

公开(公告)日：2023-02-23

申请号：US17405011

申请日：2021-08-17

Applicant: Maplebear Inc.(dba Instacart)

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

IPC: G06K9/62 ,  G06N3/08

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：US20250147997A1

公开(公告)日：2025-05-08

申请号：US18932301

申请日：2024-10-30

Applicant: Maplebear Inc.

Inventor： Xiaochen Wang ,  Taesik Na ,  Xiao Xiao ,  Ruhan Zhang ,  Xuan Zhang ,  Tejaswi Tenneti ,  Haixun Wang

IPC: G06F16/35 ,  G06F16/383

Abstract: An online system updates the labels on negative examples to account for the possibility that the example is a false negative. The system generates a set of initial training examples that each include a query input by the user and item data for an item presented as a result to the user's query. Each training example also includes an initial label, which represents whether the user interacted with the item presented as a search result. The online system updates the initial label for a negative training example by identifying a set of bridge queries and computing a similarity score between the query for the training example and the bridge queries. The online system computes an updated label for the negative example based on the similarity scores and updates the training example with the updated label.

公开(公告)号：US12217203B2

公开(公告)日：2025-02-04

申请号：US18235230

申请日：2023-08-17

Applicant: Maplebear Inc.

Inventor： Xinyu Li ,  Haixun Wang ,  Ruoming Jin

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

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：US20240311397A1

公开(公告)日：2024-09-19

申请号：US18671761

申请日：2024-05-22

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：US20240303710A1

公开(公告)日：2024-09-12

申请号：US18596590

申请日：2024-03-05

Applicant: Maplebear Inc.

Inventor： Li Tan ,  Haixun Wang ,  Shishir Kumar Prasad ,  Tejaswi Tenneti ,  Aomin Wu ,  Jagannath Putrevu

IPC: G06Q30/0601 ,  G06Q30/0201 ,  G06Q30/0282

CPC classification number: G06Q30/0627 ,  G06Q30/0201 ,  G06Q30/0282

Abstract: A system, for example, an online system uses a machine learning based language model, for example, a large language model (LLM) to process crowd-sourced information provided by users. The crowd-sourced information may include comments from users represented as unstructured text. The system further receives queries from users and answers the queries based on the crowd-sourced information collected by the system. The system generates a prompt for input to a machine-learned language model based on the query. The system provides the prompt to the machine-learned language model for execution and receives a response from the machine-learned language model. The response comprises the insight on the topic and evidence for the insight. The evidence identifies one or more comments used to obtain the insight.

公开(公告)号：US20240289867A1

公开(公告)日：2024-08-29

申请号：US18113870

申请日：2023-02-24

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Xuan Zhang ,  Vinesh Reddy Gudla ,  Tejaswi Tenneti ,  Haixun Wang

IPC: G06Q30/0601

CPC classification number: G06Q30/0633 ,  G06Q30/0619 ,  G06Q30/0631

Abstract: An online system generates a template shopping list for a user by accessing a machine learning model trained based on historical order information associated with the user, applying the model to predict likelihoods of conversion for item categories by the user, and populating the template shopping list with one or more item categories based on the predicted likelihoods. The system ranks one or more item types associated with each item category in the template shopping list and determines a set of collection rules associated with one or more item categories/types based on the historical order information. The system generates a suggested shopping list by populating each item category in the template shopping list with one or more item types and a quantity of each item type based on the ranking and rules and sends the suggested shopping list and rules for display to a client device associated with the user.