公开(公告)号：US20240005381A1

公开(公告)日：2024-01-04

申请号：US17855788

申请日：2022-06-30

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Sonali Deepak Chhabria ,  Xiangyu Wang ,  Aman Jain ,  Ganesh Krishnan ,  Trace Levinson ,  Jian Wang

IPC: G06Q30/06 ,  G06Q10/06 ,  G06Q10/08 ,  G01C21/34

CPC classification number: G06Q30/0635 ,  G06Q10/06311 ,  G06Q10/087 ,  G01C21/3407

Abstract: An online concierge system includes a marketplace automation engine for setting various control parameters affecting marketplace operation. The marketplace automation engine applies a hyperparameter learning model to the marketplace state data to predict a set of hyperparameters affecting a set of respective parameterized control decision models. The hyperparameter learning model is trained on historical marketplace state data and a configured outcome objective for the online concierge system. The marketplace automation engine independently applies the set of parameterized control decision models to the marketplace state data using the hyperparameters to generate a respective set of control parameters affecting marketplace operation of the online concierge system. The marketplace automation engine applies the respective set of control parameters to operation of the online concierge system.

公开(公告)号：US20240001981A1

公开(公告)日：2024-01-04

申请号：US17873526

申请日：2022-07-26

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Lin Gao ,  Yilin Huang ,  Shiyuan Yang ,  Xiaofei Zhou ,  Kaiyang Chu ,  Sikun Zhu

IPC: B62B5/00 ,  B62B3/14 ,  H04W4/029

CPC classification number: B62B5/0096 ,  B62B3/1424 ,  H04W4/029

Abstract: A shopping cart's tracking system determines a first baseline location of the shopping cart at a first timestamp with a wireless device located on the shopping cart detecting one or more external wireless devices (e.g., RFID tags) in the indoor environment. The shopping cart's tracking system receives wheel motion data from one or more wheel sensors coupled to one or more wheels of the shopping cart, wherein the wheel motion data describes rotation of the one or more wheels. The shopping cart's tracking system calculates a translation traveled by the shopping cart from the first baseline location based on the wheel motion data. The shopping cart's tracking system determines an estimated location of the shopping cart at a second timestamp based on the first baseline location and the translation. With the estimated location, the shopping cart can update a map with the estimated location of the shopping cart.

公开(公告)号：US20230385875A1

公开(公告)日：2023-11-30

申请号：US17824696

申请日：2022-05-25

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Leho Nigul

IPC: G06Q30/02

CPC classification number: G06Q30/0255 ,  G06Q30/0261 ,  G06Q30/0242

Abstract: A shopping cart system detects the initiation of a shopping session within a physical retail store by a customer, in which the shopping cart system includes a shopping cart, a processor, a memory, and a set of sensors. Contextual information associated with the shopping cart received by the sensors during the shopping session is tracked, in which the contextual information describes one or more locations of the shopping cart within the store, a state of the shopping cart, and a set of items within the shopping cart. Responsive to identifying an opportunity to present content to the customer based on the contextual information, the system identifies a set of content items associated with one or more items within the store based on the contextual information. The system generates a user interface including the set of content items and sends the user interface to a display area associated with the customer.

公开(公告)号：US20230377020A1

公开(公告)日：2023-11-23

申请号：US17751521

申请日：2022-05-23

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Nathan Bauer

IPC: G06Q30/06 ,  G06Q20/20

CPC classification number: G06Q30/0631 ,  G06Q30/0641 ,  G06Q20/20

Abstract: An automated checkout system automatically establishes sessions between users and shopping carts by correlating action events with distances of the user's client device to the shopping cart. The automated checkout system determines the client device's distance from the shopping cart at timestamps when an action event occurs with respect to the shopping cart. If the distances and the action events are correlated, the system establishes a session between the user and the shopping cart. Additionally, the automated checkout system attributes target actions to recipe suggestions. The automated checkout system displays a recipe suggestion to a user on a display of a shopping cart, and identifies an item added to the shopping cart. If the added item matches an item in the set of recipes, the automated checkout system applies an attribution model that determines whether to attribute a target action that relates to the item with the recipe suggestion.

公开(公告)号：US20230351465A1

公开(公告)日：2023-11-02

申请号：US17731608

申请日：2022-04-28

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Konrad Gustav Miziolek ,  Bryan Daniel Bor

IPC: G06Q30/06 ,  G06N20/20 ,  G06Q30/02

CPC classification number: G06Q30/0617 ,  G06Q30/0625 ,  G06N20/20 ,  G06Q30/0206

Abstract: A user treatment engine uses user data describing characteristics of a user to evaluate a set of treatments that the user treatment engine may apply to the user. The user treatment engine generates treatment cost predictions for the treatments and generates treatment scores for the set of treatments based on the treatment cost predictions for the treatments and the user data for the user. The user treatment engine selects and applies a treatment from the set of treatments based on the generated treatment scores. The user treatment engine determines a reward to the online concierge system for the application of the treatment to the user and updates treatment selection parameters for the applied treatment based on the determined reward.

公开(公告)号：US20230316350A1

公开(公告)日：2023-10-05

申请号：US17853619

申请日：2022-06-29

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Benjamin Knight ,  Benjamin Peyrot ,  Djordje Gluhovic ,  Rohit Turumella ,  Alice Han

IPC: G06Q30/04 ,  G06Q20/20 ,  G06Q40/00

CPC classification number: G06Q30/04 ,  G06Q20/207 ,  G06Q40/10

Abstract: An online concierge system requests an image of a receipt of an order from a picker after the picker fulfills the order at a store. The online concierge system performs image processing on the image of the receipt and uses machine learning and optical character recognition to determine a tax amount paid for the order and a confidence score associated with the tax amount. The online concierge system may use the machine learning model for segmenting extracted text in the image of the receipt into tokens. The online concierge system may then determine at least one token associated with a tax item and the tax amount associated with the tax item. The online concierge system communicates the tax amount to the store for reimbursement based on the tax amount and the confidence score.

公开(公告)号：US20230306023A1

公开(公告)日：2023-09-28

申请号：US17668358

申请日：2022-02-09

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Taesik Na ,  Yuqing Xie ,  Tejaswi Tenneti ,  Haixun Wang

IPC: G06F16/2453 ,  G06F16/2457 ,  G06F16/242 ,  G06F16/28 ,  G06N20/00 ,  G06K9/62

CPC classification number: G06F16/24534 ,  G06F16/2448 ,  G06F16/24578 ,  G06F16/283 ,  G06K9/6257 ,  G06N20/00

Abstract: An online concierge system maintains various items and an item embedding for each item. When the online concierge system receives a query for retrieving one or more items, the online concierge system generates an embedding for the query. The online concierge system trains a machine-learned model to determine a measure of relevance of an embedding for a query to item embeddings by generating training data of examples including queries and items with which users performed a specific interaction. The online concierge system generates a subset of the training data including examples satisfying one or more criteria and further trains the machine-learned model by application to the examples of the subset of the training data and stores parameters resulting from the further training as parameters of the machine-learned model.

公开(公告)号：US20230273940A1

公开(公告)日：2023-08-31

申请号：US17682187

申请日：2022-02-28

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Guanghua Shu ,  Taesik Na ,  Zhihong Xu ,  Wideet Shende ,  Manmeet Singh ,  Tejaswi Tenneti ,  Reza Sadri

IPC: G06F16/28 ,  G06F16/22 ,  G06F16/2455 ,  G06F11/34

CPC classification number: G06F16/283 ,  G06F16/2228 ,  G06F16/24556 ,  G06F16/285 ,  G06F11/3409

Abstract: An online system maintains item embeddings for items. As a number of items maintained by the online system increases, maintaining a single index of the item embeddings is increasingly difficult. To increase scalability, the online system partitions item embeddings into multiple indices, with each index corresponding to a value of a specific attribute maintained by the online system for items. For example, an online system generates indices that each correspond to a different warehouse offering items. To expedite retrieval of item embeddings, the online system allocates each index to one of a number of shards. When the online system receives a query, the online system determines an embedding for the query and retrieves an index from a shard based on metadata received with the query. Based on distances between the query for the embedding and the item embeddings in the retrieved index, the online system selects one or more items.

公开(公告)号：US20230186363A1

公开(公告)日：2023-06-15

申请号：US17550950

申请日：2021-12-14

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Ramasubramanian Balasubramanian ,  Saurav Manchanda

IPC: G06Q30/06 ,  G06N20/00 ,  G06F16/2455

CPC classification number: G06Q30/0627 ,  G06Q30/0631 ,  G06N20/00 ,  G06F16/2455

Abstract: An online concierge system selects content for presentation to a user by using a product scoring engine. The product scoring engine generates a user embedding for user data and a query embedding for query data. The product scoring engine generates an anchor embedding based on the user embedding and the query embedding, where the anchor embedding is an embedding in a product embedding space. The product scoring engine compares the anchor embedding to a set of product embeddings to score a set of products for presentation to a user.

公开(公告)号：US20230186361A1

公开(公告)日：2023-06-15

申请号：US17550960

申请日：2021-12-14

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Saurav Manchanda ,  Ramasubramanian Balasubramanian

IPC: G06Q30/06 ,  G06Q30/02

CPC classification number: G06Q30/0619 ,  G06Q30/0282 ,  G06Q30/0641

Abstract: An online concierge system uses a domain-adaptive suggestion module to score products that may be presented to a user as suggestions in response to a user’s search query. The domain-adaptive suggestion module receives data that is relevant to scoring products as suggestions in response to a search query. The domain-adaptive suggestion module uses one or more domain-neutral representation models to generate a domain-neutral representation of the received data. The domain-neutral representation is a featurized representation of the received data that can be used by machine-learning models in the search domain or the suggestion domain. The domain-adaptive suggestion module then scores products by applying one or more machine-learning models to domain-neutral representations generated based on those products. By using domain-neutral representations, the domain-adaptive suggestion module can be trained based on training examples from a similar prediction task in a different domain.