公开(公告)号：US20230267292A1

公开(公告)日：2023-08-24

申请号：US18169010

申请日：2023-02-14

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Ganglu Wu ,  Shiyuan Yang ,  Xiao Zhou ,  Qi Wang ,  Qunwei Liu ,  Youming Luo

IPC: G06K7/14 ,  G06T9/00 ,  G06V10/25 ,  G06Q30/0601

CPC classification number: G06K7/1413 ,  G06T9/00 ,  G06V10/25 ,  G06Q30/0633 ,  G06Q30/0641 ,  G06V2201/07

Abstract: An automated checkout system modifies received images of machine-readable labels to improve the performance of a label detection model that the system uses to decode item identifiers encoded in the machine-readable labels. For example, the automated checkout system may transform subregions of an image of a machine-readable label to adjust for distortions in the image's depiction of the machine-readable label. Similarly, the automated checkout system may identify readable regions within received images of machine-readable labels and apply a label detection model to those readable regions. By modifying received images of machine-readable labels, these techniques improve on existing computer-vision technologies by allowing for the effective decoding of machine-readable labels based on real-world images using relatively clean training data.

公开(公告)号：US20230252549A1

公开(公告)日：2023-08-10

申请号：US18107854

申请日：2023-02-09

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Yuqing Xie ,  Taesik Na ,  Saurav Manchanda

IPC: G06Q30/0601 ,  G06Q30/0201

CPC classification number: G06Q30/0631 ,  G06Q30/0201

Abstract: To train an embedding-based model to determine relevance between items and queries, an online system generates training data from previously received queries and interactions with results for the queries. The training data includes positive training examples including a query and an item with which a user performed a specific interaction after providing the query. To generate negative training examples for the query to include in the training data, the online system determines measures of similarity between items with which the specific interaction was not performed and the query. The online system may weight a loss function for the embedding-based model by the measure of similarity for a negative example, increasing the effect of a negative example including a query and an item with a larger measure of similarity. In other embodiments, the online system selects negative training examples based on the measures of similarities between items and queries in pairs.

公开(公告)号：US20230196389A1

公开(公告)日：2023-06-22

申请号：US18112438

申请日：2023-02-21

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Changyao Chen ,  Peng Qi ,  Weian Sheng

IPC: G06Q30/0201 ,  G06N3/084 ,  G06N3/047

CPC classification number: G06Q30/0201 ,  G06N3/084 ,  G06Q30/0206 ,  G06N3/047

Abstract: An online concierge system trains a user interaction model to predict a probability of a user performing an interaction after one or more content items are displayed to the user. This provides a measure of an effect of displaying content items to the user on the user performing one or more interactions. The user interaction model is trained from displaying content items to certain users of the online concierge system and withholding display of the content items to other users of the online concierge system. To train the user interaction model, the user interaction model is applied to labeled examples identifying a user and value based on interactions the user performed after one or more content items were displayed to the user and interactions the user performed when one or more content items were not used.

公开(公告)号：US20230153847A1

公开(公告)日：2023-05-18

申请号：US18149646

申请日：2023-01-03

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Wa Yuan ,  Ganesh Krishnan ,  Qianyi Hu ,  Aishwarya Balachander ,  George Ruan ,  Soren Zeliger ,  Mike Freimer ,  Aman Jain

IPC: G06Q30/0202 ,  G06N3/084 ,  G06Q30/0201 ,  G06Q30/0601 ,  G06Q10/087 ,  G06Q10/0631

CPC classification number: G06Q30/0202 ,  G06N3/084 ,  G06Q30/0201 ,  G06Q30/0633 ,  G06Q10/087 ,  G06Q30/0607 ,  G06Q10/06312

Abstract: An online concierge system trains a machine learning conversion model that predicts a probability of receiving an order from a user when the user accesses the online concierge system. The conversion model predicts the probability of receiving the order based on a set of input features that include price and availability information. For each access to the online concierge system, the online concierge system applies the conversion model to a current price and availability and to an optimal price availability. The online concierge system generates a metric as the difference between the two predicted probabilities of receiving an order.

公开(公告)号：US20230147670A1

公开(公告)日：2023-05-11

申请号：US17524469

申请日：2021-11-11

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Girija Narlikar

IPC: G06Q30/06

CPC classification number: G06Q30/0631 ,  G06Q30/0641

Abstract: An online concierge system modifies generic item descriptions included in a recipe displayed to a user based on the user's preferences. The online concierge system generates a replacement graph identifying a replacement generic item description for a generic item description, one or more preferences causing replacement of the generic item description with the replacement generic item description, and a replacement quantity of the replacement generic item description. To customize a recipe for the user, the online concierge system selects replacement generic item descriptions for one or more generic item descriptions in the recipe satisfying one or more stored preferences for the user based on the replacement graph.

公开(公告)号：US20230146832A1

公开(公告)日：2023-05-11

申请号：US18149652

申请日：2023-01-03

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Mathieu Ripert ,  Jagannath Putrevu ,  Deepak Tirumalasetty ,  Bala Subramanian ,  Andrew Kane

IPC: G08G1/00 ,  G06Q10/0833 ,  G06Q30/0601 ,  G06Q10/0631 ,  G05D1/02 ,  G06Q20/32 ,  G01C21/34 ,  G06Q10/087 ,  B65G1/137 ,  B65G1/04

CPC classification number: G08G1/20 ,  G06Q10/0833 ,  G06Q30/0635 ,  G06Q10/063116 ,  G05D1/0217 ,  G06Q20/322 ,  G01C21/34 ,  G06Q10/087 ,  B65G1/1373 ,  B65G1/0492 ,  G05D1/0291

Abstract: An online shopping concierge system identifies a set of delivery orders and a set of delivery agents associated with a location. The system allocates the orders among the agents, each agent being allocated at least one order. The system obtains agent progress data describing travel progress of the agents to the location, and order preparation progress data describing progress of preparing the orders for delivery. The system periodically updates the allocation of the orders among the agents based on the agent progress data and the order preparation progress data. This involves re-allocating at least one order to a different delivery agent. When a first agent arrives at the location, the system assigns to the first agent the orders allocated to the first agent. The system then removes the first agent from the set of available delivery agents, and removes the assigned delivery orders from the set of delivery orders.

公开(公告)号：US20230139335A1

公开(公告)日：2023-05-04

申请号：US18090506

申请日：2022-12-29

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Shishir Kumar Prasad ,  Sharath Rao Karikurve

IPC: G06Q30/0601 ,  G06F16/953

Abstract: An online concierge system may determine recommended search terms for a user. The online concierge system may receive a request from a user to view a user interface configured to receive a search query. The online concierge system retrieves long-term activity data including previous search terms entered by the user while searching for items to add to an online shopping cart. For each previous search term, the online concierge system retrieves categorical search terms corresponding to one or more categories to which the previous search term was mapped. The online concierge system determines a set of nearby categorical search terms and sends, for display via a client device, the set of nearby categorical search terms as recommended search terms.

公开(公告)号：US20230132730A1

公开(公告)日：2023-05-04

申请号：US17515399

申请日：2021-10-30

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Shishir Kumar Prasad ,  Natalia Botía ,  Diego Goyret ,  Allan Stewart ,  Douglas Mill ,  Andrew Wong ,  Yao Zhou

IPC: G06Q30/06 ,  G06F16/2457

Abstract: An online concierge system maintains a taxonomy associating one or more specific items offered by a warehouse with a category. When the online concierge system receives a selection of an item from a user for inclusion in an order, the online concierge system determines a category including the selected item. From prior received orders, the online concierge system 102 identifies additional categories including one or more items included in various prior received orders. Based on cooccurrences of the category and the additional categories, the online concierge system generates scores for the additional categories. An additional category is selected based on the scores and specific items from the selected additional category are displayed via an interface for selection by the user.

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：US20230035687A1

公开(公告)日：2023-02-02

申请号：US17938405

申请日：2022-10-06

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Supriyo Chakraborty ,  Keith Grueneberg ,  Bongjun Ko ,  Christian Makaya ,  Jorge J. Ortiz ,  Swati Rallapalli ,  Theodoros Salonidis ,  Rahul Urgaonkar ,  Dinesh Verma ,  Xiping Wang

IPC: G06Q30/02

Abstract: A method for targeted advertisement includes transmitting a pre-filter to the user device, responsive to contextual information from a user device, to determine, using a processor, one or more inferences based on physical browsing information, collected at the user device, in compliance with one or more privacy policies of the user. The method also includes receiving one or more inferences determined by the pre-filter from the user device and transmitting one or more targeted advertisements to the user device based on one or more inferences.