公开(公告)号：US20230351279A1

公开(公告)日：2023-11-02

申请号：US17731810

申请日：2022-04-28

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Soren Zeliger ,  Aman Jain ,  Zhaoyu Kou ,  Ji Chen ,  Trace Levinson ,  Ganesh Krishnan

IPC: G06Q10/06

CPC classification number: G06Q10/063116 ,  G06Q10/04

Abstract: An online concierge system assigns shoppers to fulfill orders from users. To allocate shoppers, the online concierge system predicts future supply and demand for the shoppers' services for different time windows. To forecast a supply of shoppers, the online concierge system trains a machine learning model that estimates future supply based on access to a shopper mobile application through which the shoppers obtain new assignments by shoppers. The online concierge system also forecasts future orders. The online concierge system estimates a supply gap in a future time period by selecting a target time to accept for shoppers to accept orders and determining a corresponding ratio of number of shoppers and number of orders. The online concierge system may adjust a number of shoppers allocated to the future time period to achieve the determined ratio number of shoppers and number of orders.

公开(公告)号：US20230342711A1

公开(公告)日：2023-10-26

申请号：US17726422

申请日：2022-04-21

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Joey Loi ,  Viswa Mani Kiran Peddinti

IPC: G06Q10/08

CPC classification number: G06Q10/087

Abstract: A warehouse from which shoppers fulfill orders for an online concierge system maintains an online concierge system-specific portion for which the online concierge system specifies placement of items in regions. To place items in the online concierge system-specific portion, the online concierge system accounts for co-occurrences of different items in orders and measures of similarity between different items. From the co-occurrences of items, the online concierge system generates an affinity graph. The online concierge system also generates a colocation graph based on distances between different regions in the online concierge system-specific portion. Using an optimization function with the affinity graph and the colocation graph, the online concierge system selects regions within the online concierge system-specific portion for different items to minimize an amount of time for shoppers to obtain items in the online concierge-system specific portion.

公开(公告)号：US20230289868A1

公开(公告)日：2023-09-14

申请号：US17871790

申请日：2022-07-22

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Jonathan Lennart Bender ,  Kevin Lau ,  Silas Burton ,  Prakash Putta ,  Manmeet Singh ,  Tejaswi Tenneti

IPC: G06Q30/06 ,  G06F16/9538 ,  G06F16/906 ,  G06N5/02

CPC classification number: G06Q30/0643 ,  G06F16/9538 ,  G06F16/906 ,  G06N5/022 ,  G06F3/0482

Abstract: An online concierge system receives a search query from a client device. The online concierge system identifies a set of matching items from an item database. The matching items correspond to the received search query. The online concierge system obtains, from a hierarchical item taxonomy, a category label for each matching item. The item taxonomy relates each item in the item database to one of a plurality of category labels. The online concierge system groups the matching items by the category labels for each of the matching items into one or more groups. The online concierge system generates instructions for a user interface. The user interface includes a scrollable list of one or more carousels. Each carousel includes a scrollable list of a group of the one or more groups. The online concierge system sends the instructions of the user interface to the client device for display.

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.