公开(公告)号：US20250022024A1

公开(公告)日：2025-01-16

申请号：US18898272

申请日：2024-09-26

Applicant: Maplebear Inc.

Inventor： Sharath Rao Karikurve ,  Abhay Pawar ,  Shishir Kumar Prasad

IPC: G06Q30/0601 ,  G06N7/01 ,  G06N20/00 ,  G06Q10/0875 ,  G06Q20/40 ,  G06Q30/0204

Abstract: A system or a method for fulfilling orders using a machine-learned model in an online system. When a user places an order, the system accesses a model trained on historical data, including characteristics of candidate locations, previous orders, and recent inventory records. The model predicts the probability that each candidate location will incompletely fulfill the order. The system selects the location with the lowest probability of incomplete fulfillment and sends fulfillment instructions to client devices of available shoppers. After the order is fulfilled, the system receives data from the client devices of shoppers, identifies whether the order was completely fulfilled, and updates the machine-learned model based on the actual outcomes.

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：US20220292567A1

公开(公告)日：2022-09-15

申请号：US17196855

申请日：2021-03-09

Applicant: Maplebear, Inc. (dba Instacart)

Inventor： Shishir Kumar Prasad ,  Sharath Rao Karikurve ,  Abhay Pawar

IPC: G06Q30/06 ,  G06Q10/08 ,  G06F16/28 ,  G06F16/2457 ,  G06N20/00

Abstract: An online concierge system accesses a hierarchical taxonomy of products each labeled with a category of the hierarchical taxonomy. The online concierge system receives, from an inventory database, an unlabeled product, which not included in the hierarchical taxonomy. The online concierge system inputs the unlabeled product to a replacement model. The replacement model is trained to output, for each of one or more labeled products from the hierarchical taxonomy, a likelihood that a user would select the labeled product as a replacement for an input product. The online concierge system selects a labeled product from the one or more labeled products based on the likelihoods. The online concierge system adds the unlabeled product to a category of the hierarchical taxonomy based on the selected labeled product.

公开(公告)号：US12175525B2

公开(公告)日：2024-12-24

申请号：US17493150

申请日：2021-10-04

Applicant: Maplebear Inc.

Inventor： Jeffrey Bernard Arnold ,  Rob Donnelly ,  Sumit Garg ,  Jonathan Gu ,  Bill Lundberg ,  David Pal ,  Sharath Rao Karikurve ,  Peng Qi

IPC: G06Q30/0601 ,  G06F9/451 ,  G06Q30/02

Abstract: An online concierge system includes sponsored content items in an interface including different slots for displaying content items. A sponsored content item may be displayed in a single slot or in multiple adjacent slots. The online concierge system determines a content score for various sponsored content items indicating a likelihood of a user interacting with a sponsored content item and a position bias for slots in the interface indicating a likelihood of the user interacting with a slot independent of content in the slot. Position biases are different dependent on a number of slots in which a content item is displayed. The online concierge system generates a graph identifying potential placements of sponsored content items in slots by selecting content items in an order according to their content scores. Sponsored content items are positioned in slots according to a path through the graph that has the highest overall expected value.

公开(公告)号：US20240354556A1

公开(公告)日：2024-10-24

申请号：US18640231

申请日：2024-04-19

Applicant: Maplebear Inc.

Inventor： Yueyang Rao ,  Brian Lin ,  Angadh Singh ,  Sharath Rao Karikurve ,  Guanghua Shu

IPC: G06N3/0455 ,  G06Q30/0601

CPC classification number: G06N3/0455 ,  G06Q30/0631

Abstract: An online system generates session-based recommendations for a user accessing an application of the online system. The online system receives, from one or more client devices, a sequence of actions performed by a user during a session of an application of an online system. The online system generates a sequence of tokens from the sequence of actions by tokenizing an action to a token representing a respective item identifier. The online system applies a transformer-based machine-learned model to the sequence of tokens to generate predictions for a set of items. The online system selects a subset of items based on the generated predictions for the set of items. The online system generates one or more recommendations to the user from the selected subset of items and displays the recommendations to the user.

公开(公告)号：US20240257221A1

公开(公告)日：2024-08-01

申请号：US18631964

申请日：2024-04-10

Applicant: Maplebear Inc.

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

IPC: G06Q30/0601 ,  G06Q10/087

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：US20240070746A1

公开(公告)日：2024-02-29

申请号：US17899483

申请日：2022-08-30

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Girija Narlikar ,  Sharath Rao Karikurve

IPC: G06Q30/06 ,  G06N20/00 ,  G06Q30/02

CPC classification number: G06Q30/0631 ,  G06N20/00 ,  G06Q30/0201 ,  G06Q30/0633

Abstract: A method implemented at a computer system includes, responsive to identifying an opportunity to present content to a target user, accessing a machine learning model trained on a dataset containing input features of a plurality of users and labels indicating openness metrics of the respective plurality of users. The machine learning model is then applied to a set of features of the target user to output an openness metric that predicts a loss in the target user's response rate when contextual relevance is not considered in selection of recommendation for the target user. A recommendation is then selected from a plurality of candidate recommendations based on the openness metric and sent for display to the target user.

公开(公告)号：US11734749B2

公开(公告)日：2023-08-22

申请号：US17230816

申请日：2021-04-14

Applicant: Maplebear, Inc.

Inventor： Shishir Kumar Prasad ,  Sharath Rao Karikurve ,  Diego Goyret

IPC: G06Q30/0601 ,  G06Q10/087 ,  G06Q30/0204 ,  G06N20/00 ,  G06Q30/0201 ,  G06N5/04

CPC classification number: G06Q30/0639 ,  G06N5/04 ,  G06N20/00 ,  G06Q10/087 ,  G06Q30/0201 ,  G06Q30/0205 ,  G06Q30/0619 ,  G06Q30/0629 ,  G06Q30/0633

Abstract: An online concierge system allows users to order items from a warehouse having multiple physical locations, allowing a user to order items at any given warehouse location. To select a warehouse location for a warehouse selected by a user, the online concierge system identifies a set of items that the user has a threshold likelihood of purchasing from prior orders by the user. For each of a set of warehouse locations, the online concierge system applies a machine-learned item availability model to each item of the identified set. From the availabilities of items of the set at each warehouse location of the set, the online concierge system selects a warehouse location. The online concierge system identifies an inventory of items from the selected warehouse location to the user for inclusion in an order.

公开(公告)号：US20220335489A1

公开(公告)日：2022-10-20

申请号：US17232621

申请日：2021-04-16

Applicant: Maplebear, Inc.(dba Instacart)

Inventor： Sharath Rao Karikurve ,  Angadh Singh

IPC: G06Q30/06 ,  G06N20/00 ,  G06F17/16

Abstract: An online concierge system maintains information about items offered for purchase and users of the online concierge system. Based on prior purchases of items by users, the online concierge system trains a model to determine a likelihood of a user purchasing an item based on an embedding for the object and embedding for the user. The online concierge system identifies a collection of items and generates an embedding for the collection. The collection may be a cluster of items determined from similarities between embeddings of items. Alternatively, the collection may be a group of items having a common category. The online concierge system includes one or more collections of items along with individual items when recommending items for the users, so the trained model is applied to embeddings of the individual items and to embeddings of the one or more collections to generate recommendations for a user.

公开(公告)号：US20250139106A1

公开(公告)日：2025-05-01

申请号：US18933807

申请日：2024-10-31

Applicant: Maplebear Inc.

Inventor： Sharath Rao Karikurve ,  Shrikar Archak ,  Shishir Kumar Prasad

IPC: G06F16/2457 ,  G06F16/248

Abstract: An online system performs an atypical replacement recommendation task in conjunction with a model serving system or the interface system to make recommendations to a user for replacing a target item with an atypical replacement item. The online system receives a search query from a user and identifies a target item based on the search query. The online system identifies a set of candidate items for replacing the target item. The online system may select one or more atypical replacement items in the set of candidate items, and generate an explanation for each atypical replacement item. The explanation provides a reason for using the atypical replacement item to replace the target item. The online system provides the atypical replacement items and the corresponding explanations as a response to the search query.