公开(公告)号：US12131358B1

公开(公告)日：2024-10-29

申请号：US16816226

申请日：2020-03-11

Applicant: Maplebear, Inc.

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

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

CPC classification number: G06Q30/0605 ,  G06N20/00 ,  G06Q10/0875 ,  G06Q20/407 ,  G06Q30/0205 ,  G06Q30/0623 ,  G06Q30/0631 ,  G06Q30/0635 ,  G06Q30/0639 ,  G06N7/01

Abstract: In an online concierge system, a shopper retrieves items specified in an order by a customer from a retail location. The online concierge system optimizes order fulfillment by selecting a retail location for an order that is most time-efficient and that is most likely to have each of the item in the order available. Hence, the online concierge system may select a less convenient retail location that is more likely to have each item being ordered available. To predict whether a retail location incompletely fulfill the order if selected to fulfill the order, the online concierge system trains a machine learning model based on prior orders fulfilled by the retail location, a shopper retrieving items in the order, items in the order, and other features.

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：US20240281869A1

公开(公告)日：2024-08-22

申请号：US18649676

申请日：2024-04-29

Applicant: Maplebear Inc.

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

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

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：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：US20210287271A1

公开(公告)日：2021-09-16

申请号：US16815846

申请日：2020-03-11

Applicant: Maplebear, Inc.(dba Instacart)

Inventor： Shishir Kumar Prasad ,  Sharath Rao

IPC: G06Q30/06 ,  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.