公开(公告)号：US20250086395A1

公开(公告)日：2025-03-13

申请号：US18244098

申请日：2023-09-08

Applicant: Maplebear Inc.

Inventor： Prithvishankar Srinivasan ,  Saurav Manchanda ,  Shih-Ting Lin ,  Shishir Kumar Prasad ,  Riddhima Sejpal ,  Luis Manrique ,  Min Xie

IPC: G06F40/30

Abstract: Embodiments relate to utilizing a language model to automatically generate a novel recipe with refined content, which can be offered to a user of an online system. The online system generates a first prompt for input into a large language model (LLM), the first prompt including a plurality of task requests for generating initial content of a recipe. The online system requests the LLM to generate, based on the first prompt input into the LLM, the initial content of the recipe. The online system generates a second prompt for input into the LLM, the second prompt including the initial content of the recipe and contextual information about the recipe. The online system requests the LLM to generate, based on the second prompt input into the LLM, refined content of the recipe. The online system stores the recipe with the refined content in a database of the online system.

公开(公告)号：US20240046313A1

公开(公告)日：2024-02-08

申请号：US17877748

申请日：2022-07-29

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Luis Manrique ,  Aamir Poonawalla ,  Amin Akbari ,  Shesh Nath Mishra ,  Eitan Pinhas Teruzzi Katznelson

IPC: G06Q30/02 ,  G06Q10/08 ,  G06Q30/08

CPC classification number: G06Q30/0272 ,  G06Q10/087 ,  G06Q30/08

Abstract: An online concierge system facilitates procurement and delivery of items for customers using a network of shoppers. The online concierge system includes a promotion management engine that paces delivery of promotions for content campaigns based in part on predicted item availability and a paced spending model that operates to pace spending of a content campaign over a budget period. The system paces the delivery by determining whether to enter a bid for the impression opportunity by comparing an observed cumulative spend for the content campaign during a portion of the budget period prior to the impression time and a desired cumulative spend for the content campaign during the portion of the budget period prior to the impression time based on the distribution of impression opportunities and a budget for the content campaign during the budget period.

公开(公告)号：US20250028768A1

公开(公告)日：2025-01-23

申请号：US18776104

申请日：2024-07-17

Applicant: Maplebear Inc.

Inventor： Riddhima Sejpal ,  Prithvishankar Srinivasan ,  Luis Manrique

IPC: G06F16/9535 ,  G06F9/451 ,  G06F16/9538 ,  G06Q30/0282 ,  G06Q30/0601

Abstract: An online system performs an inference task in conjunction with the model serving system or the interface system to generate customized recipes for users. The online system identifies a plurality of popular recipes based on historical user search data. The online system uses the collection of popular recipes to generate customized recipes for users based on user data and retailer data. The online system presents a customized recipe to the user, which may include items required to fulfill the recipe, a list of retailers at which the items are available for purchase, and instructions to combine the items. The online system collects user ratings and feedback on customized recipes to calculate a quality score. The online system may use the quality score to rank the customized recipes.

公开(公告)号：US20250029173A1

公开(公告)日：2025-01-23

申请号：US18771748

申请日：2024-07-12

Applicant: Maplebear Inc.

Inventor： Riddhima Sejpal ,  Luis Manrique ,  Shiyun Lu ,  Vikrant Verma ,  Nicole Yin Chuen Lee Altman

IPC: G06Q30/0601

Abstract: An online system leverages a machine-learning model to craft personalized meal plans for users. The system generates and presents an interface displaying categories of user preferences. The system receives, from the user via the interface, user preferences for the meal plan. The system generates a prompt including a request to generate the meal plan for the user and the user preferences. The system provides the prompt to the machine-learning model and receives, as output, a meal plan that comprises a list of meals and a list of ingredients for each meal. The system presents the meal plan to the user. The system receives user input to add ingredients to an order and generates an order including the lists of ingredients corresponding to the selected meals.

公开(公告)号：US20240403938A1

公开(公告)日：2024-12-05

申请号：US18326900

申请日：2023-05-31

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Tilman Drerup ,  Shishir Kumar Prasad ,  Zoheb Hajiyani ,  Luis Manrique

IPC: G06Q30/0601 ,  G06N20/00

Abstract: An online system predicts replacement items for presentation to a user using a machine-learning model. The online system receives interaction data describing a user's interaction with the online system. In particular, the interaction data describes an initial item that the user added to their item list. The online system identifies a set of candidate items that could be presented to the user as potential replacements for the initially-added item. The online system applies a replacement prediction model to each of these candidate items to generate a replacement score for the candidate items. The online system selects a proposed replacement item and transmits that item to the user's client device for display to the user. If the user selects the proposed replacement item, the online concierge system replaces the initial item with the proposed replacement item in the user's item list.

公开(公告)号：US20240354828A1

公开(公告)日：2024-10-24

申请号：US18137404

申请日：2023-04-20

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Luis Manrique ,  Sanchit Gupta ,  Aref Kashani Nejad ,  Diego Goyret ,  Kurtis Mirick ,  Joshua Roberts

IPC: G06Q30/0601

CPC classification number: G06Q30/0631 ,  G06Q30/0641

Abstract: An online system receives a request from a user to access an ordering interface for a retailer and identifies a retailer location based on the user's location. The system uses a machine learning model to predict availabilities of items at the retailer location and identifies anchor items the user previously ordered from the retailer that are likely available. The system computes a first score for each anchor item based on an expected value associated with it and/or a likelihood the user will re-order it, determines categories associated with the anchor items, and ranks the categories based on the first score. For each category, the system identifies associated candidate items likely to be available and ranks them based on a second score for each candidate item computed based on a probability of user satisfaction with it as an anchor item replacement. The ordering interface is then generated based on the rankings.