公开(公告)号：US20240427808A1

公开(公告)日：2024-12-26

申请号：US18214275

申请日：2023-06-26

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Vinesh Reddy Gudla ,  Sudha Rani Kolavali ,  Taesik Na ,  Xiao Xiao ,  Nkemakonam Paulet Okoye

IPC: G06F16/332 ,  G06F16/33

Abstract: Embodiments relate to using a large language model (LLM) to generate a list of items at an online system with a user defined constraint. The online system receives a query that includes at least one constraint. The online system generates a prompt for input into the LLM, based at least in part on the query. The online system requests the LLM to generate, based on the prompt, a set of constraints for a set of item types. The online system generates a list of candidate items by searching through a set of items stored in one or more non-transitory computer-readable media using the set of constraints for the set of item types. The online system causes a device of the user to display a user interface with the list of items for inclusion into a cart, the list of items obtained from the list of candidate items.

公开(公告)号：US20240311397A1

公开(公告)日：2024-09-19

申请号：US18671761

申请日：2024-05-22

Applicant: Maplebear Inc.

Inventor： Taesik Na ,  Tejaswi Tenneti ,  Haixun Wang ,  Xiao Xiao

IPC: G06F16/28 ,  G06F16/2457 ,  G06F16/248 ,  G06F18/2413

CPC classification number: G06F16/285 ,  G06F16/24573 ,  G06F16/24575 ,  G06F16/248 ,  G06F18/24147

Abstract: An online system leverages stored interactions with items made by users after the online system received queries to determine display of items satisfying the query. For example, the online system trains a model to predict a likelihood of a user performing an interaction with an item displayed after a query was received. As different items receive different amounts of interaction from users, limited historical interaction with certain items may limit accuracy of the model. The online system generates embeddings for previously received queries and uses measures of similarity between embeddings for queries to generate clusters of queries. Previous interactions with queries in a cluster are combined, with the combined data being used for determining display of items in response to a query.

公开(公告)号：US20240177212A1

公开(公告)日：2024-05-30

申请号：US18072353

申请日：2022-11-30

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Aditya Subramanian ,  Prakash Putta ,  Tejaswi Tenneti ,  Jonathan Lennart Bender ,  Xiao Xiao ,  Taesik Na

IPC: G06Q30/0601

CPC classification number: G06Q30/0631

Abstract: To determine search results for an online shopping concierge platform, the platform may receive, from a computing device associated with a customer of an online shopping concierge platform, data describing one or more search parameters input by the customer; identify, based at least in part on the data describing the search parameter(s), products offered by the online shopping concierge platform that are at least in part responsive to the search parameter(s); and determine, for each product and based at least in part on one or more machine learning (ML) models, a relevance of the product to one or more taxonomy levels of a product catalog associated with the online shopping concierge platform, a likelihood that the customer would be offended by inclusion of the product amongst displayed responsive search results, and/or the like.

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.