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：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：US20240289867A1

公开(公告)日：2024-08-29

申请号：US18113870

申请日：2023-02-24

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Xuan Zhang ,  Vinesh Reddy Gudla ,  Tejaswi Tenneti ,  Haixun Wang

IPC: G06Q30/0601

CPC classification number: G06Q30/0633 ,  G06Q30/0619 ,  G06Q30/0631

Abstract: An online system generates a template shopping list for a user by accessing a machine learning model trained based on historical order information associated with the user, applying the model to predict likelihoods of conversion for item categories by the user, and populating the template shopping list with one or more item categories based on the predicted likelihoods. The system ranks one or more item types associated with each item category in the template shopping list and determines a set of collection rules associated with one or more item categories/types based on the historical order information. The system generates a suggested shopping list by populating each item category in the template shopping list with one or more item types and a quantity of each item type based on the ranking and rules and sends the suggested shopping list and rules for display to a client device associated with the user.

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：US11776042B2

公开(公告)日：2023-10-03

申请号：US17929797

申请日：2022-09-06

Applicant: Maplebear, Inc.

Inventor： Tyler Russell Tate ,  Jason Scott ,  Logan William Murdock ,  Tejaswi Tenneti

IPC: G06Q30/0601 ,  G06F16/9538 ,  G06Q30/0283 ,  G06Q10/087

CPC classification number: G06Q30/0631 ,  G06F16/9538 ,  G06Q10/087 ,  G06Q30/0283 ,  G06Q30/0603 ,  G06Q30/0617 ,  G06Q30/0625 ,  G06Q30/0635 ,  G06Q30/0641

Abstract: An online system provides options for selection by a user. The online system receives a query entered on a client device. The online system queries an item database to retrieve a set of items related to the query and assigns each item to a product category in a predefined taxonomy that maps items to product categories. The online system inputs each item into a prediction model trained to predict a probability that an item is available at a warehouse location. The online system determines that a first product category has low availability based on predicted probabilities for items in the first product category. Responsive to determining that a first product category has low availability, the online system generates a generic item for the first product category and sends a list of items including the generic item to the client device for display responsive to the query.

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：US11468493B2

公开(公告)日：2022-10-11

申请号：US16984443

申请日：2020-08-04

Applicant: Maplebear, Inc.

Inventor： Tyler Russell Tate ,  Jason Scott ,  Logan William Murdock ,  Tejaswi Tenneti

IPC: G06Q30/06 ,  G06F16/9538 ,  G06Q30/02 ,  G06Q10/08

Abstract: An online system provides options for selection by a user. The online system receives a query entered on a client device. The online system queries an item database to retrieve a set of items related to the query and assigns each item to a product category in a predefined taxonomy that maps items to product categories. The online system inputs each item into a prediction model trained to predict a probability that an item is available at a warehouse location. The online system determines that a first product category has low availability based on predicted probabilities for items in the first product category. Responsive to determining that a first product category has low availability, the online system generates a generic item for the first product category and sends a list of items including the generic item to the client device for display responsive to the query.

公开(公告)号：US20250165513A1

公开(公告)日：2025-05-22

申请号：US18948027

申请日：2024-11-14

Applicant: Maplebear Inc.

Inventor： Vinesh Reddy Gudla ,  Tejaswi Tenneti ,  Shubhanshu Mishra

IPC: G06F16/33 ,  G06F16/2452

Abstract: An online system uses a machine-learned language model (e.g., an LLM) to improve multilingual search capabilities. The system generates a prompt for the LLM that includes a set of search queries in a first language along with their context, as well as a request for translating these queries into a second language. This prompt is sent to a model serving system, which executes it through the LLM and returns translated queries in the second language. Additionally, the concierge system accesses a first set of features derived from the search results in the first language, and updates these features based on the newly translated search queries to create a second set of features. These translated queries and the second set of features are then used to train a search model optimized for queries in the second language.

公开(公告)号：US20250139681A1

公开(公告)日：2025-05-01

申请号：US18496720

申请日：2023-10-27

Applicant: Maplebear Inc.

Inventor： Vinesh Reddy Gudla ,  David Vengerov ,  Tejaswi Tenneti

IPC: G06Q30/0601

Abstract: A system uses a contextual bandit model for query processing. The system receives, from a client device, a user query for identifying one or more items by the system and described by query feature(s). The system obtains contextual feature(s) describing the query's context. The system applies a query processing model to the user query to determine a relevance score for each query result. The system applies a contextual bandit model to the query features and the contextual features to determine a weight vector for ranking parameters. The ranking parameters include relevance of a query result to the user query and dependability of the query result. The system determines, for each query result, a ranking score based on the weight vector and ranking parameter values of the query result. The system transmits the query results ranked according to the ranking scores for display on the client device.

公开(公告)号：US12259894B2

公开(公告)日：2025-03-25

申请号：US17666531

申请日：2022-02-07

Applicant: Maplebear Inc.

Inventor： Taesik Na ,  Zhihong Xu ,  Guanghua Shu ,  Tejaswi Tenneti ,  Haixun Wang

IPC: G06F16/2457 ,  G06F16/242

Abstract: An online system maintains various items and maintains values for different attributes of the items, as well as an item embedding for each item. When the online system receives a query for retrieving one or more items, the online system generates an embedding for the query. Based on measures of similarity between the embedding for the query and item embeddings, the online system selects a set of items. The online system identifies a specific attribute of items and generates a whitelist of values for the specific attribute based on measures of similarity between item embeddings for items in the selected set and the embedding for the query. The online system removes items having values for the selected attribute outside of the whitelist of values from the selected set of items to identify items more likely to be relevant to the query.