公开(公告)号：US20240289861A1

公开(公告)日：2024-08-29

申请号：US18587655

申请日：2024-02-26

Applicant: Maplebear Inc.

Inventor： Haixun Wang ,  Tejaswi Tenneti ,  Taesik Na ,  Yuanzheng Zhu ,  Vinesh Reddy Gudla ,  Lee Cohn

IPC: G06Q30/0601

CPC classification number: G06Q30/0631 ,  G06Q30/0627 ,  G06Q30/0635 ,  G06Q30/0643

Abstract: Responsive to an input query from a user, an online system presents a list of recommended items that are related to the input query. The input query may be formulated as a natural language query. The online system performs an inference task in conjunction with the model serving system to generate one or more additional queries that are related to the input query and/or are otherwise related to the recommended items presented in response to the input query. The additional queries may be presented to the user in conjunction with the list of recommended items.

公开(公告)号：US20240249335A1

公开(公告)日：2024-07-25

申请号：US18159357

申请日：2023-01-25

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Taesik Na ,  Vinesh Reddy Gudla ,  Xiao Xiao

IPC: G06Q30/0601 ,  G06F16/9535 ,  G06Q30/0201

CPC classification number: G06Q30/0631 ,  G06F16/9535 ,  G06Q30/0201

Abstract: An online system displays search results in response to a query by receiving a query from a customer. An online system accesses a set of candidate items and computes a relevance score and personalization score for each item. The online system computes the relevance score based on query data and item data and may normalize the relevance score. The online system computes the personalization score based on item data, such as an item embedding, and user data, such as a user embedding. The online system computes a query specificity score and adjusts the personalization score with the query specificity score such that generic queries have high personalization scores and specific queries have low personalization scores. The online system combines the relevance and personalization scores for each candidate item into a ranking score and displays the candidate items to the customer based on their ranking scores.

公开(公告)号：US20250005654A1

公开(公告)日：2025-01-02

申请号：US18217329

申请日：2023-06-30

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Raochuan Fan ,  Prakash Putta ,  Vinesh Reddy Gudla ,  Nkemakonam Paulet Okoye ,  Taesik Na ,  Tejaswi Tenneti

IPC: G06Q30/0601

Abstract: An online concierge system allows a customer to search items offered by a retailer by providing a set of items to the customer based on a search query. To account for varying availability of items at the retailer, the online concierge system modifies rankings in the set of items having less than a threshold predicted availability at the retailer. This reduces a likelihood selection of an item likely to be unavailable at the retailer. To maintain customer confidence in the items selected based on the search results by maintaining visibility of items relevant to the search query, the online concierge system determines how much an item is modified within the set based on search query attributes, item attributes, or customer characteristics. This allows different items to be adjusted different amounts in a set based on the item, as well as the search query for which the item was selected.

公开(公告)号：US20240104622A1

公开(公告)日：2024-03-28

申请号：US17955250

申请日：2022-09-28

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Vinesh Reddy Gudla ,  Tyler Russell Tate ,  Tejaswi Tenneti ,  Akshay Nair

IPC: G06Q30/06 ,  G06Q30/02

CPC classification number: G06Q30/0629 ,  G06Q30/0201 ,  G06Q30/0204

Abstract: An online system receives a search query from a client device associated with a user and queries a database including item data for a set of items matching the query, in which the set of items is at a retailer location associated with a retailer type and each item is associated with an item category. For each item of the set, a machine learning model is applied to predict a probability of conversion for the user and item and a score is computed based on an expected value, in which the expected value is based on a value associated with the item and the probability. The score for each item is boosted based on the item category, retailer type, or a user segment that is based on the user's historical order data. The items are ranked based on the boosted scores and the ranking is sent to the client device.

公开(公告)号：US12266006B2

公开(公告)日：2025-04-01

申请号：US18159357

申请日：2023-01-25

Applicant: Maplebear Inc.

Inventor： Taesik Na ,  Vinesh Reddy Gudla ,  Xiao Xiao

IPC: G06Q30/06 ,  G06F16/9535 ,  G06Q30/0201 ,  G06Q30/0601

Abstract: An online system displays search results in response to a query by receiving a query from a customer. An online system accesses a set of candidate items and computes a relevance score and personalization score for each item. The online system computes the relevance score based on query data and item data and may normalize the relevance score. The online system computes the personalization score based on item data, such as an item embedding, and user data, such as a user embedding. The online system computes a query specificity score and adjusts the personalization score with the query specificity score such that generic queries have high personalization scores and specific queries have low personalization scores. The online system combines the relevance and personalization scores for each candidate item into a ranking score and displays the candidate items to the customer based on their ranking scores.

公开(公告)号：US20240354825A1

公开(公告)日：2024-10-24

申请号：US18138657

申请日：2023-04-24

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Vinesh Reddy Gudla ,  Prakash Putta ,  Tejaswi Tenneti ,  Prathyusha Bhaskar Karnam

IPC: G06Q30/0601 ,  G06Q10/083 ,  G06Q10/087

CPC classification number: G06Q30/0625 ,  G06Q10/083 ,  G06Q10/087 ,  G06Q30/0635

Abstract: A search module for an online concierge system executes searches in response to a search query with respect to item databases of retailers. The search module dynamically configures a recall set size that controls a number of search results returned for a search query based in part on a query entropy representing an estimated breadth of the search term. The query entropy may be determined relative to a diversity of items in a retailer's database. The recall set size may be configured relative to the query entropy in a manner that manages a tradeoff between latency of search execution and search result quality.

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：US20250139176A1

公开(公告)日：2025-05-01

申请号：US18496724

申请日：2023-10-27

Applicant: Maplebear Inc.

Inventor： Vinesh Reddy Gudla ,  David Vengerov ,  Tejaswi Tenneti

IPC: G06F16/9532 ,  G06Q30/0201 ,  G06Q30/0282 ,  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. The user query is described by one or more query features. The system obtains one or more contextual features describing a context of the user query. The system applies a contextual bandit model to the query features and the contextual features to select a query processing model from a plurality of query processing models. The system applies the selected query processing model to the user query to obtain query results. The system transmits the query results for display on the client device.

公开(公告)号：US20250077529A1

公开(公告)日：2025-03-06

申请号：US18241093

申请日：2023-08-31

Applicant: Maplebear Inc.

Inventor： Levi Boxell ,  Vinesh Reddy Gudla ,  Michael Kurish ,  Raochuan Fan ,  Tilman Drerup ,  Tejaswi Tenneti

IPC: G06F16/2457 ,  G06F16/248 ,  G06N20/00

Abstract: An online system displays items to a user in search results based on appeasement scores for the items, adjusted according to how specific the search query is. The online system receives a search query from a user of an online system. The online system computes a query specificity score, a measure of the specificity of the search query. The online system accesses candidate items from a database that potentially match the search query. For each candidate item, the online system may compute or predict an appeasement score. The online system adjusts the appeasement score based on the query specificity score such that a more specific query weights the appeasement score lower than a less specific query. The online system may then compute a ranking score based on the adjusted appeasement score and display the candidate items to the user based on their ranking scores.